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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 15113 papers

TitleStatusHype
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1Code2
Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement LearningCode2
Efficient World Models with Context-Aware TokenizationCode2
Emergent Tool Use From Multi-Agent AutocurriculaCode2
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline DataCode2
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
Distributional Soft Actor-Critic with Three RefinementsCode2
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender SystemsCode2
Efficient Online Reinforcement Learning with Offline DataCode2
Exploring the Limit of Outcome Reward for Learning Mathematical ReasoningCode2
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
Direct Multi-Turn Preference Optimization for Language AgentsCode2
DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical DialogueCode2
Accelerated Methods for Deep Reinforcement LearningCode2
Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionCode2
A Simulation Benchmark for Autonomous Racing with Large-Scale Human DataCode2
Assessment of Reinforcement Learning for Macro PlacementCode2
EfficientZero V2: Mastering Discrete and Continuous Control with Limited DataCode2
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement LearningCode2
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, ChallengesCode2
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function OptimizationCode2
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement LearningCode2
Diffusion Models for Reinforcement Learning: A SurveyCode2
A Critical Evaluation of AI Feedback for Aligning Large Language ModelsCode2
Diffusion Actor-Critic with Entropy RegulatorCode2
Show:102550
← PrevPage 12 of 605Next →

Benchmark Results

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