Key papers

• Explainable Deep Reinforcement Learning: State Of The Art And Challenges (2023)
  George A. Vouros
  15.80
• Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning (2026)
  Zhenyu Hou et al.
  10.35
• Optimizing Visual Generative Models via Distribution-wise Rewards (2026)
  Ruihang Li et al.
  10.21
• Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents (2026)
  Changdae Oh et al.
  8.17
• TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning (2025)
  Zhepei Wei et al.
  7.52
• SimKO: Simple Pass@K Policy Optimization (2025)
  Ruotian Peng et al.
  6.80
• NormGuard: Reward-Preserving Norm Constraints in Flow-Matching Reinforcement Learning (2026)
  Tianlin Pan et al.
  5.82
• Large Language Models Hack Rewards, and Society (2026)
  Wei Liu et al.
  5.44
• Active-GRPO: Adaptive Imitation and Self-Improving Reasoning for Molecular Optimization (2026)
  Xuefeng Liu et al.
  5.01
• Gradient-Based Speech-to-Text Alignment for Any ASR Model: From CTC to Speech LLMs (2026)
  Albert Zeyer et al.
  5.01
• RL Post-Training Builds Compositional Reasoning Strategies (2026)
  Azwar Abdulsalam et al.
  5.01
• Verifiable Rewards for Calibrated Probabilistic Forecasting (2026)
  Sadanand Singh et al.
  4.39
• Flow-Map GRPO: Reinforcement Learning for Few-Step Flow-Map Generators via Anchored Stochastic Composition (2026)
  Zhiqi Li et al.
  4.39
• Beyond Next-Token Prediction: An RLVR Proof of Concept for Tool-Use Agents on Atlassian Workflows (2026)
  Karthikeya Aditya Vissa et al.
  4.39
• Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RL (2026)
  Juliette Decugis et al.
  4.39
• Online Data Selection Is Implicit Alignment (2026)
  Aoxiong Zeng et al.
  4.39
• RLVP: Penalize the Path, Reward the Outcome (2026)
  Bojie Li et al.
  4.39
• Agon: Competitive Cross-Model RL with Implicit Rival Grading of Reasoning (2026)
  Vladislav Beliaev
  4.39
• Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards (2026)
  Yingyu Shan et al.
  4.33
• Beyond Reward Engineering: A Data Recipe for Long-Context Reinforcement Learning (2026)
  Xiaoyue Xu et al.
  4.33
• Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation (2026)
  Marta Sumyk et al.
  4.33
• Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback (2026)
  Andreas Chouliaras et al.
  4.33
• Inverse Reinforcement Learning for Interpretable Keystroke Biomarkers in Parkinson's Disease (2026)
  Navin Bondade
  4.33
• Learning with a Single Rollout via Monte Carlo Pass@k Critic (2026)
  Fengdi Che et al.
  4.33
• Semantic Consistency Policy Optimization for Reinforcement Learning of LLM Agents (2026)
  Peng Xu et al.
  4.33
• PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction (2026)
  Dongxia Wu et al.
  4.33
• E-MRL: Cross-view Aligned Evidence-driven Multimodal Reinforcement Learning for Reliable 3D Tumor Analysis (2026)
  Sijing Li et al.
  3.45
• Reinforcement Learning Towards Broadly and Persistently Beneficial Models (2026)
  Akshay V. Jagadeesh et al.
  3.45
• Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See (2026)
  Mohamed Benabdelouahad et al.
  3.45
• PEBS: Per-rater Empirical-Bayes Shrinkage for RLHF Reward-Model Calibration (2026)
  Arnav Raj
  3.45
• Retroactive Advantage Correction: Closed-Form V-Trace Bias Correction for Delay-Aware RLHF (2026)
  Arnav Raj
  3.45
• Tandem Reinforcement Learning with Verifiable Rewards (2026)
  Difan Jiao et al.
  3.45
• Reward-SQL: Boosting Text-to-SQL via Stepwise Reasoning and Process-Supervised Rewards (2025)
  Yuxin Zhang et al.
