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
Efficient World Models with Context-Aware TokenizationCode2
Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement LearningCode2
Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionCode2
AndroidEnv: A Reinforcement Learning Platform for AndroidCode2
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline DataCode2
Emergent Tool Use From Multi-Agent AutocurriculaCode2
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1Code2
DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical DialogueCode2
AGILE: A Novel Reinforcement Learning Framework of LLM AgentsCode2
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement LearningCode2
Digi-Q: Learning Q-Value Functions for Training Device-Control AgentsCode2
A Critical Evaluation of AI Feedback for Aligning Large Language ModelsCode2
Accelerated Methods for Deep Reinforcement LearningCode2
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender SystemsCode2
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with TransformersCode2
Efficient Online Reinforcement Learning with Offline DataCode2
EfficientZero V2: Mastering Discrete and Continuous Control with Limited DataCode2
Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem SolvingCode2
Diffusion Models for Reinforcement Learning: A SurveyCode2
Direct Multi-Turn Preference Optimization for Language AgentsCode2
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement LearningCode2
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
DiffMimic: Efficient Motion Mimicking with Differentiable PhysicsCode2
DIAMBRA Arena: a New Reinforcement Learning Platform for Research and ExperimentationCode2
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Benchmark Results

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