OCET1: On-Policy Counterexample Edit Training

Status: prospectively frozen before proposal output, 2026-08-10
Scope: source-disjoint training/development; holdout sealed

Diagnosis and hypothesis

RIFT1's tied second pass is 99.84% syntactically valid and strongly draft-causal, but marks KEEP on many wrong model-generated proposals. Existing training used deterministic answer substitutions; evaluation asks the model to judge its own natural errors. The objective is therefore off-policy.

OCET1 changes the learning distribution, not rank, seed, duration, prompt, or decoder budget. The immutable aligned DSET-Q35 owner generates one mandatory rewrite for every source-disjoint training presentation. The generic executor materializes it. Training labels are then derived mechanically:

• exact executed proposal: KEEP;
• incorrect local executed proposal: REPLACE_LAST from its model-owned final surface to the independently verified final surface;
• malformed proposal: fail-closed to the original draft and its existing verified transaction.

The original textual draft and proposal feedback are removed before the next attempt. Only source, executed model-owned proposal, and training-only exact transaction remain. At inference, the system is the RIFT tied recurrent transducer with no verifier, label, solver, or semantic host.

Frozen stages

1. Generate exactly one immutable aligned proposal per 15,278 training row (7,639 sources) with the pinned DSET checkpoint, greedy decoding, and the FRET executor. Hash every proposal and execution.
2. Audit complete source/proposal/target retention, local edit derivability, source-disjointness, corruption-family balance, and zero holdout use.
3. Warm-start the exact 1,179,648-parameter DSET residual, reset optimizer, and run one 256-update on-policy fit. No router/expert/base changes.
4. Evaluate the tied two-transition architecture on the exact opened 1,908-row development board against DSET, ISET, RIFT, and a matched label-permuted on-policy control. Hidden draft remains the causal control.

Before capability output, exact gates will retain RIFT's minimums: >=1,874 overall, >=220/256 choice, >=945/954 clean, >=859/954 fault, +13 over ISET and every newly trained matched control, >=95% valid scripts, and zero exhaustion. One pass may open one sealed holdout. A miss closes OCET1 without data-count, update, rank, layer, LR, seed, threshold, or recurrence variants.

Claim boundary

OCET1 tests whether closed-loop learning on immutable self-generated counterexamples makes a model-owned recurrent edit architecture operational. DAgger, self-training, edit models, and verifier-derived training labels are prior art; no primitive is claimed as novel. The possible contribution is the causal same-family draft-to-edit fixed-point system and its matched controls.