Shohin 72-Hour Product Reasoning Execution Queue

Status: active. Start: 2026-08-02 21:26 EDT. Owner: Codex.

Live Scoreboard (2026-08-03 05:20 EDT)

Decisive U1000 Result

The first short Qwen result was real but transient. At 1,000 updates on the balanced V8 stream, T2 retains a four-point gain over LoRA but ties the dense control within one solved example and fails the frozen regression rule. On verified-priority V10, T2 specializes sharply into competition math while destroying logic and science. It therefore has not earned a Qwen scale run.

SmolLM3 establishes the higher practical capability floor. Same-data LoRA reaches 93/100 GSM8K, 55/100 semantic MATH-500, and 203/538 solved overall. Dense has the best five-domain macro because it raises executable code to 15/40. T2 reaches 58/100 MATH and 2/30 AIME, including one fully coherent ETTR-only 204-minute rate-problem solution, but its general macro and solved count both fall below LoRA. This is evidence of useful specialist computation, not a general adapter win.

The immediate product decision is scale-dense-or-lora. V11 will compare B1 and C2 only. A future ETTR successor must be a prompt-conditioned expert that defaults exactly to the general model outside its measured hard-math region; it may not retrain a globally active workspace and repeat the observed regression. The static per-domain expert ceiling from existing reports is 45.52% macro and 213/538 solved, which makes routing a concrete opportunity, not a claim that the router has already been learned.

Learned Prompt-Gate Result

The expert complementarity did not translate into a successful learned product router. The preregistered prompt-only classifier uses 50,000 hashed word and bigram features and 17,976 V11 prompts labelled only by ordinary training source: math/science/arithmetic procedure selects C2, code and non-arithmetic procedure selects B1, and teacher rows are excluded. It sees no benchmark outcomes. Although source-held validation is 92.807%, the learned score distribution sends almost every public prompt to B1. It reaches 40.280% macro and 1,997/3,930 solved on the expanded board, and 46.668% macro with 1,790/3,392 solved on the previously unopened four-domain remainder. That is only eight more remainder answers than B1 and remains far below the static ceiling.

One bounded calibration repair enumerated a single global score threshold on the already-used development reports, maximizing five-domain development macro subject to no domain falling more than two points below B1. It selected threshold -133.58884639320723 and reached 44.240% development macro, but the apparent gain did not generalize. The calibrated router reaches 41.907% and 2,003/3,930 solved on the expanded board, then 47.013% and 1,771/3,392 solved on the unopened remainder. It routes nearly all math, MBPP, and BBH prompts to C2 and loses both frozen arms in broad solved count. Manual composition is 5/12 before calibration and 8/12 after calibration because every manual prompt is routed to C2.

This router family is closed. Source identity is not a sufficient proxy for per-prompt expert advantage, and a global threshold calibrated on a small development board overfits. No further threshold or feature variants are authorized. A future gate must be trained on a large disjoint corpus with paired frozen-expert outcomes, or learned jointly with the experts, and must beat the best single arm on an untouched board. Until then the deployable result is C2 as the strongest single arm, not the oracle ceiling.

Paired Outcome-Supervision Closure

The one allowed stronger routing test also fails. Two new boards were built from answer-verified OpenMath and OpenScience rows after exact exclusion of every V11 training question. The first 100 rows of each were evaluated by the frozen selected 3k B1 and C2 adapters under the same model revision, decoding, and evaluator. Math is B1/C2 6/100 / 8/100, with only two B1-only and four C2-only wins. Fresh science is 26/100 / 7/100, with 21 B1-only and two C2-only wins. Across 200 prompts, a perfect per-prompt selector raises B1 only from 32/200 to 38/200; C2 contributes six exclusive answers and loses 23.

This misses the outcome gate fixed before reading results: oracle lift at least five percentage points and at least 5% exclusive wins for each arm. The remaining 1,900 rows per board will not be spent on inference, and no outcome-label classifier will be trained. The result changes the practical interpretation of C2: it is a narrow in-distribution math specialist whose fresh science regression is severe, not a broadly complementary expert.

The next product gate is data plus adapter capacity, not routing. After the 9,000-program TACO audit, V12 attempts a unique 16M-target stream at 46m/18c/33s/1p/2t. Matched one-pass 3,000-update jobs compare the proven 1.679M-parameter LoRA with an approximately eight-times-wider LoRA. Both are held until the V12 capacity/hash/truncation report passes. Identical public and fresh boards decide whether verified-data breadth plus adapter capacity raises real solved counts without C2's code/science/logic tradeoff.

Post-Gate 72-Hour Queue (2026-08-03)

The first objective has been answered: T2 produces a real short-run Qwen lift, but it does not survive the longer matched gate and also loses the SmolLM3 general comparison. The queue now advances the strongest practical adapters and treats ETTR as a measured hard-math specialist. No new layer-tap, byte-rail, compiler/reactor/reader, or globally active ETTR duration/width variant is part of this queue.

Hours 0--8: close the two active causal questions

1. Finish V8 exposure-control jobs 729992/729993 and their exact six-board chains through strict aggregate 730013. This decides whether 200 to 1,000 updates improves T2 relative to both B1 and C2, rather than merely fitting the balanced stream harder.
2. Finish verified-priority V10 jobs 730037/730038, exact six-board chains through 730057, and strict AIME rescoring through 730063. This decides whether independently checked code/math/science examples improve hard answers and rationale quality.
3. Inspect every new T2-only AIME/code win and a stratified sample of T2-only MATH/BBH/GPQA wins. A correct option with a false explanation is recorded as answer accuracy, not sophisticated reasoning.

Go rule: T2 must remain at least three macro points above B1, beat C2 by at least two macro points or ten additional answers, improve at least three domains, and avoid a domain regression greater than two points. If only C2 improves, recurrence has not earned the next run. If V8 improves but V10 does not, exposure is the lever; if V10 improves at matched exposure, verified data is the lever.

Hours 8--24: build the next admitted training stream

1. Complete the 9,000-candidate TACO all-test audit and admit only programs that pass every supplied test.
2. Exact retention selects 4,096 tokens: it preserves 53.25% of OpenMath and 49.10% of OpenScience rows versus 16.06% and 17.38% at 2,048. Context is increased for retained reasoning supervision, not unused VRAM.
3. The Smol-tokenized capacity audit rejects the draft mix: procedural has only 352,609 fully retained targets and teacher seed data only 807,714. The admitted no-replay stream has 8,005,985 targets at 42% math, 25% executable code, 23% answer-checked science, 4% procedural, and 6% teacher. Independently verified rows are consumed before teacher-only traces.
4. Freeze data hash, tokenizer revision, source counts, verifier counts, truncation counts, license inventory, and benchmark-overlap report before any V11 fit.

