Key papers

• CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning (2018)
  C\'edric Colas et al.
  12.25
• Parallel Exploration Via Negatively Correlated Search (2019)
  Peng Yang, Qi Yang, Ke Tang, et al.
  8.60
• Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model (2020)
  Gen Li et al.
  7.83
• CCLF: A Contrastive-curiosity-driven Learning Framework For Sample-efficient Reinforcement Learning (2022)
  Chenyu Sun, Hangwei Qian, Chunyan Miao
  7.16
• SimKO: Simple Pass@K Policy Optimization (2025)
  Ruotian Peng et al.
  6.80
• Design Space Exploration Of Approximate Computing Techniques With A Reinforcement Learning Approach (2023)
  Sepide Saeedi, Alessandro Savino, Stefano di Carlo
  5.84
• Spotlight: Synergizing Seed Exploration and Spot GPUs for DiT RL Post-Training (2026)
  Ruiqi Lai et al.
  5.44
• Playing 20 Question Game with Policy-Based Reinforcement Learning (2018)
  Huang Hu et al.
  5.24
• Full Bayesian Reinforcement Learning via LF-IBIS (2026)
  Stefano Masini et al.
  5.01
• Mathematical methods of reinforcement learning (2026)
  Denis Belomestny et al.
  5.01
• Entropy Pacing Policy Optimization for Multi-Task Agentic Reinforcement Learning (2026)
  Zetian Hu et al.
  5.01
• Diverse Exploration Via Conjugate Policies For Policy Gradient Methods (2019)
  Andrew Cohen, Xingye Qiao, Lei Yu, et al.
  4.52
• Reinforcement Learning With Success Induced Task Prioritization (2022)
  Maria Nesterova, Alexey Skrynnik, Aleksandr Panov
  4.52
• Learning Generalizable Skill Policy with Data-Efficient Unsupervised RL (2026)
  Jongchan Park 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
• From Pixels to Temporal Correlations: Learning Informative Representations for Reinforcement Learning Pre-training (2026)
  Jinwen Wang et al.
  4.39
• Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RL (2026)
  Juliette Decugis et al.
  4.39
• Inertia-1: An Open Exploration of Wearable Motion Foundation Models (2026)
  Zongzhe Xu et al.
  4.39
• UP: Unbounded Positive Asymmetric Optimization for Breaking the Exploration-Stability Dilemma (2026)
  Chongyu Fan et al.
  4.39
• Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production (2026)
  Arun Malik
  4.39
• HPG-Diff: Hierarchical physics-guided diffusion with differentiable connectivity constraints for topology optimization (2026)
  Jinbo Yang et al.
  4.39
• RLVP: Penalize the Path, Reward the Outcome (2026)
  Bojie Li et al.
  4.39
• Creativity from Friction: Human-AI Interaction for Exploratory Structural Design (2026)
  Ricardo Maia Avelino et al.
  4.39
• Breaking the Solver Bottleneck: Training Task Generators at the Learnable Frontier (2026)
  Lorenz Wolf et al.
  4.33
• UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning (2026)
  Mohamed Nabail et al.
  4.33
• ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning (2026)
  Anurag Akula et al.
  4.33
• Uncertainty-aware reinforcement learning for chemical language models (2026)
  Borja Medina et al.
  4.33
• ExTra: Exploratory Trajectory Optimization for Language Model Reinforcement Learning (2026)
  Wenyang Hu et al.
  4.33
• Beyond One-Size-Fits-All: Diagnosis-Driven Online Reinforcement Learning with Offline Priors (2026)
  Guozheng Ma et al.
  4.33
• FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation (2026)
  Shuyi Zhang et al.
  4.33
• Ornstein-uhlenbeck Adaptation As A Mechanism For Learning In Brains And Machines (2024)
  Jesus Garcia Fernandez, Nasir Ahmad, Marcel van Gerven
  3.58
• Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation (2026)
  Shenghui Zhang et al.
