arXiv AI

Learning Clinical-Trial Strategy: Offline Policy Training for Decision Agents

arXiv:2608. 03606v1 Announce Type: new Abstract: Clinical development is sequential decision-making under uncertainty, where a sponsor must plan a portfolio of experiments from heterogeneous evidence.

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 AI

Jun 30

An AI agent for treatment reasoning over a biomedical tool universe

arXiv:2606. 28692v1 Announce Type: new Abstract: Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an appropriate therapy.

By Shanghua Gao, Ayush Noori, Richard Zhu, Curtis Ginder, Zhenglun Kong, Xiaorui Su, Justin Kauffman, Benjamin S. Glicksberg, Joshua Lampert, Ankit Sakhuja, Ashwin Sawant, ATHENA-R1 Evaluation Consortium, David A. Clifton, Noa Dagan, Ran Balicer, Marinka Zitnik

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 24

PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning

arXiv:2607. 21419v1 Announce Type: new Abstract: In long-horizon LLM agent reinforcement learning, weak policies often repeat similar failures, producing uninformative rollout trajectories and limiting effective policy optimization.

By Yipeng Shi, Zhipeng Ma, Yue Wang, Qitai Tan, Yang Li, Peng Chen, Zhengzhou Zhu

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 9

SDM-Q: Cost-Aware Staged Decision-Making for Multi-Omics Classification with Deep Q-Learning

arXiv:2605. 31014v2 Announce Type: replace Abstract: Multi-omics data provide complementary molecular characterizations of disease phenotypes and play an important role in disease diagnosis and subtype classification in precision medicine.

By Nan Mu, Yangfan Xiao, Ling Wang, Xiaoning Li, Yue Kang, Chen Zhao

reinforcement-learning

arXiv Machine Learning

Jun 19

Insulin4RL: Real-Time Insulin Management in the Intensive Care Unit for Offline Reinforcement Learning

arXiv:2606. 19481v1 Announce Type: new Abstract: Offline reinforcement learning (ORL) offers the potential to improve the quality of clinical decision-making using historical electronic health record (EHR) data.

By Thomas Frost, Steve Harris

reinforcement-learning

arXiv AI

Jun 30

MedEvoEval: Evaluating Continual Evolution of Doctor Agents through Simulated Clinical Episodes

arXiv:2606. 28900v1 Announce Type: new Abstract: Doctor agents are moving beyond single-turn answer generation toward evolving clinical decision systems.

By Hui Zhang

agents benchmarks

arXiv AI

Jul 8

Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning

arXiv:2607. 05458v1 Announce Type: cross Abstract: Large language model (LLM) agents are usually improved by changing prompts, models, or hand-written workflows, while the execution harness around the model is treated as fixed infrastructure.

By Haiwen Yi, Xinyuan Song

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 17

Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation

arXiv:2606. 17405v1 Announce Type: new Abstract: Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints.

By Xinyu Qin, Anil K. Sood, Ruiheng Yu, Sara Corvigno, Elaine Stur, Lu Wang

reinforcement-learning safety

arXiv Machine Learning

Jun 3

Fairness Definitions and Metrics in Deep Reinforcement Learning for Drug Discovery in Healthcare: A Rapid Evidence Review

arXiv:2606. 02902v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) is increasingly applied to de novo molecular design, but choices in data, rewards, and evaluation can yield uneven performance across disease areas and chemotypes.

By Esmaeil Shakeri, Ronnie de Souza Santos, Behrouz Far

reinforcement-learning safety

arXiv AI

Aug 5

TumorBoard: Evidence-Grounded Multi-Agent Decision Support for Longitudinal Neuro-Oncology

arXiv:2608. 03190v1 Announce Type: new Abstract: Neuro-oncology decisions require coordinated interpretation of serial MRI, pathology, molecular markers, treatment history, performance status, and evolving guidelines.

By Yantong Liu, Zheyu Zhang, Runpeng Liu, Mu Xitang, Seong-Yoon Shin, Hyun-Ae Lee

agents benchmarks safety

arXiv Machine Learning

Jun 25

Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents

arXiv:2606. 26080v1 Announce Type: new Abstract: Process reward models enable fine-grained, step-level evaluation of LLMs, yet building them for agentic settings remains prohibitively difficult: long-horizon interactions, irreversible actions, and stochastic environment feedback make both human annotation and Monte Carlo estimation infeasible at scale.

By Changdae Oh, Wendi Li, Seongheon Park, Samuel Yeh, Tanwi Mallick, Sharon Li

llms agents reinforcement-learning benchmarks

arXiv AI

1d ago

Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning

arXiv:2608. 16831v1 Announce Type: new Abstract: Generative pretraining established reusable task representations; later work on language-based task conditioning and in-context learning showed that a fixed model could adapt its behavior from instructions and demonstrations.

By Minh-Ha Nguyen, Cathy Shyr

llms reinforcement-learning benchmarks

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