PRISM is a contrastive, policy-aware training framework for process reward models that reduces false positives by 22% on PRMBench and boosts downstream accuracy up to 33% in Best-of-N selection by learning reliable relative comparisons instead of pointwise labels.
arXiv preprint arXiv:2509.21016 , year=
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ADR generates novel verifiable code tasks via atomic decomposition and recombination, outperforming heuristic baselines in originality, difficulty, and downstream RLVR gains across coding domains.
VeriGate adds verifier-gated step-level supervision to GRPO via cumulated PRM rewards and group-normalized token advantages, raising accuracy 20% and 12% on 1.5B and 7B models on MATH and six benchmarks.
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The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning
PRISM is a contrastive, policy-aware training framework for process reward models that reduces false positives by 22% on PRMBench and boosts downstream accuracy up to 33% in Best-of-N selection by learning reliable relative comparisons instead of pointwise labels.
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VeriGate: Verifier-Gated Step-Level Supervision for GRPO
VeriGate adds verifier-gated step-level supervision to GRPO via cumulated PRM rewards and group-normalized token advantages, raising accuracy 20% and 12% on 1.5B and 7B models on MATH and six benchmarks.