Hours 24--48: scale one Qwen lever or measure the capacity floor

V10 T2 failed the go rule and the SmolLM3 comparison confirms that current T2 should not scale. Matched B1 and C2 5,000-update jobs are now live on V11i. B1 uses BS4/ACC4 at 4,096. C2's short BS4 canary did not cover the longest V11 batch and OOMed before update 1; BS2/ACC8 preserves the exact 16-example update and is the real-stream safe geometry. Seven identical public boards per arm are already dependency-bound to the final checkpoints.

If V10 T2 fails the dense-control comparison, do not create another Qwen workspace variant. Run the now-generic trainer on pinned SmolLM3-3B:

1. frozen baseline and manual transcript sample;
2. two-update generation smoke and 100-update bounded fit for B1/T2/C2;
3. one matched 1,000-update campaign only if T2 learns the fit board at least as well as C2 and generates valid autonomous answers.

The trainer records whether the backbone is loaded through the multimodal Qwen text path or a standard causal-LM path. Data, update order, charged targets, decoding, and evaluators remain identical across arms.

Hours 48--72: public milestone and verified-reward decision

Open the full milestone only for the best development arm that passed the go rule. Report full GSM8K, MATH-500, AIME 2024/2025, HumanEval, MBPP, a pinned LiveCodeBench slice, GPQA-Diamond, and the frozen logic board with identical decoding. Publish solved counts, pass rates, throughput, VRAM, wall time, and at least 50 manually classified transcripts.

Start RLVR only if the selected SFT model produces at least a 5% deterministic positive rate on an independently verified math/code pool. The first RLVR arm must have an identical no-RLVR continuation control. Best-of-N or verifier selection is reported separately and cannot replace greedy pass@1.

The 72-hour decision is one of: scale-ettr, scale-dense-or-lora, move-to-smollm3-capacity-floor, or repair-data/evaluator. It is never repeat-a-similar-synthetic-architecture-arm.

Transcript inspection shows the deterministic thinking score is strongly affected by decode behavior: many GSM8K completions calculate the right value inside a long plan, then exhaust the 768-token limit before producing the requested final answer. Frozen no-thinking controls and official sampling are therefore running. This is a decoding diagnosis, not permission to count an unemitted answer as correct.

The original T1 interface is not advancing. It learned the bounded fit set, but less efficiently than both controls and incurred severe score-time latency. The one permitted redesign keeps the tied recurrent core while reading frozen token embeddings and adding a near-zero gated residual to the existing prompt positions. This removes the second full Qwen prompt pass and the 16 random soft-prefix positions. C2 completed its exact gate and now leads the bounded fit comparison at a 60.4% NLL reduction, ahead of LoRA's 54.9%. The original T2 job 729837 encountered an evc33 NVIDIA-driver lock before writing a checkpoint and was canceled as hardware-invalid evidence. Its empty output was preserved with an aborted-job suffix. Unchanged replacement 729849 excludes evc33, with scorer 729850; no duration or width variant follows a negative result.

T2 therefore clears the mechanics/fit release by narrowly beating C2 and materially beating B1. This is authorization for a held-out comparison, not a reasoning claim. Matched 200-update jobs are B1 729854, T2 729855, and C2 729856; jobs 729858--729875 bind the same six development boards to their resulting checkpoints.

Evaluator schema v2 now records per-example token usage and explicit max-token exhaustion. The first HumanEval run exposed both real algorithm errors and mid-function truncation, so code milestone scores are rerun at 1,024 generated tokens before promotion (729851/729852). Official GPQA-Diamond has been normalized from exact source commit 56686c06f5e19865c153de0fdb11be3890014df7: 198 rows, deterministic balanced answer permutation, and two disclosed duplicate-distractor rows.

The first full GPQA thinking run is a decoding failure: every one of 198 responses exhausted 768 tokens, and its 20/198 exact score is largely driven by accidental final option letters in unfinished text. No-thinking control 729877 establishes the usable frozen reference. Evaluator-v2 HumanEval at 1,024 tokens rises from 3/20 to 6/20; MBPP remains 5/20, proving that truncation explained some HumanEval misses but not the broader code deficit.

The optional fast-kernel runtime remains rejected for use. Its H100 parity canary 729847 landed on the same unhealthy evc33 node, ran more than five minutes against the base runtime's 20.7 seconds, produced no report, and was found beside a stuck nvidia-smi process. It was canceled as hardware-invalid rather than interpreted as a kernel benchmark. All product jobs continue on the known-good base environment unless a future isolated canary proves both output parity and lower wall time.

Objective

Produce the first credible, directly measured ETTR-versus-baseline reasoning delta on a capable pretrained backbone. The primary development backbone is Qwen/Qwen3.5-0.8B@2fc06364715b967f1860aea9cf38778875588b17; HuggingFaceTB/SmolLM3-3B@a07cc9a04f16550a088caea529712d1d335b0ac1 is the capacity fallback and confirmation arm.

This campaign optimizes answer quality. Synthetic packet accuracy, native causal claims, immutable runtime ceremony, and architectural novelty are not promotion criteria. They remain useful only when they diagnose a concrete answer-quality failure.

Product Scoreboard

Every compared arm uses the same backbone revision, examples, example order, token budget, optimizer-update budget, decoding configuration, and evaluator.

Primary metrics are pass@1 exact answer or test pass rate, per board and as an unweighted five-domain macro average. Generation is greedy for the first gate. Sampling and verifier-guided search are reported separately and cannot replace pass@1. Every report records solved counts, prompt count, maximum generation length, tokens/second, peak VRAM, wall time, and exact model/data revisions.

First promotion gate

The integrated ETTR treatment advances when all conditions hold on the frozen development board:

1. At least +3.0 absolute macro percentage points over the same-data LoRA baseline, or at least +10% relative additional solved examples when the baseline macro score is below 30%.
2. More solved examples in at least three of five domains.
3. No domain regresses by more than two absolute points.
4. The gain survives transcript inspection: final answers must follow from the generated computation rather than extraction quirks or malformed text.
5. A capacity-matched non-ETTR adapter must not clearly exceed the treatment.

The first result is evidence of a useful mechanism, not proof that ETTR alone caused general reasoning. The full sealed board is opened only after the dev gate passes.

pipeline/aggregate_product_reasoning_campaign.py enforces report completeness, decode/data comparability, the five-domain numeric gates, and the dense-control comparison. It deliberately leaves transcript coherence as a required manual gate rather than inferring it from aggregate accuracy. pipeline/rescore_product_reasoning_report.py replays answer extraction over saved completions, so evaluator fixes never require or alter model generation. The first use corrected one GSM8K false negative (2.00 versus 2) and one truncation-driven false positive (1 dollar 40 cents parsed as 1); B1's net score remains 32/100. For MATH-500, the v3 lane uses isolated Hugging Face Math-Verify 0.9.0. B1 rises from raw exact-string 15/100 to 18/100 because three predictions are mathematically equivalent to their references. The backend and version are embedded in each rescored report.

