RL for Language Model Reasoning

RL: Reinforcement Learning

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CLR-voyance: Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics
May 10, 2026Aishik Nagar, Arun-Kumar Kaliya-Perumal, Yu-Hsuan Han +5Healthcare Clinical Decision Support
ASUS Intelligent Cloud Services (AICS) · Rehabilitation Research Institute of Singapore, Nanyang Technological University · Department of Family Medicine,2026 Taipei Veterans General Hospital +2
DARE: Difficulty-Adaptive Reinforcement Learning with Co-Evolved Difficulty Estimation
May 9, 2026Yang Zhou, Can Jin, Zihan Dong +7Reinforcement Learning RL for Language Model Reasoning
Rutgers University · Amazon · Washington University +1
Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization
May 9, 2026Xingyuan Hua, Sheng Yue, Ju RenRL for Language Model Reasoning Test-Time Scaling
Department of Computer Science and Technology, Tsinghua University, Beijing, China · State Key Laboratory of Internet Architecture, Tsinghua University, Beijing, China · School of Cyber Science and Technology, Sun Yat-sen University Shenzhen Campus, Shenzhen, China
Self-ReSET: Learning to Self-Recover from Unsafe Reasoning Trajectories
May 9, 2026Dongcheng Zhang, Yi Zhang, Yuxin Chen +3LLM Self-Correction RL for Language Model Reasoning
University of Science and Technology of China · Shanghai Artificial Intelligence Laboratory · National University of Singapore
CoDistill-GRPO: A Co-Distillation Recipe for Efficient Group Relative Policy Optimization
May 9, 2026Soo Min Kwon, Ziteng Sun, Ananda Theertha Suresh +2RL for Language Model Reasoning Group Relative Policy Optimization
University of Michigan, Ann Arbor · *Work done during an internship at Google · Google Research +1
How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors
May 9, 2026Yifan Xu, Junren Chen, Yifan ChenRL for Language Model Reasoning Prompt Tuning
Hong Kong Baptist University · University of Maryland, College Park
The Cancellation Hypothesis in Critic-Free RL: From Outcome Rewards to Token Credits
May 9, 2026Tianhao Cheng, Zeyu Huang, Zihan Qiu +5Reinforcement Learning Token-Level Credit Assignment
1Fudan University · 3The University of Edinburgh · 6Qwen Team, Alibaba Group +4
Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models
May 8, 2026Aswin RRV, Jacob Dineen, Divij Handa +4Synthetic Data Generation RL for Language Model Reasoning
Arizona State University · Google Cloud AI Research · Google DeepMind
AIPO: Learning to Reason from Active Interaction
May 8, 2026Junnan Liu, Linhao Luo, Thuy-Trang Vu +1RL for Language Model Reasoning LLM-Guided RL
Department of Data Science and AI, Faculty of Information Technology, Monash University, Australia
Rubric-Grounded RL: Structured Judge Rewards for Generalizable Reasoning
May 8, 2026Manish Bhattarai, Ismael Boureima, Nishath Rajiv Ranasinghe +2Reinforcement Learning RL for Language Model Reasoning
Los Alamos National Laboratory, Los Alamos, NM, USA.
Prune-OPD: Efficient and Reliable On-Policy Distillation for Long-Horizon Reasoning
May 8, 2026Zhicheng Yang, Zhijiang Guo, Yifan Song +5RL for Language Model Reasoning Language Model Distillation
1The Hong Kong University of Science and Technology (Guangzhou) · 2The Hong Kong University of Science and Technology · 3MBZUAI +2
Not All Tokens Learn Alike: Attention Entropy Reveals Heterogeneous Signals in RL Reasoning
May 8, 2026Gengyang Li, Zheng-Fan Wu, Siqi Bao +1Token-Level Credit Assignment RL for Language Model Reasoning
1Baidu · School of Software and Microelectronics, Peking University · School of Computer Science, Peking University
Your Language Model is Its Own Critic: Reinforcement Learning with Value Estimation from Actor's Internal States
May 8, 2026Yunho Choi, Jongwon Lim, Woojin Ahn +3Value Function Estimation RL for Language Model Reasoning
Graduate School of Data Science Seoul National University · Computer Science and Engineering Seoul National University
ExpThink: Experience-Guided Reinforcement Learning for Adaptive Chain-of-Thought Compression
May 8, 2026Tingcheng Bian, Yuzhe Zhang, Jing Jin +5Reward Shaping RL for Language Model Reasoning
1Baidu Inc. · 2Shenzhen University · 3Peking University +2
HTPO: Towards Exploration-Exploitation Balanced Policy Optimization via Hierarchical Token-level Objective Control
May 8, 2026Xincheng Yao, Ruoqi Li, Cheng Chen +4RL for Language Models Exploration-Exploitation Tradeoff
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University · Xiaohongshu Inc. · MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University.
Rethinking Importance Sampling in LLM Policy Optimization: A Cumulative Token Perspective
May 8, 2026Yuheng Zhang, Chenlu Ye, Shuowei Jin +4RL for Language Model Reasoning Importance Sampling
UIUC · University of Michigan · Amazon
Adaptive Negative Reinforcement for LLM Reasoning:Dynamically Balancing Correction and Diversity in RLVR
May 8, 2026Yash Ingle, Jaival Chauhan, Ankit Yadav +1Reinforcement Learning RL for Language Model Reasoning
Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India
Where to Spend Rollouts: Hit-Utility Optimal Rollout Allocation for Group-Based RLVR
May 8, 2026Tao Wang, Shuo Li, Yan Sun +2RL for Language Model Reasoning Group Relative Policy Optimization
University of Pennsylvania · New Jersey Institute of Technology · University of Tennessee, Knoxville
Beyond Negative Rollouts: Positive-Only Policy Optimization with Implicit Negative Gradients
May 7, 2026Mingwei Xu, Hao FangReinforcement Learning RL for Language Model Reasoning
University of Washington, Seattle, USA
Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key
May 7, 2026Tianle Wang, Zhaoyang Wang, Guangchen Lan +4Logical Reasoning RL for Language Model Reasoning
Purdue University · UNC Chapel Hill · Georgia Tech +1
Skip What You Can Predict: Predictive Repositioning for Policy Optimization for Efficient LLM Training
May 7, 2026Ismam Nur Swapnil, Aranya Saha, Tasneea Zahra +3RL for Language Model Reasoning Reinforcement Learning with Verifiable Rewards
Bangladesh University of Engineering and Technology · University of Maryland, College Park · Illinois Institute of Technology +1
Asymmetric On-Policy Distillation: Bridging Exploitation and Imitation at the Token Level
May 7, 2026Nan Jia, Haojin Yang, Xing Ma +6RL for Language Model Reasoning Language Model Distillation
Huazhong University of Science and Technology · Peking University · Meituan
Teaching Thinking Models to Reason with Tools: A Full-Pipeline Recipe for Tool-Integrated Reasoning
May 7, 2026Qianjia Cheng, Yuchen Zhang, Zhilin Wang +9RL for Language Model Reasoning RL for Tool Use
Zhejiang University · Shanghai AI Laboratory · Peking University +4
Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning
May 7, 2026Ömer Faruk Akgül, Rajgopal Kannan, Willie Neiswanger +1RL for Language Model Reasoning Efficient Language Model Reasoning
University of Southern California · 2DEVCOM ARL
Listwise Policy Optimization: Group-based RLVR as Target-Projection on the LLM Response Simplex
May 7, 2026Yun Qu, Qi Wang, Yixiu Mao +11RL for Language Model Reasoning Group Relative Policy Optimization
Department of Automation, Tsinghua University · 2LLM Department, Tencent
Beyond Uniform Credit Assignment: Selective Eligibility Traces for RLVR
May 7, 2026Chaoli Mou, Zhan Zhuang, Xinning Chen +1RL for Language Model Reasoning Credit Assignment in RL
Southern University of Science and Technology · City University of Hong Kong
AGPO: Asymmetric Group Policy Optimization for Verifiable Reasoning and Search Ads Relevance at JD
May 7, 2026Yang Xu, Kun Yao, Yiming Deng +3RL for Language Model Reasoning Policy Optimization
Nanjing University, Nanjing, China · Peking University, Beijing, China · JD.com, Beijing, China
Nonsense Helps: Prompt Space Perturbation Broadens Reasoning Exploration
May 7, 2026Langlin Huang, Chengsong Huang, Jinyuan Li +3LLM Prompting RL for Language Model Reasoning
Washington University in St. Louis
ZAYA1-8B Technical Report
May 6, 2026Robert Washbourne, Rishi Iyer, Tomas Figliolia +15Mixture-of-Experts Language Models RL for Language Model Reasoning
Zyphra San Francisco, CA
Self-Induced Outcome Potential: Turn-Level Credit Assignment for Agents without Verifiers
May 6, 2026Senkang Hu, Yong Dai, Xudong Han +4Agentic RL Reinforcement Learning
Hong Kong JC STEM Lab of Smart City · City University of Hong Kong · University of Oxford +4

