Key papers

• R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model (2025)
  Hengguang Zhou and Xirui Li and Ruochen Wang and Minhao Cheng and Tianyi Zhou and Cho-Jui Hsieh
  12.65
• You Only Need Minimal RLVR Training: Extrapolating LLMs via Rank-1 Trajectories (2026)
  Zhepei Wei et al.
  12.51
• CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning (2018)
  C\'edric Colas et al.
  12.25
• Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning (2025)
  Yuxiao Qu et al.
  12.18
• Maximum Entropy Semi-Supervised Inverse Reinforcement Learning (2026)
  Julien Audiffren et al.
  11.75
• RLVMR: Reinforcement Learning with Verifiable Meta-Reasoning Rewards for Robust Long-Horizon Agents (2025)
  Zijing Zhang et al.
  9.59
• OmniVerifier-M1: Multimodal Meta-Verifier with Explicit Structured Recalibration (2026)
  Xinchen Zhang et al.
  9.22
• ISO: An RLVR-Native Optimization Stack (2026)
  Hanqing Zhu et al.
  9.11
• Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning (2025)
  Syeda Nahida Akter et al.
  8.58
• 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities (2025)
  Kevin Wang et al.
  8.52
• Vision-Zero: Scalable VLM Self-Improvement via Strategic Gamified Self-Play (2025)
  Qinsi Wang et al.
  8.46
• VCRL: Variance-based Curriculum Reinforcement Learning for Large Language Models (2025)
  Guochao Jiang et al.
  7.35
• Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models (2025)
  Siddarth Venkatraman et al.
  7.35
• MEML-GRPO: Heterogeneous Multi-Expert Mutual Learning for RLVR Advancement (2025)
  Weitao Jia et al.
  7.30
• Agent models: Internalizing Chain-of-Action Generation into Reasoning models (2025)
  Yuxiang Zhang et al.
  7.19
• The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning (2025)
  Sheila Schoepp and Masoud Jafaripour and Yingyue Cao and Tianpei Yang and Fatemeh Abdollahi and Shadan Golestan and Zahin Sufiyan and Osmar R. Zaiane and Matthew E. Taylor
  6.96
• Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR (2026)
  Utkarsh Tyagi et al.
  6.85
• Reinforcement Learning Teachers of Test Time Scaling (2025)
  Edoardo Cetin et al.
  6.80
• SEEA-R1: Tree-Structured Reinforcement Fine-Tuning for Self-Evolving Embodied Agents (2025)
  Wanxin Tian et al.
  6.80
• TD-JEPA: Latent-predictive Representations for Zero-Shot Reinforcement Learning (2025)
  Marco Bagatella et al.
  6.80
• Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicing (2025)
  Diji Yang et al.
  6.75
• Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents (2025)
  Davide Paglieri et al.
  6.75
• SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution (2026)
  Zhiyuan Yao et al.
  6.72
• Post-Training Large Language Models via Reinforcement Learning from Self-Feedback (2025)
  Carel van Niekerk et al.
  6.64
• Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables (2025)
  Yang Chen et al.
  6.51
• Option Discovery Using LLM-guided Semantic Hierarchical Reinforcement Learning (2025)
  Chak Lam Shek and Pratap Tokekar
  5.90
• Dynamic Channel Access Via Meta-reinforcement Learning (2021)
  Ziyang Lu, M. Cenk Gursoy
  5.84
• Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning (2025)
  Suzan Ece Ada and Emre Ugur
  5.76
• Meta-Reinforcement Learning for Building Energy Management System (2022)
  Huiliang Zhang et al.
  5.72
• Reinforcement Learning for Tool-Integrated Interleaved Thinking towards Cross-Domain Generalization (2025)
  Zhengyu Chen et al.
  5.63
• Learning to Ball: Composing Policies for Long-Horizon Basketball Moves (2025)
  Pei Xu et al.
  5.57
• Complementary Reinforcement Learning (2026)
  Dilxat Muhtar et al.
  5.27
• Learning from Failures in Multi-Attempt Reinforcement Learning (2025)
  Stephen Chung et al.
  5.24
• Provably Correct Automata Embeddings for Optimal Automata-Conditioned Reinforcement Learning (2025)
  Beyazit Yalcinkaya et al.
  5.24
• Active-GRPO: Adaptive Imitation and Self-Improving Reasoning for Molecular Optimization (2026)
  Xuefeng Liu et al.
  5.01
• Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning (2026)
  Ruiheng Jiang et al.
  5.01
• Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning (2026)
  Julian G. Soltes
  5.01
• Reward Model Overoptimisation in Iterated RLHF (2025)
  Lorenz Wolf et al.
  4.93
• RLoop: An Self-Improving Framework for Reinforcement Learning with Iterative Policy Initialization (2025)
  Zeng Zhiyuan et al.
  4.90
• roto 2.0: The Robot Tactile Olympiad (2026)
  Elle Miller et al.
  4.88
• Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning (2025)
  Hongshu Guo et al.
  4.82
• Evolutionary Policy Optimization (2025)
  Jianren Wang and Yifan Su and Abhinav Gupta and Deepak Pathak
  4.82
• Autotelic Reinforcement Learning: Exploring Intrinsic Motivations for Skill Acquisition in Open-Ended Environments (2025)
  Prakhar Srivastava et al.
  4.76
• Sequential Multi-Agent Dynamic Algorithm Configuration (2025)
  Chen Lu et al.
  4.69
• Zero Reinforcement Learning Towards General Domains (2025)
  Yuyuan Zeng et al.
  4.69
• Data-Efficient RLVR via Off-Policy Influence Guidance (2025)
  Erle Zhu et al.
  4.69
• Self-Rewarding Rubric-Based Reinforcement Learning for Open-Ended Reasoning (2025)
  Zhiling Ye et al.
  4.64
• Beyond Policy Optimization: A Data Curation Flywheel for Sparse-Reward Long-Horizon Planning (2025)
  Yutong Wang et al.
  4.58
• Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning (2025)
  Patrik Reizinger et al.
  4.53
• Reinforcement Learning With Success Induced Task Prioritization (2022)
  Maria Nesterova, Alexey Skrynnik, Aleksandr Panov
  4.52
• Diversity-Enriched Option-Critic (2020)
  Anand Kamat and Doina Precup
  4.48
• Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey (2023)
  Chao Yu et al.
  4.48
• When Can Model-Free Reinforcement Learning be Enough for Thinking? (2025)
  Josiah P. Hanna and Nicholas E. Corrado
  4.47
• CCL: Collaborative Curriculum Learning for Sparse-Reward Multi-Agent Reinforcement Learning via Co-evolutionary Task Evolution (2025)
  Yufei Lin et al.
  4.42
• Learning Generalizable Skill Policy with Data-Efficient Unsupervised RL (2026)
  Jongchan Park et al.
  4.39
• Loss Smoothing for Stable Adaptation Under Distribution Shift (2026)
  Darshan Patil et al.
  4.39
• Task-Relevant Representation Decoupling for Visual Reinforcement Learning Generalization (2026)
  Jinwen Wang et al.
  4.39
• Local Motion Matters: A Deconstruct-Recompose Paradigm for Reinforcement Learning Pre-training from Videos (2026)
  Jinwen Wang et al.
  4.39
• The Rollout Infrastructure Tax in Coding-Agent Reinforcement Learning (2026)
  Daniel Thi Graviet et al.
  4.39
• From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning (2026)
  Garvit Singla et al.
  4.39