# MedPRMBench: A Fine-grained Benchmark for Process Reward Models in Medical Reasoning

arXiv: https://arxiv.org/abs/2604.17282 · HTML: https://mlsanity.com/paper/2604.17282/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 19 Apr 2026.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)
- LLM alignment / RLHF / preference optimisation / [Medical LLM alignment](https://mlsanity.com/alignment/medical-llm-alignment/)

## Builds on (most cited first, 21 of 21)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [OpenAI o1 System Card](https://mlsanity.com/paper/2412.16720/) (2024)
- [Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations](https://mlsanity.com/paper/2312.08935/) (2023)
- [Solving math word problems with process- and outcome-based feedback](https://mlsanity.com/paper/2211.14275/) (2022)
- [ProcessBench: Identifying Process Errors in Mathematical Reasoning](https://mlsanity.com/paper/2412.06559/) (2024)
- [Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning](https://mlsanity.com/paper/2410.08146/) (2024)
- [HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs](https://mlsanity.com/paper/2412.18925/) (2024)
- [RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style](https://mlsanity.com/paper/2410.16184/) (2024)
- [Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions](https://mlsanity.com/paper/2411.14405/) (2024)
- [Evaluating Mathematical Reasoning Beyond Accuracy](https://mlsanity.com/paper/2404.05692/) (2024)
- [UltraMedical: Building Specialized Generalists in Biomedicine](https://mlsanity.com/paper/2406.03949/) (2024)
- [MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation](https://mlsanity.com/paper/2312.17080/) (2023)
- [TxGemma: Efficient and Agentic LLMs for Therapeutics](https://mlsanity.com/paper/2504.06196/) (2025)
- [MedS3: Towards Medical Slow Thinking with Self-Evolved Soft Dual-sided Process Supervision](https://mlsanity.com/paper/2501.12051/) (2025)
- [Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified Process Rewards](https://mlsanity.com/paper/2506.11474/) (2025)
- [Why Chain of Thought Fails in Clinical Text Understanding](https://mlsanity.com/paper/2509.21933/) (2025)
- [MedCEG: Reinforcing Verifiable Medical Reasoning with Critical Evidence Graph](https://mlsanity.com/paper/2512.13510/) (2025)

## Built on by (most cited first, 2 of 2)

- [The Error You See Is Not the Error You Made: Progression-aware Reasoning Origin for Reasoning Error Localization](https://mlsanity.com/paper/2609.33297/) (2026)
- [Learning Strategies to Break Judges](https://mlsanity.com/paper/2609.33773/) (2026)

## Abstract (arXiv)

Medical reasoning can reach a plausible answer while overlooking an unsafe or unsupported intermediate step. Process-Level Reward Models (PRMs) can verify such steps, yet existing PRM benchmarks largely target mathematics rather than patient-specific clinical errors. We introduce MedPRMBench, a step-level medical reasoning benchmark with 14 clinical error types, including five new and two clinically re-scoped types, and rule-defined severity grades. Built from seven medical QA sources via structured error construction and review by 86 physicians from Grade-A tertiary hospitals, it provides 6,500 evaluation questions, 13,000 reasoning chains, and 113,910 step labels, plus a leakage-clean 6,870-question training split. The best of 20 prompted critics reaches only 75.4% PRMScore, and critics differ sharply in error-type profiles and operating points. A Qwen3-8B PRM trained on the split reaches 86.6% PRMScore and 89.0% strict first-error localization; in matched ablations, physician review raises error recall by 6.55 points and cuts clean-chain false alarms from 5.6% to 2.1%. The training signal replicates across model families; without further training, the PRM transfers to ProcessBench and PRMBench, exceeding six of nine math-trained PRMs on each.