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About this paper
Shiping Gao, Hongzhan Chen, Xiaojun Quan, Qifan Wang, Lifu Huang
The authors introduce IPVRM, which directly trains a reward model to score partial reasoning prefixes from verifier outcomes, then uses score changes to guide policy updates over sampled and high-probability candidate tokens. On Qwen3-0.6B, it reaches 39.1 average F1 on ProcessBench, versus 25.4 for Implicit PRM; DistRL with IPVRM reaches AVG@8 16.7, within verifiable reasoning tasks.
Summary by alphaXiv.
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