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    <abstract>Faithful reasoning is essential in medicine, where clinical decisions require transparent justification grounded in reliable evidence. Current medical LLMs either lack active access to evidence or use retrieved evidence without supervising how it should be appraised and applied during reasoning. To address this, we formalize evidence-based medicine principles as process-level criteria and introduce FaithMed, a framework that combines clinician-designed, automatically refined rubrics with reinforcement learning using step-level process reward assignment and advantage grouping. Across seven medical benchmarks, FaithMed improves over agentic-search baselines (+9% on average) and outcome-only RL (+5.8%), while raising average evidence-based medicine rubric scores over agentic-search Qwen3 baselines (+15.5%). This work demonstrates that explicit step-level supervision can improve both task success and the faithfulness of the reasoning process. Code is available at https://github.com/cxcscmu/FaithMed.</abstract>
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%0 Conference Proceedings
%T FaithMed: Training LLMs For Faithful Evidence-Based Medical Reasoning
%A Zhang, Zhiyun
%A Sun, Liwen
%A Qian, Xiang
%A Xiong, Chenyan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-etal-2026-faithmed
%X Faithful reasoning is essential in medicine, where clinical decisions require transparent justification grounded in reliable evidence. Current medical LLMs either lack active access to evidence or use retrieved evidence without supervising how it should be appraised and applied during reasoning. To address this, we formalize evidence-based medicine principles as process-level criteria and introduce FaithMed, a framework that combines clinician-designed, automatically refined rubrics with reinforcement learning using step-level process reward assignment and advantage grouping. Across seven medical benchmarks, FaithMed improves over agentic-search baselines (+9% on average) and outcome-only RL (+5.8%), while raising average evidence-based medicine rubric scores over agentic-search Qwen3 baselines (+15.5%). This work demonstrates that explicit step-level supervision can improve both task success and the faithfulness of the reasoning process. Code is available at https://github.com/cxcscmu/FaithMed.
%U https://aclanthology.org/2026.emnlp-main.1619/
%P 36807-36822

Markdown (Informal)

[FaithMed: Training LLMs For Faithful Evidence-Based Medical Reasoning](https://aclanthology.org/2026.emnlp-main.1619/) (Zhang et al., EMNLP 2026)

• FaithMed: Training LLMs For Faithful Evidence-Based Medical Reasoning (Zhang et al., EMNLP 2026)

ACL

• Zhiyun Zhang, Liwen Sun, Xiang Qian, and Chenyan Xiong. 2026. FaithMed: Training LLMs For Faithful Evidence-Based Medical Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 36807–36822, Budapest, Hungary. Association for Computational Linguistics.