The first treatment board exposed an adapter-generation defect before any promotion decision. Qwen's tokenizer EOS token was not passed explicitly to generate, so a completion could emit the correct <|im_end|> delimiter and then continue into another synthetic chat turn. Those later turns were decoded as literal assistant/user text and sometimes contradicted an earlier correct result. A saved-prefix audit showed that removing the invalid suffix raises T2 GSM8K from 42 to 44 and leaves MATH/BBH at 30/43. Exact H100 job 729881 confirms T2 GSM8K 44/100 with zero leaked role turns. Runtime commit 2ee1f7b passes EOS explicitly and stops on exact turn delimiters. The fully matched corrected campaign is 729918--729935; every B1/T2/C2 board must use the same recorded stop-token IDs before final aggregation.

Manual behavior remains part of the gate. Eleven GSM8K cases are solved by T2 that neither control solves, with explicit multi-step arithmetic for rates, inventory, proportions, and totals. Conversely, several T2-only GPQA answers select the correct option while giving dimensionally or chemically incorrect explanations. Current evidence is therefore a credible elementary-math and logic foothold, not sophisticated general reasoning. The immediate corpus repair is verified science rationale and executable code, where V8 is plainly underrepresented.

The first full strict decision is now closed. V6 rejects implicit fallback answers from cap-exhausted traces unless the model emitted a boxed or labelled final answer. T2 reaches 26.33% macro and 148/538 solved, versus B1 18.31% and 95/538, and dense C2 22.93% and 128/538. Relative to B1, T2 adds 11 GSM8K, 12 semantic MATH-500, 12 BBH logic, and 20 GPQA answers. It beats C2 by 3.41 macro points and 20 total solved examples, so tied recurrence has earned a practical foothold rather than merely matching extra dense capacity. The numeric gate remains false only because code falls from B1 2/40 to T2 0/40. Direct code transcripts show that T2 frequently identifies the requested operation but answers in prose or emits malformed Python. Balanced jobs 729936/729943/729950 began immediately and automatic semantic decision 729983 follows their exact boards. This is a targeted repair of one failed domain, not an attempt to erase the positive four-domain result.

The strict aggregate does not clear the transcript gate unconditionally. Many T2-only GSM8K and BBH solutions contain coherent multi-step arithmetic or truth chains, but the manual sample also contains one correct arithmetic result with the wrong unit and several correct final options preceded by a contradictory explanation. GPQA rationales remain especially unreliable. Accordingly, v6 is evidence of broad practical answer improvement, not yet a claim of consistently sophisticated reasoning.

Compared Systems

T2 is an end-to-end language model, not a composition of separately fitted compiler, reactor, and reader checkpoints:

1. Frozen token embeddings expose the entire problem prompt to the workspace.
2. Learned queries compress those embeddings into 16 workspace slots.
3. One tied gated recurrent cell updates those slots for 8 internal steps while cross-attending to the prompt. A learned STOP head is trained, but the first bounded gate also reports fixed-step behavior.
4. The final workspace is projected back into existing late prompt positions through a near-zero initialized residual gate; it adds no sequence tokens and does not run the full backbone twice.
5. Workspace, prompt projection, STOP/readout, and backbone LoRA parameters are optimized jointly from rationale and final-answer losses.

C2 receives the same prompt features, parameter budget, update count, and a matched number of dense transformations, but its transformations are untied. If C2 matches or beats T2, recurrence/ETTR has not earned inclusion even if both beat B1.

Data Contract

The immediate short run uses only audited, frozen sources already in Shohin. They are seed material for a product experiment, not a sufficient final post-training corpus.

Rows are balanced by charged target tokens, not raw row count. Training data must be deduplicated before arm assignment and scanned against all development and milestone prompts. Live teacher-writer files are never direct training inputs. The existing roughly 25,000 frontier-teacher rows are seeds only.

In parallel, Stokes builds the larger post-training corpus. Admission requires source license/attribution, exact-answer or execution verification where possible, semantic and near-duplicate removal, benchmark decontamination, length/format validation, domain accounting, and reproducible manifests. The 72-hour target is 0.5--2.0 million admitted examples; the subsequent target is several million. RLVR starts only after a trained model generates a nontrivial positive rate under deterministic reward checkers.

The first external seed is pinned OpenThoughts3. Its nominal 1.2M rows are 16 annotations over roughly 75,000 unique questions, so the acquisition job keeps one deterministic best trace per normalized prompt rather than miscounting annotations as independent data. It then applies math/code/science caps, length and repeated-line checks, exact and 13-gram benchmark filtering, and marks every surviving response teacher_trace_unverified. NVIDIA OpenCodeReasoning-2 and OpenScienceReasoning-2 are audit candidates for the next stage; code is admitted only after its judgement/pass-rate and executable solution are verified, and science labels are answer-checked before use.

Jobs 729807 -> 729848 completed this seed path. The first pass selected 64,938 unique prompts (53,004 math, 6,243 science, 5,691 code); expanded replay against 59,512 evaluation prompts removed 19 more 13-gram overlaps. The frozen 64,919-row derivative has SHA-256 d9daa5720f7d27ed9c49a24be5fecf3f9db80bdd9dc12d648f11907e12928d90. It remains seed material because upstream exposes no answer, test, or judge field. A new two-pass consensus lane recovers quality signal from the sixteen independent annotations: math/science rows require at least eight extracted answers, eight votes for one exact-normalized answer, at least 60% agreement, and a three-vote margin. Code is excluded from this lane until execution can verify it. Stokes job 761159 runs from private commit e55412f; its runtime manifest SHA-256 is f515310809570f251709e9b09f8aae2986e18db1cf643a120dd4e6f50059b8b7.

Two independent expected-answer lanes run from private commit 12f3b0c. Stokes job 761160 reads pinned nvidia/OpenScienceReasoning-2@174b02c9cdf231f220765b2a1d5ece4550921894 and admits only rows whose normalized generated final answer exactly matches the provided expected answer. Job 761161 applies the same rule to pinned nvidia/OpenMathReasoning@d3d08664755704f422af97d43a7ff0ded4bd95df, restricted to problem_type=has_answer_extracted. Both replay the complete local benchmark inventory before atomic output. A source label or upstream teacher score alone cannot admit a row.

