arXiv:2604.27644 · v3
ANCORA: Learning to Question via Manifold-Anchored Self-Play for Verifiable Reasoning
摘要
We propose a paradigm shift toward open-ended curriculum self-play: rather than learning to answer on a fixed prompt set, a unified policy learns to question: generating verifiable problems, solving them, and turning verifier feedback into self-improvement. We introduce ANCORA, in which the policy alternates between a Proposer that synthesizes novel specifications and a Solver that produces verified solutions, anchored by three mechanisms: a two-level group-relative update coupling Proposer advantages across specifications with Solver advantages across solution attempts; iterative self-distilled SFT projecting the base model onto its valid-output manifold before RL; and a UCB-guided Curriculum DAG whose policy-induced problem set can provably expand under self-composition. Without these stabilizers, sparse verifier feedback drives Proposer collapse even under MLRL-aligned rewards; with them, ANCORA bootstraps a verifiable curriculum from curated seeds. Instantiated in Verus, ANCORA lifts Dafny2Verus pass@1 from a 26.6% SFT baseline to 81.5% in test-time training (TTT, 0-shot), outperforming PSV self-play (1-shot) by 15.8 points; in a transfer setting, training from Dafny2Verus seeds yields 36.2% and 17.2% pass@1 on MBPP and HumanEval.
作者备注：v3: Added a prompt-matched comparison with PSV, data-provenance audits, and a limitations section; corrected transcription errors in pass@5/10 values of Table 3; updated abstract; reformatted for journal submission