RL for Language Models

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36 papers in the last four weeks, up 71% on the four weeks before. 0.4% of all new papers.

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EAGER: Enhancing Generative Event Extraction via Reinforcement Learning with Verifiable Rewards
Sep 24, 2026Omar Adjali, Siting Liang, Omair Shahzad Bhatti +1RL for Language Models Document Information Extraction
German Research Center for Artificial Intelligence (DFKI), Germany · Carl von Ossietzky Universität Oldenburg, Germany
Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation
Sep 22, 2026Yanghe Dong, Wanting Huang, Weiran WangRL for Language Models Speech Translation
Independent Researcher · Department of Computer Science, University of Iowa, USA
WeightBridge: An Efficient Weight Transfer Library for Reinforcement Learning
Sep 21, 2026Xuanlin Jiang, Samuel Hsia, Michael Kuchnik +3RL for Language Models Distributed RL
FAIR at Meta · Harvard University · Work carried out at Meta
Score Centering Stabilizes Off-policy Reinforcement Learning
Sep 17, 2026Martin Marek, Max RyabininRL for Language Models Off-Policy RL
Together AI
EPIG-Tree: Compute-Optimal Branching for Gradient-Efficient Reinforcement Learning
Sep 17, 2026Nikita Khomich, Leopold Hermansson, Ido HakimiRL for Language Models Policy Gradient
Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flattening
Sep 16, 2026Yizhuo Li, Jianhao Yan, Yun Luo +9RL for Language Models Value Function Estimation
Shanghai Jiao Tong University · Shanghai AI Laboratory · Westlake University +4
TIAO: Token Importance-Aware Policy Optimization for Text Summarization
Sep 15, 2026Qixiu Li, Chenlong Bao, Xiang Zhu +4RL for Language Models Token-Level Credit Assignment
National University of Defense Technology
Learning to Coach for Experiential Learning
Sep 14, 2026Guanheng Chen, Tianzhu Ye, Li Dong +3RL for Language Models RL for Language Model Reasoning
Microsoft Research
Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation
Sep 14, 2026Yi Chen, Rufeng Cheng, Qiang Xie +1RL for Language Models LLM Personalization
Baidu Inc., Beijing, China
What Does an LLM Learn from Reinforcement Learning? A Mechanistic Interpretability Perspective with Fixed-SAE Track
Sep 14, 2026Lingheng Du, Yiming Tang, Xufeng Duan +1RL for Language Models Sparse Autoencoders
Peking University · National University of Singapore · The Chinese University of Hong Kong
GLARE: Generative Learning via Adversarial Reward Estimation For Social Dynamics Forecasting
Sep 14, 2026Tenghao Huang, Zhaoxuan Tan, Muhao Chen +5RL for Language Models Adversarial Imitation Learning
Microsoft · University of Notre Dame · University of California, Davis +1
Direct Preference Density Alignment for Conversational Audio Equalization
Sep 14, 2026Ioannis Stylianou, Sven Ewan Shepstone, Jon Francombe +2RL for Language Models LLM Alignment
Department of Electronic Systems Aalborg University Aalborg, Denmark · Bang & Olufsen A/S Struer, Denmark
Forty Shades of Blue: Quality-Diversity Alignment via Mode-Conditioned Reinforcement Learning
Sep 14, 2026Jiayi Yuan, Hangoo Kang, James Jihao Liu +4RL for Language Models Multi-Objective Language Model Alignment
University of Washington · 2Stanford University
Beyond Verified Answers: Solver-Informed Self-Distillation for Bootstrapping Operations Research Language Models
Sep 12, 2026Rui Zhu, Minglong Cao, Chenyu Zhou +2RL for Language Models Large Language Model-Guided Optimization
Shanghai Jiao Tong University, Shanghai, China · Fudan University, Shanghai, China
SocialRL: Refining LLMs' Social Intelligence through Multi-turn Reinforcement Learning and Reward Design
Sep 9, 2026Jianing Wang, Xintao Wang, Aili Chen +7RL for Language Models Reinforcement Learning
Fudan University · Hello Group
FiMI Banking: A Sovereign Model for Indian Retail Banking
Sep 3, 2026NPCI AI Research Team, Aman Kumar, Asit Desai +15Financial Services RL for Language Models
NPCI AI Research Team
PGPO: Potential-Guided Policy Optimization for Multi-Turn Agentic Tasks
Sep 2, 2026Yuyao Zheng, Haipeng Sun, Junwei Bao +4RL for Language Models Credit Assignment
1Fudan University · 2Zuoyebang Education Technology · 3BEDI Cloud
DMRL: Document-Mediated Reinforcement Learning for Skill Optimization in Advertising Recommendation
Sep 2, 2026Wei Zhang, Hongji Li, Song Sun +4Recommender Systems RL for Language Models