The immediate code-balance repair is already materialized rather than waiting for the first campaign to fail. pipeline/build_balanced_product_reasoning_mix.py selects the largest exact-weight subset possible without replaying or duplicating any prompt. From frozen V8 it produces 36,250 rows: 12,688 math / 7,250 code / 7,250 procedural / 9,062 teacher, SHA-256 aebf832278b8b0792cdde423b87f187808b918f5b6dc84631fde81e63a0b7fee. The artifact is hash-matched on Stokes and Newton. Code is now 20% rather than 1%, while every selected row remains unique. Balanced 200-update B1/T2/C2 jobs 729936/729943/729950 and their identical corrected board chains 729937--729956 are dependency-held behind the current full campaign, so the data-mix repair begins without another approval or scheduling gap.

This immediate artifact is balanced by rows, not by charged target tokens. Exact Qwen tokenizer audit 729980 measures the effective target-token mix as 47.14% math / 28.60% code / 7.33% procedural / 16.93% teacher over 6,094,439 charged targets. It also records 94 response-truncated and 1,488 prompt-truncated rows. The arm comparison remains matched because every arm sees the same stream, and the 28.6% effective code exposure makes this a strong test of the data-exposure hypothesis. Future promoted mixes must be balanced by charged tokens and reduce long-example prompt truncation.

OpenScienceReasoning-2 job 761160 completed with 500,000 unique expected-answer-matched rows selected from 1,600,812 raw rows after quality, duplicate, and benchmark-overlap filtering. The full output SHA-256 is e11e1923d237e1986725a7148503219e8871523649072cb38c835176854a5caa. A deterministic 10,000-row pilot has zero duplicate questions, zero replay, and SHA-256 eaca4020fc5dceab1cff41d5bae94e5308949773ee262a9153ee767deec89173. It is hash-matched on Stokes and Newton and stored with its report and CC-BY-4.0 attribution in private dataset Godlydonuts/shohin-ettr-reasoning-data. Expected-answer agreement is an admission filter, not a guarantee that every intermediate explanation is flawless. OpenMath 761161 is complete: 46,006 unique expected-answer-matched rows survive from 3,201,061 raw rows, with output SHA-256 aeb373e8fb4fedc746527653e09e3d98e73d9749cd34e5dc628f9845de125e55. OpenThoughts consensus 761159 completed with 15,201 admitted rows (14,192 math and 1,009 science), SHA-256 fb2af5eeaa0e2823355f7927997993b21ea35ea4adfdb13dfcc59e6112974346.

The first tokenizer-exact 4M-token build failed closed for the correct reason: the 10k OpenScience pilot provides only 155,899 nontruncated science response tokens, below the 400,000 target. The 1,009 consensus-science rows are too long to add any usable capacity at sequence length 1,024. A provenance-bound 50k prefix from the already deterministic/hash-shuffled 500k corpus is now materialized at SHA-256 37a9fa96931ecfe719ceddc94c024ab298496f8a6e105ce4f78814a1c9e58937. Token mix job 761173 uses that pool; verified-priority successor 761175 adds the code repair below.

The code bucket also required a provenance repair. All 7,250 V8 code rows lost their verification metadata during mix construction. Their direct CodeContests ancestor was bounded-tested on three cases, but that linkage is not available in the training rows and the bucket includes completion derivatives that cannot be scored as fully verified. A stronger TACO artifact had already replayed every supplied test but was accidentally omitted from V8. Commit 4910549 restores exact metadata only when candidate and verified response bytes match. The resulting 2,936-row corpus covers 239,533 passed tests and has SHA-256 960ebf7dbcefa92bf44b71738e510bb259cb3fab681a817b6fb3bd749760c5da. Commit 048dd8e then makes the token selector consume verified rows before weaker fallbacks within a domain. Pinned Stokes jobs 761176 -> 761178 scale this route toward 9,000 candidates and a 48-CPU all-tests audit.

The original audit 761178 timed out after six hours with zero checked rows: its implementation first scanned the entire streaming source and only then submitted matches to the 48 verification workers. This is not a data-quality failure. Commit c6febaa changes the audit to overlap deterministic source-order verification with discovery and fsync resumable output every 100 checked candidates. Replacement 761230 runs the identical 9,000-candidate input, pinned source revision, and all supplied tests with a 12-hour ceiling; final mix 761231 is held after successful completion.

V12 scale gate and V13 contingency (2026-08-03)

The exact-code bottleneck is closed. An 18-way, 576-CPU TACO replay admits 8,919 of 9,000 candidate programs; the merged output SHA-256 is 4fe6e92737ef190423aff8e53246f77f213bb1fe0651c548a008b106fb04bfb4. The resulting V12 corpus has 25,139 unique rows and 16,003,044 charged target tokens at 46% math / 18% code / 33% science / 1% procedural / 2% teacher. There are zero selected prompt truncations, response truncations, or duplicate questions. Its SHA-256 is 98527177e6e2abad364112659aaf11c71313babfde7f7031c635cdd1dc9ce5ab, matched on Stokes, transfer, and Newton.

Three one-H100 V12 arms are released: B1 731325, wide LoRA 731326, and late-two-layer release 731919. Twenty-four independent fixed-board workers are dependency-staged. This maximizes useful cluster occupancy without paying small-model multi-GPU synchronization overhead.

V11 wide LoRA closes at a 39.2% five-domain development macro after 3,000 updates. It improves selected math/code cases but does not resolve the science/logic tradeoff. The strongest direct-interaction result is late-layer update 500 at 10/12 hand-written compositions under a 1,536-token generation budget, versus 7/12 at 768 tokens. Truncation is a real evaluation/deployment constraint, but harder public domains remain weak even at 1,536 tokens.

V13 is the predeclared repair if V12's broad verified mix still loses composition. It substitutes 13% execution-verified procedural traces and uses 42/16/27/13/2 math/code/science/procedural/teacher weights. Only B1 and the manual-transcript-leading late-layer arm are staged (732353/732354), with sixteen independent benchmark workers. It will release only after exact hash/composition/truncation gates pass.

One deployment-only diagnostic is permitted alongside training. For a trace that exhausts its budget without an explicit final marker, the same frozen model receives the original problem and its own draft once, then has 64 greedy tokens to emit only a boxed final answer. This performs no search or host calculation and leaves the historical evaluator unchanged by default. It is measured separately because it repairs answer emission rather than underlying reasoning.

V13 subsequently cleared the gate with 62,874 rows, 16,003,742 charged target tokens, zero selected truncation/duplicates, and SHA-256 7df4f35d15d925b3f1a039f7cd877b1a887a942dd050b70abe5d500dc1f05621. Both staged arms are released. V12 B1 and wide are already training at about 6.76k and 5.77k charged target tok/s respectively.

