Key papers

• Step-dpo: Step-wise Preference Optimization For Long-chain Reasoning Of Llms (2024)
  Xin Lai, Zhuotao Tian, Yukang Chen, et al.
  31.31
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (2025)
  DeepSeek-AI et al.
  31.18
• Agentscope: A Flexible Yet Robust Multi-agent Platform (2024)
  Dawei Gao, Zitao Li, Xuchen Pan, et al.
  29.54
• Is DPO Superior To PPO For LLM Alignment? A Comprehensive Study (2024)
  Shusheng Xu, Wei Fu, Jiaxuan Gao, et al.
  27.53
• Agentgym: Evolving Large Language Model-based Agents Across Diverse Environments (2024)
  Zhiheng Xi, Yiwen Ding, Wenxiang Chen, et al.
  27.20
• Direct Nash Optimization: Teaching Language Models To Self-improve With General Preferences (2024)
  Corby Rosset, Ching-An Cheng, Arindam Mitra, et al.
  25.73
• Teaching Large Language Models To Reason With Reinforcement Learning (2024)
  Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, et al.
  25.08
• Self-play With Execution Feedback: Improving Instruction-following Capabilities Of Large Language Models (2024)
  Guanting Dong, Keming Lu, Chengpeng Li, et al.
  24.59
• Federatedscope-llm: A Comprehensive Package For Fine-tuning Large Language Models In Federated Learning (2023)
  Weirui Kuang, Bingchen Qian, Zitao Li, et al.
  24.26
• Expel: LLM Agents Are Experiential Learners (2023)
  Andrew Zhao, Daniel Huang, Quentin Xu, et al.
  23.10
• Stepcoder: Improve Code Generation With Reinforcement Learning From Compiler Feedback (2024)
  Shihan Dou, Yan Liu, Haoxiang Jia, et al.
  22.83
• Reinforced Self-training (rest) For Language Modeling (2023)
  Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.
  22.75
• LLM Evaluators Recognize And Favor Their Own Generations (2024)
  Arjun Panickssery, Samuel R. Bowman, Shi Feng
  22.26
• Smartplay: A Benchmark For Llms As Intelligent Agents (2023)
  Yue Wu, Xuan Tang, Tom M. Mitchell, et al.
  21.32
• Vineppo: Refining Credit Assignment In RL Training Of Llms (2024)
  Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, et al.
  21.27
• Understanding The Effects Of RLHF On LLM Generalisation And Diversity (2023)
  Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, et al.
  21.24
• Alphazero-like Tree-search Can Guide Large Language Model Decoding And Training (2023)
  Xidong Feng, Ziyu Wan, Muning Wen, et al.
  21.21
• Interpretable Preferences Via Multi-objective Reward Modeling And Mixture-of-experts (2024)
  Haoxiang Wang, Wei Xiong, Tengyang Xie, et al.
  20.64
• Recursive Introspection: Teaching Language Model Agents How To Self-improve (2024)
  Yuxiao Qu, Tianjun Zhang, Naman Garg, et al.
  19.75
• RAIN: Your Language Models Can Align Themselves Without Finetuning (2023)
  Yuhui Li, Fangyun Wei, Jinjing Zhao, et al.
  19.73
• Optimization-based Prompt Injection Attack To Llm-as-a-judge (2024)
  Jiawen Shi, Zenghui Yuan, Yinuo Liu, et al.
  19.67
• Remax: A Simple, Effective, And Efficient Reinforcement Learning Method For Aligning Large Language Models (2023)
  Ziniu Li, Tian Xu, Yushun Zhang, et al.
  19.63
• Efficient Exploration For Llms (2024)
  Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao, et al.
  19.21
• Panacea: Pareto Alignment Via Preference Adaptation For Llms (2024)
  Yifan Zhong, Chengdong Ma, Xiaoyuan Zhang, et al.
  18.85
• Rewarding Progress: Scaling Automated Process Verifiers For LLM Reasoning (2024)
  Amrith Setlur, Chirag Nagpal, Adam Fisch, et al.
  18.76
• Preference Fine-tuning Of Llms Should Leverage Suboptimal, On-policy Data (2024)
  Fahim Tajwar, Anikait Singh, Archit Sharma, et al.
  18.58
• Navgpt-2: Unleashing Navigational Reasoning Capability For Large Vision-language Models (2024)
  Gengze Zhou, Yicong Hong, Zun Wang, et al.
  18.43
• Pku-saferlhf: Towards Multi-level Safety Alignment For Llms With Human Preference (2024)
  Jiaming Ji, Donghai Hong, Borong Zhang, et al.
  18.18
• Aligning Large Language Models With Human Preferences Through Representation Engineering (2023)
  Wenhao Liu, Xiaohua Wang, Muling Wu, et al.
  16.98
• Reason For Future, Act For Now: A Principled Framework For Autonomous LLM Agents With Provable Sample Efficiency (2023)
  Zhihan Liu, Hao Hu, Shenao Zhang, et al.
  16.93
• When Is Tree Search Useful For LLM Planning? It Depends On The Discriminator (2024)
  Ziru Chen, Michael White, Raymond Mooney, et al.
