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 426–450 of 15113 papers

TitleStatusHype
Reinforcement Learning for Ballbot Navigation in Uneven TerrainCode1
The Cell Must Go On: Agar.io for Continual Reinforcement LearningCode1
Think-RM: Enabling Long-Horizon Reasoning in Generative Reward ModelsCode1
RLBenchNet: The Right Network for the Right Reinforcement Learning TaskCode1
GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI AgentsCode1
From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement LearningCode1
TinyV: Reducing False Negatives in Verification Improves RL for LLM ReasoningCode1
Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision TraceabilityCode1
Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMsCode1
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
ImagineBench: Evaluating Reinforcement Learning with Large Language Model RolloutsCode1
Measuring General Intelligence with Generated GamesCode1
Kalman Filter Enhanced GRPO for Reinforcement Learning-Based Language Model ReasoningCode1
Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson DiseaseCode1
Compile Scene Graphs with Reinforcement LearningCode1
DUMP: Automated Distribution-Level Curriculum Learning for RL-based LLM Post-trainingCode1
Harnessing Equivariance: Modeling Turbulence with Graph Neural NetworksCode1
Echo Chamber: RL Post-training Amplifies Behaviors Learned in PretrainingCode1
Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement LearningCode1
Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement LearningCode1
Concise Reasoning via Reinforcement LearningCode1
Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?Code1
GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical ReasoningCode1
ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement LearningCode1
Probabilistically safe and efficient model-based Reinforcement LearningCode1
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Benchmark Results

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