mlsanityData to 14 Oct 2026

Audit-First VAPO: Risk-Certified Selective Updates under Imperfect Verification

arXiv 2609.33662 ↗First posted 27 Sep 2026cs.AI cs.CL cs.LGcited by 0

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Abstract (arXiv)

Imperfect verifiers can assign a harmful update direction even when clipping and regularization bound its magnitude. We introduce Audit-First VAPO, which separates discrete directional admission from continuous magnitude control. An observation-only accept-appeal-abstain policy uses a finite secondary-verification budget; its action trace is frozen before clean labels are joined. Simultaneous finite-sample bounds then certify selected harmful risk, coverage, and verifier-call rate over a predeclared policy family. Conditional Hoeffding-Azuma bounds account for the dependence induced by shared budgets, and rollout or verifier changes initiate a new certification stage. After admission, a bounded trust-clip-KL actuator controls magnitude. We evaluate two models on two reasoning benchmarks against static RLVR, matched-random selection, confidence thresholding, noise correction, and verifier augmentation. On Qwen3.5-0.8B and GSM8K at target risk ρ=0.08, RC-VAPO achieves 74.1% accuracy, selected harmful risk 0.0697, coverage 0.4125, and relative verifier cost 1.16×. At matched coverage and update magnitude, its selected-risk difference from matched random is -0.0260 with paired 95% interval [-0.0364,-0.0157]. Across asymmetric, confidence-dependent, and correlated-verifier noise, the certificate is satisfied on 57 of 60 independent runs. These comparisons isolate informative directional selection from proposal suppression, update shrinkage, and additional verifier computation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 27 references that are in the map. First posted on arXiv 27 Sep 2026.

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References · 24

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12 more
  1. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  2. 2024Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvementcited by 1,090
  3. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotationscited by 928
  4. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 853
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  12. 2025TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoningcited by 21

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