arXiv AI October 15, 2026 By Li-Ching Chen, Andr\'es Franco Valiente, Yuta Ishiyama, Zachary R. Chalmers, William Chen, Kelsey Natsuhara, Taema Brinjikji, Madhumita Sushil, Travis Zack, Julian C. Hong

Reinforcement Learning In Partially Verifiable Environments for Colon Cancer Treatment Planning

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arXiv Machine Learning

Sep 22

Fathom-Vaidya: Advancing Medical Reasoning with Rubric-Based Rewards

arXiv:2609.24480v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the conv...

By Kalash Shah, Kunal Singh, Snehan J, Shreyas Singh

llms reinforcement-learning benchmarks

arXiv AI

Aug 10

ResidencyRL: Reinforcement Learning in Simulated Clinical Environments

arXiv:2608. 07418v1 Announce Type: new Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of feedback and progressively greater autonomy.

By Valentin Li\'{e}vin, Samuel Schmidgall, Tim Strother, Alex Bijamov, Akshay Goel, Anil Palepu, Chunjong Park, Vahid Balazadeh, Min Woo Sun, Marius Guerard, Justin Chen, Dave Steiner, Vikram Dhillon, Ibrahim Azar, Akhil Mehta, Nicholas Spetsieris, Shilpan Shah, Maen Abdelrahim, Amit Dahiya, Yun Liu, Katherine Chou, Yossi Matias, Avinatan Hassidim, Dale R. Webster, Quoc V. Le, Raia Hadsell, Joelle Barral, Carey Radebaugh, Aleksandra Faust, Shekoofeh Azizi, Mike Schaekermann, Po-Hsuan Cameron Chen, Tao Tu, David Racz, Lin Yang

llms agents reinforcement-learning benchmarks safety

arXiv Computation and Language

Sep 23

Learning Diagnostic Reasoning for Decision Support in Toxicology

The paper introduces DeToxR, a reinforcement‑learning‑enhanced large language model designed to support decision making in acute toxicology cases. It fuses unstructured narratives from paramedics and patients with structured vital‑sign data to predict co‑ingested substances across 14 classes. In preliminary validation, DeToxR outperforms baseline models, achieving higher micro‑F1 and recall scores for poison identification.

By Nico Oberl\"ander, David Bani-Harouni, Tobias Zellner, Nassir Navab, Florian Eyer, Matthias Keicher

llms reinforcement-learning

arXiv Computation and Language

Aug 28

CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models

CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models proposes a new framework to improve medical reasoning in LLMs. It introduces two key conditions—Causal Sufficiency and Proximal Learnability—to curate high-quality training trajectories, using agreement-based self-verification and dynamic entropy bounds. Experiments on medical multimodal and text-only benchmarks show that CARE outperforms competitors, reducing incorrect reasoning and enhancing training stability.

By Yucheng Zhou, Peng Luo, Qianning Wang, Chengzhong Xu, Jianbing Shen

llms reinforcement-learning multimodal benchmarks

arXiv AI

Aug 25

BioMed-Agent-RL: A Meta Learning, All You Need for Biomedical Applications

arXiv:2608.21864v1 Announce Type: cross Abstract: The current progress of Clinical Vision Large Language Models (C-VLLMs) has substantially improved digital diagnostics, still these frameworks often...

By Md Asaduzzaman Jabin, Zihao Wu, Tianming Liu

llms agents reinforcement-learning multimodal benchmarks safety

arXiv Machine Learning

Jun 2

OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction

arXiv:2510. 17532v2 Announce Type: replace-cross Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data.

By Raghu Vamshi Hemadri, Geetha Krishna Guruju, Kristi Topollai, Anna Ewa Choromanska

llms nlp reinforcement-learning fine-tuning benchmarks safety