The completed late-500 long-decode subset is materially stronger than the locked 768-token view: MATH 46/100 versus 26/100, GPQA 21/100 versus 16/100, science 46/100 versus 44/100, AIME unchanged at 2/30, and manual composition 10/12 versus 7/12. GSM8K, code, and BBH long-decode shards are required before computing a consistent macro.

Those shards now complete. The consistent 1,536-token late-500 board is GSM8K 79/100, MATH 46/100, HumanEval 2/20, MBPP 4/20, GPQA 21/100, and BBH 47/100. The five-domain macro is 41.6% when code is the mean of HumanEval and MBPP percentages, versus 36.6% for identical weights under the locked 768-token budget. This promotes 1,536 tokens for deployment trials without changing the historical experimental protocol.

Execution Queue

Hours 0--6: make the backbone and measurements real

1. Finish already-running jobs 729556, 729564, 729565, 729767, and 729768; record their terminal result but launch no successor in that mechanistic family.
2. Job 729773 downloads and loads the exact Qwen revision on an H100, emits a manual reasoning transcript, and records package support, parameter count, VRAM, throughput, and model class.
3. Freeze the development and milestone benchmark manifests. Run evaluator answer-extraction unit tests and a small known-answer fixture before model scoring.
4. Run B0 on the compact development board and inspect at least 25 transcripts across correct, near-miss, and malformed cases.
5. Audit the actual Newton paths, hashes, row counts, token counts, verifier flags, and domain balance of the immediate training sources.

Deliverable: a baseline scoreboard, transcript diagnosis, and admitted short- run data manifest.

Hours 6--18: build the end-to-end treatment

1. Implement one architecture-agnostic soft-workspace wrapper around the Qwen text path, plus checkpoint/save/resume and greedy generation.
2. Implement B1 and C1 through the same trainer and batch builder so the treatment is the only meaningful variable.
3. Run one 128-example overfit test and one held-out generation smoke for each arm. Required gates are finite loss, decreasing training loss, exact resume, no OOM, and coherent generated completion.
4. Profile batch size, sequence length, tokens/second, and peak H100 VRAM. Use independent one-H100 jobs for small arms; use data parallelism only after a measured throughput gain.

Deliverable: three trainable arms that generate answers from identical prompts.

Hours 18--36: matched short training

1. Train B1, T2, and C2 on the same frozen mixed stream for the same charged target-token and update budgets.
2. Reserve separate H100s when available so wall-clock queue differences do not become training differences.
3. Save periodic checkpoints and run a small non-sealed monitor board. Select checkpoints by development loss plus aggregate monitor score, never by one benchmark.
4. Run the frozen five-domain development board identically on all arms.

Deliverable: the first ETTR-versus-baseline product delta with solved counts, throughput, VRAM, and transcripts.

Hours 36--48: forced decision

No seeds, widths, or longer durations are launched merely to avoid a negative decision.

Hours 48--72: scale only the winning lever

1. Expand the winning Qwen arm or execute the SmolLM3 capacity fallback.
2. Grow the verified reasoning corpus on Stokes while Newton trains.
3. Open the sealed milestone board only for an arm that cleared the dev gate.
4. If supervised generation yields enough verified positives, launch a small RLVR pilot paired with an identical no-RLVR control.
5. Produce a final 72-hour scoreboard and a go/repair/retire decision.

Compute Layout

Small-model jobs use one H100 each unless measurement proves distributed scaling helps. Parallelism is across informative arms and evaluations:

This can consume 6--10 H100s without paying small-model DDP overhead. Extra H100s are used for data generation only when CPU verification is the measured bottleneck. Stokes remains the default home for CPU curation and verification.

Stop Conditions

• Do not launch a trillion-token scratch run from this campaign.
• Do not count best-of-N, host arithmetic, answer leakage, or benchmark contamination as native model improvement.
• Do not continue separate compiler/reactor/reader fitting.
• Do not repeat the closed layer-tap/byte-rail family after its current result.
• Do not promote an ETTR claim when an ordinary matched adapter performs as well or better.
• Preserve the protected Shohin 125M checkpoint as a baseline; it is not assumed to be the final-capacity product.

72-Hour Success Definition

Minimum success is a reproducible positive Qwen or SmolLM3 ETTR-vs-baseline development delta with real solved examples and no evaluator artifact. Strong success is a dev-gate pass plus improvement on the sealed public milestone. If neither occurs, the campaign still ends with a hard capacity/interface decision rather than another open-ended synthetic experiment.

Live Product Scoreboard (2026-08-03 16:18 EDT)

The macro is GSM8K, MATH, code mean, GPQA, and BBH. V12 late-1,000 at 1,536 tokens scores 81/48/30/16/52, with science 30, AIME 0, and manual composition 10/12. Expanded full-board jobs 733119--733126 are active or independently pending. Token-matched V13 jobs 732605/732606 and their result fan 732607--732624 test whether its equal-update loss was only target-length underexposure. V12 late-layer 2,000-update job 733201 and evaluators 733212--733220 test the single direct dose-response question.

At peak this campaign used 14 concurrent single-H100 jobs. This is deliberate: the small models scale poorly with DDP, while independent training arms and domain evaluators scale nearly linearly across allocations. Every current H100 request has a distinct product decision attached to it; unhealthy nodes evc33/evc43 remain excluded.

V13 token-match decision

The matched late-layer V13 run completes at 10.11M charged targets with GSM8K 85, MATH 21, HumanEval 25%, MBPP 25%, GPQA 14, BBH 50, AIME 0, and manual composition 7/12. Its five-domain macro is 39.0%. This closes V13 as a leader: the extra procedural exposure recovers 3.3 macro points over the underexposed checkpoint but does not beat V12. No further V13 late-layer dose or width variants are authorized.

The active positive-direction queue is now:

• 733119--733126: full 1,536-token V12 late-1,000 board;
• 733201 plus 733212--733220: V12 late-layer 2,000-update dose response;
• 733293--733299: V12 late-1,000 deterministic 768+64 finalization path;
• 732605 plus 732607--732615: token-matched B1 completion and closure.

Final Rollout-Replay And Selection Result (2026-08-04)

The protected generator is the SmolLM3-3B V12 late-two-layer checkpoint after 1,000 broad updates plus 400 verified rollout-replay updates:

artifacts/product_reasoning/smollm3-3b-a07cc9a/baseline_late2_v12u1000_rollout_replay_u400_r1/checkpoint_0000400.pt

Its SHA-256 is 34c82454e0c53609bc1ac6a9f127437080e431f147e28ae63b4080c413d9a82e. Further fresh-SFT and preference-training corrections did not clear the fixed regression gate, so this checkpoint remains immutable.

