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

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Showing 441–450 of 15113 papers

TitleStatusHype
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