  2.82
• RollArt: Scaling Agentic RL Training via Disaggregated Infrastructure (2025)
  Wei Gao et al.
  2.68
• SLIM-RL: Risk-Budgeted Random-Masking RL for Diffusion LLMs Without Trajectory Slicing (2026)
  Ruikang Zhao et al.
  2.00
• 3D-RFT: Reinforcement Fine-Tuning for Video-based 3D Scene Understanding (2026)
  Xiongkun Linghu et al.
  1.78
• OpenClaw-RL: Train Any Agent Simply by Talking (2026)
  Yinjie Wang et al.
  1.78
• APEX-Searcher: Augmenting LLMs' Search Capabilities through Agentic Planning and Execution (2026)
  Kun Chen et al.
  1.78
• AREAL-DTA: Dynamic Tree Attention for Efficient Reinforcement Learning of Large Language Models (2026)
  Jiarui Zhang et al.
  1.72
• Rethinking the Trust Region in LLM Reinforcement Learning (2026)
  Penghui Qi et al.
  1.72
• StagePilot: A Deep Reinforcement Learning Agent for Stage-Controlled Cybergrooming Simulation (2026)
  Heajun An et al.
  1.72
• Bayesian Preference Learning for Test-Time Steerable Reward Models (2026)
  Jiwoo Hong et al.
  1.72
• Space Syntax-guided Post-training for Residential Floor Plan Generation (2026)
  Zhuoyang Jiang and Dongqing Zhang
  1.72
• LSRIF: Logic-Structured Reinforcement Learning for Instruction Following (2026)
  Qingyu Ren et al.
  1.67
• Owen-Shapley Policy Optimization (OSPO): A Principled RL Algorithm for Generative Search LLMs (2026)
  Abhijnan Nath et al.
  1.67
• ABBEL: LLM Agents Acting through Belief Bottlenecks Expressed in Language (2025)
  Aly Lidayan et al.
  1.61
• Taming the Judge: Deconflicting AI Feedback for Stable Reinforcement Learning (2025)
  Boyin Liu et al.
  1.50
• Failure Modes of Maximum Entropy RLHF (2025)
  \"Omer Veysel \c{C}a\u{g}atan and Bar{\i}\c{s} Akg\"un
  1.44
• How LLMs Learn to Reason: A Complex Network Perspective (2025)
  Sihan Hu et al.
  1.44
• A Service-oriented Adaptive Hierarchical Incentive Mechanism For Federated Learning (2025)
  Jiaxing Cao, Yuzhou Gao, Jiwei Huang
  1.44
• Enhanced Whole Page Optimization via Mixed-Grained Reward Mechanism-Adapted Language Models (2025)
  Xinyuan Wang et al.
  1.28
• Value-Free Policy Optimization via Reward Partitioning (2025)
  Bilal Faye et al.
  1.28
• DRA-GRPO: Your GRPO Needs to Know Diverse Reasoning Paths for Mathematical Reasoning (2025)
  Xiwen Chen et al.
  1.22
• DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM Guardrails (2025)
  Yihe Deng et al.
  1.06
• Causal State Distillation For Explainable Reinforcement Learning (2023)
  Wenhao Lu, Xufeng Zhao, Thilo Fryen, et al.
  0.00
• Mental Modeling Of Reinforcement Learning Agents By Language Models (2024)
  Wenhao Lu, Xufeng Zhao, Josua Spisak, et al.
  0.00
• The Effects Of Reward Misspecification: Mapping And Mitigating Misaligned Models (2022)
  Alexander Pan, Kush Bhatia, Jacob Steinhardt
  0.00
• Explainable Reinforcement Learning: A Survey (2020)
  Erika Puiutta, Eric Msp Veith
  0.00
• Causal Confusion And Reward Misidentification In Preference-based Reward Learning (2022)
  Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson, et al.
  0.00
• Towards Measuring Goal-directedness In AI Systems (2024)
  Dylan Xu, Juan-Pablo Rivera
  0.00