The final practical system uses greedy decoding for GSM8K, HumanEval, MBPP, GPQA-Diamond, and BBH. It generates four autonomous candidates and applies the disjointly trained completion-shape reranker only for MATH; science uses the same K=4 reranker and is reported separately. The reranker reads answer-vote, truncation, explicit-final, repetition, length, and response-shape features, but never gold answers or correctness labels at inference. Its artifact SHA-256 is b64b2f4a185e41891000123b8d0dc0bf1d5e43b8397b45a8cdd8016427d97160.

The routed system adds 3.200 macro points and 80 primary-board solves with no primary-domain regression. On the separate 500-row held-out science board it scores 227/500 = 45.40%, versus greedy 156/500 = 31.20%, adding 71 solves. On AIME-2024 it scores 4/30, versus greedy 0/30; this remains a small, high-variance signal.

MATH uses corrected exact-equivalence labels. Across 2,000 K=4 candidates, numeric fallback repairs 29 false negatives; first/modal/shape/oracle become 263/323/335/369 of 500. The original greedy MATH report changes from 249/500 to 255/500. These six evaluator repairs are not model gains. Corrected candidate-bank and shape-report SHA-256 values are 17ec3a3f2a8baa8b85e1197d0148dc20d1080e54672b3b8ab0788064316b59cf and 4dea0532d1fc853ad70cfb12ea230a512259b4b3f8ee939f8cfab151cb11b6fe.

Two tempting routes are explicitly closed. Modal self-consistency scores only 706/1250 = 56.48% on full BBH, below greedy 770/1250 = 61.60%; the fixed 100-row gain did not generalize. A counterbalanced zero-shot semantic verifier also loses to the cheap shape reranker on every non-tied routed domain. The remaining useful target is a correctness model trained on a disjoint candidate corpus: current shape-to-oracle gaps are 34 MATH answers and 57 science answers. This is an inference-selection opportunity, not evidence of native ETTR reasoning or a reason to mutate the protected generator.

Supervised late-state correctness head

The one justified successor is also complete and negative. The 8,192-prompt fresh candidate bank is first rescored with the corrected numeric matcher, repairing 160 of 32,768 trajectory labels. Sixteen independent H100 jobs then extract three pooled representations from each of backbone layer offsets -1/-2/-4/-8, yielding 24,576 hidden features plus 32 label-blind shape features per candidate. A 3,206,497-parameter pairwise correctness head is trained only on within-prompt correct/wrong pairs while holding out complete prompt identities.

On 766 held-out prompts the head selects 170, versus 168 for the existing shape reranker. The per-domain split is MATH 41/389 versus 44/389 and science 129/377 versus 124/377; only science earns a full-board test. On the 500-row science board the apparent gain reverses: neural 215, modal 220, shape 227, oracle 284. The head is therefore closed without a public MATH run or any pooling/layer/width/seed variant. The generator and promoted route remain unchanged.

Corrected source, neural model, and full-science selection SHA-256 values are 20a496867c1afc46d094a1ee2762cc553bd0460bd2915cc7e60d9c53025aa816, aa11215843ef810008f86dbd864459557ccba25ce30424d17dca076783529838, and 780e72b0d94d5cdab01361476ca8c70e7deb38962391779766112b76cc6f901d. This result says that candidate correctness is not reliably exposed by these pooled frozen states. Further progress must come from process-level checking or stronger generator training, not another static-state pooling variant.

End-to-end process verifier

The distinct process-level successor is implemented in private commit d17a1fd. It reads the entire problem and candidate trajectory, combines the final contextual state with the fixed label-blind shape vector, and updates an isolated copy of the leader's late two layers plus LoRA. The protected generator is never modified. Prompt-identity hashing reserves 6,539 train, 794 development, and 859 untouched final prompts; training alternates MATH and science correct/wrong pairs.

Job 736193 completes 300 updates and 2,400 pair presentations in 1,469.25 seconds. At the development-selected update 200, process selection reaches 203/794 versus shape 194/794: science gains nine answers and MATH ties. The untouched final fold reverses the result: process 185/859, shape 190/859; science 142/419 versus 143/419, MATH 43/440 versus 47/440. Model/report SHA-256 values are bd4cd0e0030c2cde8599b744e5a030b7697cfdb8723dd0575ff639decdf1cea4 and ddab0c3375492e0b2fcd263123197eaf01ad6ff29beb8c049b517d1cf3c7ddf4. The static and end-to-end verifier directions are therefore closed on this candidate bank.

The immediate replacement is task-specialist routing. Existing fresh-replay update 200 scored fixed MATH/science 53/38 versus leader 51/31 but was discarded for unrelated-domain regressions. Full-board jobs 736338/736339 now test it only as a routed MATH/science expert; no other domain or generator lineage changes unless those 500-row gates improve.

That gate is now running alongside a stronger domain-only test. Hash-bound MATH and science corpora contain respectively 5,624 and 4,550 unique prompts, each split evenly between fresh verifier-correct trajectories and disjoint in-domain V12 replay. Matched 400-update warm starts finish in 17 minutes. MATH checkpoints score 52/54/56 on the fixed 100-row board; science scores 35/31/35. The science branch is rejected. MATH update 400 advances to one full 500-row K=4 generation/shape-selection gate, while the earlier mixed update-200 checkpoint is measured on full MATH and science boards in parallel. Promotion requires beating the existing corrected routed results, not merely the old fixed subset: MATH 335/500 and science 227/500.

Routed MATH specialist promotion

The mixed fresh-replay update-200 checkpoint clears that scale gate. It has SHA-256 baf4623755d26ae13b4a8de8b304c07ae197d95a51f0119e0c67f62a0aa139b5 and is used only as the MATH candidate generator; every other primary domain stays on the protected leader. Across the full 500-row MATH board its K=4 candidate bank scores first/modal/shape/oracle 273/337/353/371 after exact numeric rescoring. Shape selection is 70.60%, 18 answers above the former protected-leader K=4 route at 67.00%.

This is a real generator-plus-selection gain: numeric rescoring repairs 25 candidate labels but does not account for the 18-answer delta because both routes use the same corrected evaluator. Corrected candidate and shape-report SHA-256 values are c21cabd81d809d778e6369d607449389dc17226cc3fe032463a412085b05803a and 662fafa746aa92e38e54d3f28232afbde155dc94afb4a1235e16fcfd79a301d3.

The science fan and domain-MATH update-400 fan remain one-shot gates. Their new floors are 227/500 science and 353/500 MATH. Completed GPU candidate shards are immutable; downstream selector failures are repaired from their aggregates rather than by rerunning inference.

