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 101150 of 15113 papers

TitleStatusHype
R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPOCode3
Deep Reinforcement LearningCode3
DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing ReasoningCode3
Practical Deep Reinforcement Learning Approach for Stock TradingCode3
Test-Time Training Scaling Laws for Chemical Exploration in Drug DesignCode3
OpenSpiel: A Framework for Reinforcement Learning in GamesCode3
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement LearningCode3
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement LearningCode3
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement LearningCode3
OGBench: Benchmarking Offline Goal-Conditioned RLCode3
Multi-SWE-bench: A Multilingual Benchmark for Issue ResolvingCode3
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning ResearchCode3
Adversarial Cheap TalkCode3
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning AlgorithmsCode3
MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the MetaverseCode3
Fine-Tuning Language Models from Human PreferencesCode3
o1-Coder: an o1 Replication for CodingCode3
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement LearningCode3
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid AlgorithmsCode3
CarDreamer: Open-Source Learning Platform for World Model based Autonomous DrivingCode3
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning LibraryCode3
Learning to Reason under Off-Policy GuidanceCode3
Learning Bipedal Walking for Humanoids with Current FeedbackCode3
Learning Bipedal Walking On Planned Footsteps For Humanoid RobotsCode3
Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement LearningCode3
imitation: Clean Imitation Learning ImplementationsCode3
Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQLCode3
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise RewardCode3
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RLCode3
Accelerating Goal-Conditioned RL Algorithms and ResearchCode3
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and PlanningCode3
Perception-R1: Pioneering Perception Policy with Reinforcement LearningCode3
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement LearningCode2
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex ManipulationCode2
Generalized Inner Loop Meta-LearningCode2
FlowReasoner: Reinforcing Query-Level Meta-AgentsCode2
Foundation Policies with Hilbert RepresentationsCode2
Smooth Exploration for Robotic Reinforcement LearningCode2
Flightmare: A Flexible Quadrotor SimulatorCode2
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Flow: A Modular Learning Framework for Mixed Autonomy TrafficCode2
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative FinanceCode2
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based MethodsCode2
Feedback Efficient Online Fine-Tuning of Diffusion ModelsCode2
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
Exploring the Limit of Outcome Reward for Learning Mathematical ReasoningCode2
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1Code2
Evolving Reservoirs for Meta Reinforcement LearningCode2
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path PlanningCode2
AndroidEnv: A Reinforcement Learning Platform for AndroidCode2
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Benchmark Results

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