GenPRM: Generative Process Reward Models — interactive visual explainer | Rudrite Research

Zhao et al. published GenPRM, a generative process reward model that reasons and runs code to verify each step, achieving state-of-the-art performance where a 7B parameter model outperforms a 72B parameter model. The paper, available on arXiv, is accompanied by a free interactive visual explainer on Rudrite Research.

GenPRM: Generative Process Reward Models A process reward model that reasons and runs code to verify each step — a 7B beats a 72B. Zhao et al. · arXiv 2025 · Reasoning & RL. Read the paper ↗ https://arxiv.org/abs/2504.00891 A free, interactive, animated visual explainer of GenPRM: Generative Process Reward Models — every exhibit computed from the real formulas, with verbatim quotes from the source. Questions - What is GenPRM: Generative Process Reward Models? - A process reward model that reasons and runs code to verify each step — a 7B beats a 72B. - Who published GenPRM: Generative Process Reward Models, and where? - Zhao et al. — arXiv 2025 arXiv:2504.00891 . - Where can I find a visual explainer of GenPRM: Generative Process Reward Models? - Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source. Related explainers DeepSeek-R1 /deepseek-r1 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models /chain-of-thought Training language models to follow instructions with human feedback /instructgpt Direct Preference Optimization: Your Language Model is Secretly a Reward Model /dpo DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models /deepseekmath Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters /test-time-compute Constitutional AI: Harmlessness from AI Feedback /constitutional-ai DAPO: An Open-Source LLM Reinforcement Learning System at Scale /dapo