The science result clears its floor by two answers. Corrected first/modal/shape/oracle scores are 186/220/229/308 of 500, so the mixed update-200 generator plus the unchanged shape selector becomes the science route at 45.80%. This is only a 0.40-point final-answer gain over the prior route, but its oracle rises from 56.80% to 61.60%. The generator has created 24 additional solvable candidate groups that the current selector does not capture. Corrected-candidate and shape-report SHA-256 values are 4bdc0e304f1fc3dfd07ad04fa00114b9a74c660ebce4319873f7fcc828f1faf5 and 559cfb8235cb84979ba8cd8bbe9d03ebfca7ee3ed3c806418b573621bb472156.

pipeline/merge_product_candidate_sets.py supports the resulting bounded K=8 diagnostic. It merges independent draws only when identity sets, questions, gold values, task labels, training groups, and per-source sample indices agree exactly, then reindexes the supplement contiguously. Additional inference is promoted only if unchanged shape selection beats the K=4 route; oracle-only gains are diagnostic.

The domain-only MATH update-400 branch fails its full-board decision despite the positive fixed subset. Its corrected first/modal/shape/oracle vector is 266/330/352/367; shape is one answer below the mixed update-200 route at 353. Domain-only MATH is closed. The mixed specialist also fails to transfer to AIME-2024: first/modal/shape/oracle are all 2/30, below the retained protected-leader route at 4/30. This is an oracle-level generator failure, so AIME receives no additional sampling.

The mixed specialist's full greedy results are 257/500 MATH and 171/500 science, confirming that its improvement exists before selection. The final bounded inference gate is K=8 on MATH, where the accepted K=4 result still has an 18-answer oracle gap. Jobs 736767--736776 add four independent draws and 736777 performs the validated merge and unchanged selectors. Promotion requires shape selection above 353/500; oracle-only lift does not count.

In parallel, the next training-data wave is frozen without touching public evaluation labels. After excluding V12, V13, and prior rollout banks V1/V2/V3, 4,096 prompts are selected from 34,571 still-admissible answer-verified OpenMath rows. The bank SHA-256 is e8a03468d8b229ad06d84e15931ab6daa0e6f90e763780847b858fefd6719ead. The initial under-batched jobs 736832--736863 are canceled before allocation. Replacement jobs 736893--736924 search the same exact slices with four trajectories per prompt at the previously validated prompt batch 64 production shape (674.42 generated tok/s, 49.20GB peak); 736925 admits the ledger only after complete coverage and at least 512 verifier-correct identities. This is data production, not a model result. Any continuation waits for the ledger hash and a frozen mix.

Choice-equivalence evaluator correction

Transcript review finds a correct hyperbola derivation whose bare option E was scored against \\text{(E)} as wrong. Commit d69e9e6 adds exact single-letter A--E equivalence to the MATH matcher; all 27 focused evaluator and selector tests pass. This correction is reported separately from model gain.

With the same corrected matcher, protected-leader K=4 first/modal/shape/oracle is 264/326/338/371, while mixed-update-200 K=4 is 275/339/355/372. The mixed specialist therefore retains a 17-answer, 3.40-point generator-plus-selection advantage. The current primary route is 79.303 / 71.000 / 27.948 / 17.172 / 61.600, macro 51.405%, and 2,392/3,930 solved. Mixed corrected-candidate and shape-report SHA-256 values are ab06c170c4e819f771eaed76de95cee8dc652e69e33e7c4bbc65e329fa01a41c and 70382e8ebac85e2daea00e81facd8859e72deb1ac870cf4b3227b8806ee852b7. The live K=8 gate must beat 355/500 under this evaluator.

The corrected immutable Newton rollout runtime is product_rollout_runtime_d69e9e6_r1. V4 jobs are replaced before allocation as 736941--736972, with aggregate 736973; this avoids dropping valid bare choice labels from the future verified-positive ledger.

K=8 MATH promotion

Four additional independent candidates per MATH identity are merged with the immutable K=4 bank under the validated contiguous-index contract. With the choice-aware evaluator, K=8 first/modal/shape/oracle is 275/360/366/398 of 500. Shape reaches 73.20%, 11 answers above K=4 at 71.00%.

Corrected-candidate and shape-report SHA-256 values are f4a12477883ac5ae8c0ad6ce06d284a32652fe55870c38b3a56b44fd2f88b90e and 8551baa43471bbbd60ed69d23fc85e0a8bb86f7f682d19cf29690db8abf968d9. No K=16 extension is authorized: K=8 already leaves a 32-answer shape-to-oracle gap, so the next bottleneck is generator/data quality rather than candidate availability. Released H100s immediately backfill the V4 verified OpenMath rollout jobs.

V4 target-quality correction

Manual inspection catches a defect before V4 training: the previous rollout positive path could validate an answer mentioned inside a max-token-exhausted draft, then store a response that ended mid-derivation. Correct-answer verification did not imply a complete training target. Commit 668eb01 repairs future candidate ranking and adds a hash-bound join to the original answer-verified DeepSeek-R1 response for every immutable V4 prompt.

All 4,096 bank identities join exactly. Tokenizer-exact 4,096-context filtering then retains 2,172 complete, unique traces totaling 5,002,781 target tokens, with zero selected prompt or response truncations. Every retained trace has balanced reasoning tags and an explicit final answer. The admitted data SHA-256 is d44b93aaddca5aac44a1e944b128d2487e4bdf2413a7e5fc615c64f5b6600f56; its report SHA-256 is 66635a11b3598337f1ce6d01100975dbdea892db2006fd64cce7a173fa307893.

One conservative warm continuation, job 737104, starts from the promoted mixed update-200 specialist with LR 1e-6, BS4/ACC4, and checkpoints at 100/200 updates. Source and both checkpoints are compared immediately by same-runtime fixed MATH jobs 737115--737117. This is a data-quality intervention, not another width, seed, or sampling variant.

That continuation is now closed as a negative. It completes 200 updates and 7,385,868 charged targets in 531.9 seconds at 13,885.4 target tok/s. Under the repaired scorer, which replays each saved finalization rather than silently discarding it, source/update-100/update-200 score 73/69/71 on the identical fixed 100-row MATH gate. The intermediate 61/60/61 rescore is tooling-invalid and must never be cited: it rescored drafts without the saved finalization. The fair update-200 delta is therefore -2, so neither checkpoint advances to K=8. Training-report and update-200 checkpoint SHA-256 values are 3e3d2942ec3d89c2d43abb8f7308d58047f2433de3442f323611729e56835f7f and cf65e9111d47ea9fb00bc57b425402f430de73f56aeea9361e653ec5afc4fb9b.

The completed V4 search remains useful as a capability-frontier map. Four trajectories for each of 4,096 disjoint prompts produce 16,384 candidates, 5,007 correct candidates, and 2,176 prompt identities with at least one correct answer (53.125%). Candidate, positive-ledger, and aggregate-report SHA-256 values are cc8c3895fc4d65d6c2e9ead0fada3a03fedae4d08d28b3ba41c3f4ce2ea4aefa, 3e2adaae6ee47d25563188e3b94d4de8eddf1c44610977d167b933b6db029358, and 0bf3dec0282d25dc96212dc44e0c2e00a1cc71ab847a11f0e9a3d9250a33b2d6.

