SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Showing 51100 of 15113 papers

TitleStatusHype
Let's Verify Step by StepCode4
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RLCode4
Kwai Keye-VL Technical ReportCode4
Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation ModelsCode4
CPGD: Toward Stable Rule-based Reinforcement Learning for Language ModelsCode4
TorchRL: A data-driven decision-making library for PyTorchCode4
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement LearningCode4
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement LearningCode4
Video-R1: Reinforcing Video Reasoning in MLLMsCode4
TDMPBC: Self-Imitative Reinforcement Learning for Humanoid Robot ControlCode4
Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement LearningCode4
T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoTCode4
Skywork Open Reasoner 1 Technical ReportCode4
s3: You Don't Need That Much Data to Train a Search Agent via RLCode4
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization BenchmarkCode4
RLlib: Abstractions for Distributed Reinforcement LearningCode4
SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion PlanningCode4
Stop Overthinking: A Survey on Efficient Reasoning for Large Language ModelsCode4
Discovered Policy OptimisationCode3
Distributed Prioritized Experience ReplayCode3
Rainbow: Combining Improvements in Deep Reinforcement LearningCode3
R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement LearningCode3
R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPOCode3
Perception-R1: Pioneering Perception Policy with Reinforcement LearningCode3
Demystifying Long Chain-of-Thought Reasoning in LLMsCode3
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement LearningCode3
Practical Deep Reinforcement Learning Approach for Stock TradingCode3
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement LearningCode3
DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing ReasoningCode3
Adversarial Cheap TalkCode3
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement LearningCode3
OpenSpiel: A Framework for Reinforcement Learning in GamesCode3
Reinforcement Learning Enhanced LLMs: A SurveyCode3
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement LearningCode3
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning LibraryCode3
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and PlanningCode3
Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRsCode3
MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the MetaverseCode3
Multi-SWE-bench: A Multilingual Benchmark for Issue ResolvingCode3
Learning to Reason under Off-Policy GuidanceCode3
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning AlgorithmsCode3
CarDreamer: Open-Source Learning Platform for World Model based Autonomous DrivingCode3
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character controlCode3
Learning Bipedal Walking On Planned Footsteps For Humanoid RobotsCode3
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid AlgorithmsCode3
Is Value Learning Really the Main Bottleneck in Offline RL?Code3
Deep Reinforcement LearningCode3
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning ResearchCode3
Learning Bipedal Walking for Humanoids with Current FeedbackCode3
o1-Coder: an o1 Replication for CodingCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76Unverified
2PPOMean Normalized Performance0.58Unverified