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
Ray: A Distributed Framework for Emerging AI ApplicationsCode4
Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation ModelsCode4
Delving into RL for Image Generation with CoT: A Study on DPO vs. GRPOCode4
Kwai Keye-VL Technical ReportCode4
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RLCode4
Mastering Diverse Domains through World ModelsCode4
TorchRL: A data-driven decision-making library for PyTorchCode4
TDMPBC: Self-Imitative Reinforcement Learning for Humanoid Robot ControlCode4
Diffusion Policy Policy OptimizationCode4
Discovering faster matrix multiplication algorithms with reinforcement learningCode4
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
Video-R1: Reinforcing Video Reasoning in MLLMsCode4
SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion PlanningCode4
Skywork Open Reasoner 1 Technical ReportCode4
RLlib: Abstractions for Distributed Reinforcement LearningCode4
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 Flow: Distributed Reinforcement Learning is a Dataflow ProblemCode4
R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPOCode3
Discovered Policy OptimisationCode3
Practical Deep Reinforcement Learning Approach for Stock TradingCode3
Distributed Prioritized Experience ReplayCode3
R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement LearningCode3
Rainbow: Combining Improvements in Deep Reinforcement LearningCode3
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement LearningCode3
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement LearningCode3
Demystifying Long Chain-of-Thought Reasoning in LLMsCode3
OpenSpiel: A Framework for Reinforcement Learning in GamesCode3
OGBench: Benchmarking Offline Goal-Conditioned RLCode3
DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing ReasoningCode3
Deep Reinforcement LearningCode3
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning ResearchCode3
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement LearningCode3
Perception-R1: Pioneering Perception Policy with Reinforcement LearningCode3
MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the MetaverseCode3
Adversarial Cheap TalkCode3
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement LearningCode3
A Clean Slate for Offline Reinforcement LearningCode3
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning LibraryCode3
Learning Bipedal Walking On Planned Footsteps For Humanoid RobotsCode3
ACEGEN: Reinforcement learning of generative chemical agents for drug discoveryCode3
Learning to Reason under Off-Policy GuidanceCode3
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and PlanningCode3
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning AlgorithmsCode3
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character controlCode3
Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRsCode3
o1-Coder: an o1 Replication for CodingCode3
CarDreamer: Open-Source Learning Platform for World Model based Autonomous DrivingCode3
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid AlgorithmsCode3
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Benchmark Results

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