The sole remaining nonredundant curriculum test uses those 2,176 identities only as a selection mask, while restoring the complete verified teacher response for every selected prompt. The joined data have SHA-256 b001068dbba1fce95002b7a7c42729a1afeb38a1f75faf31e6bb200d9f0e2d02. Tokenizer-exact filtering retains 1,734 complete rows and 3,705,995 available targets; the frozen training subset contains 1,684 rows and 3,600,762 targets at SHA-256 34380cd46602d54185be45d85859d2da0b4985922eef32593f2b08cc2a2c1580. Job 737170 completes one bounded 100-update curve from the same promoted specialist in 267 seconds at 14,450.0 charged target tok/s. Jobs 737171/737174 test updates 50/100 against the unchanged fixed gate. Their repaired scores are 66/67, both below the source floor of 73/100. Rescore-report SHA-256 values are a4d9ae0fe973a3be3af9a376f7f1d069e89f0bd62673c8a1220e1e425e088cb4 and 9a59a7f5ae6fb49fae0b091377142a68197fcb75c26917413878e7b05e2c9061. Teacher continuation is closed without another data/LR/duration retry.

This paired negative isolates a useful distinction. Complete teacher traces are not sufficient: both the uniform and student-frontier subsets overwrite useful behavior. Prior student self-distillation is still the only weight update that produced the promoted MATH specialist. The final data-quality intervention therefore admits only verifier-correct, non-exhausted student drafts whose explicit terminal answer is present in the autonomous draft itself. The V4 bank contains 691 such prompt identities; 1,309 additional positive identities require a separate finalization and 176 have no clean terminal trajectory, so neither category is silently converted into a reasoning target.

The immutable self/replay corpus contains 691 complete student trajectories and 691 disjoint V12 MATH replay rows. All 1,382 rows are retained at 4,096 context, totaling 2,136,502 charged target tokens; data and token-audit-report SHA-256 values are fd66831fddd5601aac1e921dd62822c4299489e29485699073e93a7f482090b1 and e4fd6d9be250e8b3735bd452272d3fbdcdf7d765a47adc01e8c8bfbcf972ff4f. Job 737197 completes 100 updates in under five minutes at 11,445 target tok/s. Update 50 scores 70/100: it gains five source misses but loses eight source solves, so it is three answers below the 73/100 floor. Endpoint eval 737199 is hardware-invalid because evc33 exposes no CUDA accelerator and fails before model loading. Fair replacement 737206 excludes evc33 and completes on evc46; failure-only fallback 737209 is canceled without allocation. Update 100 scores 66/100, gaining six former misses but losing thirteen source solves on the identical prompt identities. Its rescore-report SHA-256 is 1d2547d24c76f316569c43170ebc4f530414c5ac651b4ebdab40ea40763ddeac.

This closes the complete-teacher, student-frontier-teacher, and clean student-native offline-imitation family. All three reduce the unchanged fixed score despite valid targets and bounded updates. Another replay ratio, LR, seed, or duration is not justified. The promoted mixed specialist and K=8 MATH shape route at 366/500 remain the production result; the next training intervention must change the optimization signal rather than repackage offline traces.

On-policy verified-reward gate

The replacement optimization signal is a single bounded RLVR curve. For each update, the changing model samples four trajectories for each of four unseen V4 MATH prompts. The exact MATH verifier assigns a positive reward only when the final answer is correct, an explicit answer marker is present, and the trajectory terminates before the 1,536-token cap. Advantages are centered within each four-sample prompt group; uniform groups intentionally contribute no policy gradient. Four broad V12 replay rows per update provide a matched behavior anchor at weight 0.25. Training uses the promoted specialist, 4,096 context, LR 2e-7, 100 global updates, and exact optimizer resume across five-update short-backfill jobs.

This reward contract is important. An earlier answer-only diagnostic found 11/80 answer-correct candidates, but seven continued looping until the token cap after stating the answer. Rewarding those drafts would optimize answer substring discovery rather than complete autonomous reasoning. Only the four correct self-terminating candidates are positives under the production contract, establishing exactly 5% initial clean reward density.

The identical no-RLVR continuation control finishes 100 updates with fixed MATH-100 scores 71/70/71 at updates 25/50/100, versus the protected source at 73. Therefore the on-policy arm must beat 73, not merely beat the control. The production training chain is 737432--737451; fixed evaluations are 737452/737453/737454. At the time of this update the first chunk remains pending H100 priority. Only a checkpoint above 73/100 advances to the full 500-row K=8 product gate.

Update 25 is negative at 69/100, versus source 73 and replay-only control 71. The first 400 trajectories contain 79 correct answers, but only 42 are correct and self-terminating; terminal-only reward discards the other 37. Transcript inspection confirms these are not all false-positive answer substrings: one complete Legendre-formula derivation reaches and states the correct exponent 2005 before wasting the remaining context on repeated verification.

One failover objective follows directly from that error. It gives reward 1 to a correct terminal trajectory and 0.5 to a correct exhausted trajectory, but computes the latter's policy objective only through the earliest token prefix accepted by the exact verifier. This preserves the reasoning and answer while assigning no positive credit to the post-answer loop. Wrong trajectories still receive zero. Uniform sampling is also replaced before allocation because it yields mixed reward in only 13/100 prompt groups through terminal update 25. The bounded replacement uses the 2,176 disjoint V4 identities where the source already demonstrated at least one verifier-correct trajectory, restoring their exact original reward rows. The selected bank SHA-256 is 48b28c777ca6f3b3402a5b30a213ab55681009530022b644009e575adf25b604. Jobs 737538--737542 test 25 matched prefix-frontier updates and 737543 applies the unchanged fixed gate. It must exceed 73 to continue; no reward-weight, seed, or duration family follows a failure.

The gate fails despite the intended signal increase. Prefix-frontier update 20 scores 71/100 and update 25 scores 68/100. Through update 20, 42/80 groups provide mixed policy signal versus 13/100 for terminal-only uniform sampling, so this is not another signal-starvation failure. The likely common cause is update blast radius: replay-only and both RLVR objectives all regress while 157,925,376 warm-specialist parameters remain optimizer-controlled.

The final bounded RLVR boundary keeps the full specialist state loaded and checkpointed but steps only its LoRA A/B tensors, approximately 1.68M parameters. All other data, reward, replay, LR, seed, and schedule contracts remain unchanged. Jobs 737626--737630 train 25 updates and 737631 applies the fixed gate. A score at or below 73 retires this RLVR family.