  16.92
• Inferaligner: Inference-time Alignment For Harmlessness Through Cross-model Guidance (2024)
  Pengyu Wang, Dong Zhang, Linyang Li, et al.
  16.83
• Iterative Nash Policy Optimization: Aligning Llms With General Preferences Via No-regret Learning (2024)
  Yuheng Zhang, Dian Yu, Baolin Peng, et al.
  16.80
• Formal-llm: Integrating Formal Language And Natural Language For Controllable Llm-based Agents (2024)
  Zelong Li, Wenyue Hua, Hao Wang, et al.
  16.74
• Archer: Training Language Model Agents Via Hierarchical Multi-turn RL (2024)
  Yifei Zhou, Andrea Zanette, Jiayi Pan, et al.
  16.61
• Self-explore: Enhancing Mathematical Reasoning In Language Models With Fine-grained Rewards (2024)
  Hyeonbin Hwang, Doyoung Kim, Seungone Kim, et al.
  16.59
• Let's Reward Step By Step: Step-level Reward Model As The Navigators For Reasoning (2023)
  Qianli Ma, Haotian Zhou, Tingkai Liu, et al.
  16.52
• Pairwise Proximal Policy Optimization: Harnessing Relative Feedback For LLM Alignment (2023)
  Tianhao Wu, Banghua Zhu, Ruoyu Zhang, et al.
  16.51
• Tree Search for LLM Agent Reinforcement Learning (2025)
  Yuxiang Ji et al.
  16.46
• Real: Efficient RLHF Training Of Large Language Models With Parameter Reallocation (2024)
  Zhiyu Mei, Wei Fu, Kaiwei Li, et al.
  16.34
• Language Agents With Reinforcement Learning For Strategic Play In The Werewolf Game (2023)
  Zelai Xu, Chao Yu, Fei Fang, et al.
  16.23
• Gaia2: Benchmarking LLM Agents On Dynamic And Asynchronous Environments (2026)
  Romain Froger, Pierre Andrews, Matteo Bettini, et al.
  16.12
• K-level Reasoning: Establishing Higher Order Beliefs In Large Language Models For Strategic Reasoning (2024)
  Yadong Zhang, Shaoguang Mao, Tao Ge, et al.
  16.09
• RLHF Deciphered: A Critical Analysis Of Reinforcement Learning From Human Feedback For Llms (2024)
  Shreyas Chaudhari, Pranjal Aggarwal, Vishvak Murahari, et al.
  16.03
• Learn-by-interact: A Data-centric Framework For Self-adaptive Agents In Realistic Environments (2025)
  Hongjin Su, Ruoxi Sun, Jinsung Yoon, et al.
  16.01
• Prompt A Robot To Walk With Large Language Models (2023)
  Yen-Jen Wang, Bike Zhang, Jianyu Chen, et al.
  15.96
• RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization For Llms (2024)
  John Dang, Arash Ahmadian, Kelly Marchisio, et al.
  15.93
• ISR-LLM: Iterative Self-refined Large Language Model For Long-horizon Sequential Task Planning (2023)
  Zhehua Zhou, Jiayang Song, Kunpeng Yao, et al.
  15.73
• Toolchain*: Efficient Action Space Navigation In Large Language Models With A* Search (2023)
  Yuchen Zhuang, Xiang Chen, Tong Yu, et al.
  15.59
• Embodied Agent Interface: Benchmarking Llms For Embodied Decision Making (2024)
  Manling Li, Shiyu Zhao, Qineng Wang, et al.
  15.57
• LMRL Gym: Benchmarks For Multi-turn Reinforcement Learning With Language Models (2023)
  Marwa Abdulhai, Isadora White, Charlie Snell, et al.
  15.55
• Direct Preference Optimization With An Offset (2024)
  Afra Amini, Tim Vieira, Ryan Cotterell
  15.48
• Bonbon Alignment For Large Language Models And The Sweetness Of Best-of-n Sampling (2024)
  Lin Gui, Cristina Gârbacea, Victor Veitch
  15.46
• Metaagents: Large Language Model Based Agents For Decision-making On Teaming (2023)
  Yuan Li, Lichao Sun, Yixuan Zhang
  15.46
• Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning (2026)
  Xinyu Tang et al.
  15.34
• Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning (2025)
  Sikuan Yan et al.
  15.31
• Towards Analyzing And Understanding The Limitations Of DPO: A Theoretical Perspective (2024)
  Duanyu Feng, Bowen Qin, Chen Huang, et al.
  15.28
• Prompt Optimization In Multi-step Tasks (PROMST): Integrating Human Feedback And Heuristic-based Sampling (2024)
  Yongchao Chen, Jacob Arkin, Yilun Hao, et al.
  15.18
• Do LLM Agents Have Regret? A Case Study In Online Learning And Games (2024)
  Chanwoo Park, Xiangyu Liu, Asuman Ozdaglar, et al.
  15.08
• Agent-pro: Learning To Evolve Via Policy-level Reflection And Optimization (2024)
  Wenqi Zhang, Ke Tang, Hai Wu, et al.
  15.06