Shanghai Jiao Tong University · Kuaishou Technology
The Rise of Verbal Reinforcement Learning
Sep 1, 2026Kshitij Tayal, Arun Sharma, Genta Indra Winata +2RL for Language Models Reinforcement Learning
1AI Foundations, Capital One · University of Minnesota, USA
PersuaRL: Reinforcement Learning-Driven Multi-Expert Selection for Persuasive Dialogue Generation in Insurance
Sep 1, 2026Rohan Kirti, Akash Ghosh, Aryan Vats +5RL for Language Models LLM Agent Training
Indian Institute of Technology Patna, India · Ramakrishna Mission Vivekananda Educational and Research Institute, India · Accenture Labs, Bangalore, India
Context-Grounding Gains Are Mediated by Pre-existing Machinery: Auditing GRPO, SFT, and DPO
Sep 1, 2026Prakhar Gupta, Vaibhav GuptaRL for Language Models LLM Grounding
University of Michigan · University of Waterloo
Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement
Aug 31, 2026Yi Ding, Ruqi ZhangRL for Language Models Language Model Distillation
Department of Computer Science, Purdue University, USA
When Does Predictor-Based RL Align with Human Perception? A Study of Subjective Rewards in Codec-Based Speech Language Models
Aug 31, 2026Joonyong Park, Jerry LiReward Modeling RL for Language Models
Spellbrush
Co-Evolving Actor-Conditioned Critics for Non-Verifiable Generation
Aug 31, 2026Jinyoung Kim, Muhammad Khalifa, Lajanugen Logeswaran +4RL for Language Models LLM Evaluation
University of Michigan · LG AI Research · University of Illinois at Chicago
A Survey on Rubric-Guided Reinforcement Learning for Language Models
Aug 27, 2026Zifei Shan, Fangning ShaoRL for Language Models LLM Alignment
WeChat, Tencent · Independent Researcher
ToSCA: Leveraging Hierarchical Reinforcement Learning on Temporal and Strategic Abstractions of Conversational Agents
Aug 22, 2026Xiaoyu Wang, Qingqing Gu, Yue Zhao +5RL for Language Models Hierarchical RL
1Geely AI Lab · 2Beijing Institute of Technology · 3Peking University
SAPO: Single-Rollout Autoregressive Policy Optimization for Agentic Reinforcement Learning
Aug 20, 2026Dayang Liang, Lang Feng, Bo An +1RL for Language Models Agentic RL
Xiamen University, China
Beyond Correctness: Benchmarking and Aligning Response Behaviors in Hybrid-Thinking MLLMs
Aug 13, 2026Xinming Wang, Weinong Wang, Hongming Yang +13RL for Language Models LLM Alignment
Institute of Automation, Chinese Academy of Sciences · Tencent · Zhongguancun Academy +3
RoutePack: Expert Placement and Attention-Aware Data Packing for MoE Reinforcement Learning
Aug 12, 2026Yibo Shen, Xudong Han, Xiaowei Zhu +2RL for Language Models Expert Load Balancing
Ant Group
When the API Speaks the Wrong Language: Revisiting Post-Training for Multilingual Tool Use
Aug 12, 2026Siddharth Chauhan, Thomas Butler, Abhishek Singhania +2Supervised Fine-Tuning Multilingual Language Model Evaluation
Amazon.

Sep 24, 2026, cs.CLJ/K move · Enter open · S save

EAGER: Enhancing Generative Event Extraction via Reinforcement Learning with Verifiable Rewards

Omar Adjali, Siting Liang, Omair Shahzad Bhatti, Daniel Sonntag

German Research Center for Artificial Intelligence (DFKI), Germany · Carl von Ossietzky Universität Oldenburg, Germany

RL for Language Models Document Information Extraction LLM Information Extraction Reinforcement Learning with Verifiable Rewards

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End-to-end event extraction remains challenging for large language models as it requires simultaneous identification of event triggers, classification of event types, and extraction of schema-grounded argument spans. We present EAGER, a reinforcement learning framework for generative event extraction that combines fine-grained verifiable rewards with Schema-Contrastive Advantage Estimation to alleviate advantage collapse under sparse binary rewards. Our reward design explicitly targets structural validity, extraction accuracy, groundedness, coverage, over-generation, and span precision. Experiments across seven benchmark datasets show that EAGER consistently outperforms prompting, supervised fine-tuning, and prior reinforcement learning baselines, achieving a substantial improvement over the strongest prior method. Results demonstrate that task-aligned verifiable rewards and contrastive advantage estimation substantially improve structured extraction.

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