{"total":55,"rows":[{"id":"2504.07086","title":"A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility","created":"2025-04-09","cited_by_count":93,"area":"lr"},{"id":"2510.11977","title":"Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation","created":"2025-10-13","cited_by_count":92,"area":"agents"},{"id":"2410.01729","title":"Evaluating Robustness of Reward Models for Mathematical Reasoning","created":"2024-10-02","cited_by_count":22,"area":"lr"},{"id":"2411.07934","title":"Doubly Mild Generalization for Offline Reinforcement Learning","created":"2024-11-12","cited_by_count":21,"area":"offlinerl"},{"id":"2412.12089","title":"Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation","created":"2024-12-16","cited_by_count":20,"area":"deeprl"},{"id":"2402.03046","title":"Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning","created":"2024-02-05","cited_by_count":17,"area":"deeprl"},{"id":"2406.16241","title":"Position: Benchmarking is Limited in Reinforcement Learning Research","created":"2024-06-23","cited_by_count":15,"area":"deeprl"},{"id":"2411.16905","title":"Boundless Socratic Learning with Language Games","created":"2024-11-25","cited_by_count":12,"area":null},{"id":"2509.21882","title":"Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards","created":"2025-09-26","cited_by_count":12,"area":null},{"id":"2404.02113","title":"Position: Lifetime tuning is incompatible with continual reinforcement learning","created":"2024-04-02","cited_by_count":11,"area":"deeprl"},{"id":"2412.04233","title":"HyperMARL: Adaptive Hypernetworks for Multi-Agent RL","created":"2024-12-05","cited_by_count":11,"area":null},{"id":"2501.15893","title":"Benchmarking Quantum Reinforcement Learning","created":"2025-01-27","cited_by_count":10,"area":"deeprl"},{"id":"2312.02405","title":"BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks","created":"2023-12-05","cited_by_count":8,"area":null},{"id":"2410.08870","title":"Can we hop in general? A discussion of benchmark selection and design using the Hopper environment","created":"2024-10-11","cited_by_count":7,"area":"deeprl"},{"id":"2311.08290","title":"On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling","created":"2023-11-14","cited_by_count":6,"area":null},{"id":"2407.16312","title":"MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning","created":"2024-07-23","cited_by_count":6,"area":null},{"id":"2504.06048","title":"Trust-Region Twisted Policy Improvement","created":"2025-04-08","cited_by_count":6,"area":"deeprl"},{"id":"2507.09087","title":"Deep Reinforcement Learning with Gradient Eligibility Traces","created":"2025-07-12","cited_by_count":6,"area":"deeprl"},{"id":"2310.05723","title":"Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning","created":"2023-10-09","cited_by_count":4,"area":"offlinerl"},{"id":"2602.08690","title":"SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity","created":"2026-02-09","cited_by_count":4,"area":"deeprl"},{"id":"2411.01521","title":"Diversity Progress for Goal Selection in Discriminability-Motivated RL","created":"2024-11-03","cited_by_count":3,"area":"deeprl"},{"id":"2411.03604","title":"Temporal-Difference Learning Using Distributed Error Signals","created":"2024-11-06","cited_by_count":3,"area":"deeprl"},{"id":"2510.16175","title":"The Formalism-Implementation Gap in Reinforcement Learning Research","created":"2025-10-17","cited_by_count":3,"area":"deeprl"},{"id":"2604.08805","title":"Building Better Environments for Autonomous Cyber Defence","created":"2026-04-09","cited_by_count":3,"area":"deeprl"},{"id":"2407.18840","title":"The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning","created":"2024-07-26","cited_by_count":2,"area":"deeprl"},{"id":"2505.18347","title":"The Cell Must Go On: Agar.io for Continual Reinforcement Learning","created":"2025-05-23","cited_by_count":2,"area":"deeprl"},{"id":"2505.22772","title":"Calibrated Value-Aware Model Learning with Probabilistic Environment Models","created":"2025-05-28","cited_by_count":2,"area":"deeprl"},{"id":"2509.20008","title":"Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation","created":"2025-09-24","cited_by_count":2,"area":"deeprl"},{"id":"2603.02008","title":"Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards","created":"2026-03-02","cited_by_count":2,"area":"deeprl"},{"id":"2606.00135","title":"On