GenPRM: Scaling Test-Time Compute of Process Reward Models via
  Generative Reasoning
Recent advancements in Large Language Models (LLMs) have shown that it is
promising to utilize Process Reward Models (PRMs) as verifiers to enhance the
performance of LLMs. However, current PRMs face three key challenges: (1)
limited process supervision and generalization capabilities, (2) dependence on
scalar value prediction without leveraging the generative abilities of LLMs,
and (3) inability to scale the test-time compute of PRMs. In this work, we
introduce GenPRM, a generative process reward model that performs explicit
Chain-of-Thought (CoT) reasoning with code verification before providing
judgment for each reasoning step. To obtain high-quality process supervision
labels and rationale data, we propose Relative Progress Estimation (RPE) and a
rationale synthesis framework that incorporates code verification. Experimental
results on ProcessBench and several mathematical reasoning tasks show that
GenPRM significantly outperforms prior PRMs with only 23K training data from
MATH dataset. Through test-time scaling, a 1.5B GenPRM outperforms GPT-4o, and
a 7B GenPRM surpasses Qwen2.5-Math-PRM-72B on ProcessBench. Additionally,
GenPRM demonstrates strong abilities to serve as a critic model for policy
model refinement. This work establishes a new paradigm for process supervision
that bridges the gap between PRMs and critic models in LLMs. Our code, model,
and data will be available in https://ryanliu112.github.io/GenPRM.
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Runze Liu
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BowenZhou
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GenPRM: Scaling Test-Time Compute of Process Reward Models via
  Generative Reasoning
Recent advancements in Large Language Models (LLMs) have shown that it is
promising to utilize Process Reward Models (PRMs) as verifiers to enhance the
performance of LLMs. However, current PRMs face three key challenges: (1)
limited process supervision and generalization capabilities, (2) dependence on
scalar value prediction without leveraging the generative abilities of LLMs,
and (3) inability to scale the test-time compute of PRMs. In this work, we
introduce GenPRM, a generative process reward model that performs explicit
Chain-of-Thought (CoT) reasoning with code verification before providing
judgment for each reasoning step. To obtain high-quality process supervision
labels and rationale data, we propose Relative Progress Estimation (RPE) and a
rationale synthesis framework that incorporates code verification. Experimental
results on ProcessBench and several mathematical reasoning tasks show that
GenPRM significantly outperforms prior PRMs with only 23K training data from
MATH dataset. Through test-time scaling, a 1.5B GenPRM outperforms GPT-4o, and
a 7B GenPRM surpasses Qwen2.5-Math-PRM-72B on ProcessBench. Additionally,
GenPRM demonstrates strong abilities to serve as a critic model for policy
model refinement. This work establishes a new paradigm for process supervision
that bridges the gap between PRMs and critic models in LLMs. Our code, model,
and data will be available in https://ryanliu112.github.io/GenPRM.
GenPRM, a generative process reward model with Chain-of-Thought reasoning and code verification, outperforms existing PRMs and critic models in process supervision tasks with less data and computational resources.
Large Language Models Process Reward Models Chain-of-Thought Relative Progress Estimation rationale synthesis GenPRM ProcessBench mathematical reasoning tasks critic model policy model refinement