  3.51
• Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling (2026)
  Yiran Guo et al.
  3.23
• CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving (2025)
  Xiaoji Zheng et al.
  3.10
• How Log-barrier Helps Exploration In Policy Optimization (2026)
  Leonardo Cesani, Matteo Papini, Marcello Restelli
  1.94
• Regret-aware Policy Optimization: Environment-level Memory For Replay Suppression Under Delayed Harm (2026)
  Prakul Sunil Hiremath
  1.83
• Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation (2026)
  Zixuan Liu et al.
  1.78
• AcceRL: A Distributed Asynchronous Reinforcement Learning and World Model Framework for Vision-Language-Action Models (2026)
  Chengxuan Lu et al.
  1.78
• Dynamic Dual-Granularity Skill Bank for Agentic RL (2026)
  Songjun Tu et al.
  1.78
• Segment to Focus: Guiding Latent Action Models in the Presence of Distractors (2026)
  Hamza Adnan et al.
  1.72
• GraphDancer: Training LLMs to Explore and Reason over Graphs via Curriculum Reinforcement Learning (2026)
  Yuyang Bai et al.
  1.72
• On the Role of Computation in Reinforcement Learning (2026)
  Raj Ghugare et al.
  1.72
• ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Intrinsic Adaptation (2026)
  Jingqi Zhou et al.
  1.72
• Online Learning In Mdps With Partially Adversarial Transitions And Losses (2026)
  Ofir Schlisselberg, Tal Lancewicki, Yishay Mansour
  1.72
• Safe Exploration via Policy Priors (2026)
  Manuel Wendl et al.
  1.67
• Unsupervised Learning of Efficient Exploration: Pre-training Adaptive Policies via Self-Imposed Goals (2026)
  Octavio Pappalardo
  1.67
• Deep Reinforcement Learning for Dynamic Algorithm Configuration: A Case Study on Optimizing OneMax with the (1+($\lambda$,$\lambda$))-GA (2025)
  Tai Nguyen et al.
  1.61
• VULPO: Context-Aware Vulnerability Detection via On-Policy LLM Optimization (2025)
  Youpeng Li et al.
  1.56
• Finding Kissing Numbers with Game-theoretic Reinforcement Learning (2025)
  Chengdong Ma et al.
  1.56
• Interpretable By Design: Query-specific Neural Modules For Explainable Reinforcement Learning (2025)
  Mehrdad Zakershahrak
  1.56
• Dual-Uncertainty Guided Policy Learning for Multimodal Reasoning (2025)
  Rui Liu et al.
  1.50
• Q-Learning with Fine-Grained Gap-Dependent Regret (2025)
  Haochen Zhang et al.
  1.50
• Using Reinforcement Learning to Optimize the Global and Local Crossing Number (2025)
  Timo Brand et al.
  1.44
• How LLMs Learn to Reason: A Complex Network Perspective (2025)
  Sihan Hu et al.
  1.44
• Exploring Large Action Sets With Hyperspherical Embeddings Using Von Mises-fisher Sampling (2025)
  Walid Bendada, Guillaume Salha-Galvan, Romain Hennequin, et al.
  1.33
• Learning Safe, Constrained Policies Via Imitation Learning: Connection To Probabilistic Inference And A Naive Algorithm (2025)
  George Papadopoulos, George A. Vouros
  1.33
• Q-learning With Posterior Sampling (2025)
  Priyank Agrawal, Shipra Agrawal, Azmat Azati
  1.28
• Adaptable Hindsight Experience Replay For Search-based Learning (2025)
  Alexandros Vazaios, Jannis Brugger, Cedric Derstroff, et al.
  1.28
• Beyond-expert Performance With Limited Demonstrations: Efficient Imitation Learning With Double Exploration (2025)
  Heyang Zhao, Xingrui Yu, David M. Bossens, et al.
  1.28