Effectiveness and Efficiency of Agentic Tool-calling and RL Training","created":"2026-05-28","cited_by_count":2,"area":"agents"},{"id":"2606.06746","title":"Performance Variation in Deep Reinforcement Learning","created":"2026-06-04","cited_by_count":2,"area":"deeprl"},{"id":"2607.15003","title":"SMC-ES: Automated synthesis of formally verified control policies","created":"2026-07-16","cited_by_count":2,"area":"deeprl"},{"id":"2609.06107","title":"DataFlex-RL: An Evaluation Platform for RLVR Data Policies","created":"2026-09-05","cited_by_count":2,"area":null},{"id":"2506.07134","title":"Monotone and Conservative Policy Iteration Beyond the Tabular Case","created":"2025-06-08","cited_by_count":1,"area":null},{"id":"2512.03805","title":"Deep Reinforcement Learning for Dynamic Algorithm Configuration: A Case Study on Optimizing OneMax with the (1+($\\lambda$,$\\lambda$))-GA","created":"2025-12-03","cited_by_count":1,"area":"deeprl"},{"id":"2601.04268","title":"Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning","created":"2026-01-07","cited_by_count":1,"area":"deeprl"},{"id":"2602.12375","title":"Value Bonuses using Ensemble Errors for Exploration in Reinforcement Learning","created":"2026-02-12","cited_by_count":1,"area":"deeprl"},{"id":"2603.28416","title":"Evolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models","created":"2026-03-30","cited_by_count":1,"area":null},{"id":"2605.02469","title":"Reference-Sampled Boltzmann Projection for KL-Regularized RLVR: Target-Matched Weighted SFT, Finite One-Shot Gaps, and Policy Mirror Descent","created":"2026-05-04","cited_by_count":1,"area":null},{"id":"2606.28433","title":"Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy","created":"2026-06-26","cited_by_count":1,"area":"deeprl"},{"id":"2608.06481","title":"Safe and Robust Neural Policy Learning with Statistical Verification for Sim-to-Real Deployment in Robotics","created":"2026-08-06","cited_by_count":1,"area":null},{"id":"2306.13004","title":"Can Differentiable Decision Trees Enable Interpretable Reward Learning from Human Feedback?","created":"2023-06-22","cited_by_count":0,"area":"alignment"},{"id":"2412.16970","title":"A Research Agenda for Usability and Generalisation in Reinforcement Learning","created":"2024-12-22","cited_by_count":0,"area":"deeprl"},{"id":"2504.14636","title":"When Search Teaches Style: Causal Internalization of Tactical Priors in AlphaZero","created":"2025-04-20","cited_by_count":0,"area":null},{"id":"2506.16608","title":"Distributions as Actions: A Unified Framework for Diverse Action Spaces","created":"2025-06-19","cited_by_count":0,"area":"deeprl"},{"id":"2512.24145","title":"When Does Pairing Seeds Reduce Variance? Evidence from a Multi-Agent Economic Simulation","created":"2025-12-30","cited_by_count":0,"area":null},{"id":"2603.19864","title":"NASimJax: A GPU-Accelerated Policy Learning Framework for Penetration Testing","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2605.01131","title":"Forager: a lightweight testbed for continual learning with partial observability in RL","created":"2026-05-01","cited_by_count":0,"area":"deeprl"},{"id":"2605.05856","title":"Gradient-Momentum Coupling: A Parameter-Space Proxy for Learning Progress","created":"2026-05-07","cited_by_count":0,"area":"deeprl"},{"id":"2605.21282","title":"Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent","created":"2026-05-20","cited_by_count":0,"area":"deeprl"},{"id":"2607.25123","title":"Endpoint Replay: Compressing the Recency Buffer in Deep Reinforcement Learning","created":"2026-07-27","cited_by_count":0,"area":"deeprl"},{"id":"2608.02433","title":"Foundations of Reinforcement Learning and Control:Connections and New Perspectives","created":"2026-08-03","cited_by_count":0,"area":"deeprl"},{"id":"2609.05508","title":"When Do Options Help? Policy Necrosis and Redundant Coverage in Option-Critic","created":"2026-08-30","cited_by_count":0,"area":null},{"id":"2609.32532","title":"What Does a ProcGen Generalization Gap Measure? Action Rules, Residual Entropy, and the Missing Random Floor","created":"2026-09-26","cited_by_count":0,"area":"deeprl"},{"id":"2610.15328","title":"Anchor-and-Harvest Curriculum: A Fully Unmasked Non-LLM Curriculum for Craftax","created":"2026-10-11","cited_by_count":0,"area":null}],"next_cursor":null}