May 10, 2026, cs.CLJ/K move · Enter open · S save

CLR-voyance: Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics

Aishik Nagar, Arun-Kumar Kaliya-Perumal, Yu-Hsuan Han, Andrew Sheng-Han Huang+4

ASUS Intelligent Cloud Services (AICS) · Rehabilitation Research Institute of Singapore, Nanyang Technological University · Department of Family Medicine,2026 Taipei Veterans General Hospital+2

Healthcare Clinical Decision Support RL for Language Model Reasoning Clinical Reasoning Partially Observable Markov Decision Processes Clinical Language Model Evaluation+1

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Inpatient clinical reasoning is a sequential decision under partial observability: the clinician sees the admission so far and must choose the next action whose downstream consequences are not yet visible. Existing clinical-LLM evaluations and RL rewards signals collapse this into closed-form retrieval, clinical journey leakage, or unanchored LLM-as-judge scoring. We introduce CLR-voyance, a framework that reformulates inpatient reasoning as a Partially Observable Markov Decision Process (POMDP) and supervises it with rewards that are simultaneously outcome-grounded and clinician-validated. We instantiate the formulation as CLR-POMDP, which partitions successful patient journeys into a policy-visible past and an oracle-only future. Using the past information, an oracle LLM generates a case-specific query-answer pair, and the first adaptive rubric for clinical reasoning which is verifiable in the future of the patient journey. These rubrics are used for both post-training and evaluation of models for inpatient clinical reasoning. We post-train Qwen3-8B and MedGemma-4B with GRPO followed by model merging, yielding state-of-the-art inpatient clinical reasoning while retaining generalist capabilities. CLR-voyance-8B achieves 84.91% on CLR-POMDP, ahead of frontier medical reasoning models like GPT-5 (77.83%) and MedGemma-27B (66.66%) and has comparable or better performance on existing medical benchmarks. To ensure a clinically meaningful setting, we conduct a large-scale clinician alignment study, where physicians curate per-case rubrics, grade candidate responses, and provide blinded pairwise preferences of model reasoning. This study provides insights on clinical LLM-as-a-judge and clinical preference-model selection, which can inform the community at large. CLR-voyance has been deployed for 6+ months at a partner public hospital, drafting thousands of reasoning-heavy inpatient notes.

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