The Verifier is the Curriculum: Precision Sets the Return on Search in Code Self-Distillation
Post-training a code generator against a learned judge can optimize proxy features that raise the score without improving the artifact. We study the opposite signal: a deterministic, judge-free filter that asks only whether a generated project launches cleanly under a headless engine (strict-launch). Under this gate, rejection-sampling self-distillation compounds out-of-family generalization: on GameCraft-Bench a 14B model raises the per-candidate clean-launch rate on four held-out families from 8.8% to 42.2% and coverage at 32 candidates from 84% to 100%, the gold references' own ceiling, beating the supervised model on every one of the 25 held-out tasks. The gate costs one engine invoca...
| Authors | Chenyu Zhou, Qiliang Jiang, Shuning Wu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 June 2026 |
| Last updated at source | 15 September 2026 |
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