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 431–440 of 15113 papers

TitleStatusHype
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
Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMsCode1
Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision TraceabilityCode1
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
ImagineBench: Evaluating Reinforcement Learning with Large Language Model RolloutsCode1
Kalman Filter Enhanced GRPO for Reinforcement Learning-Based Language Model ReasoningCode1
Measuring General Intelligence with Generated GamesCode1
Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson DiseaseCode1
Compile Scene Graphs with Reinforcement LearningCode1
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Benchmark Results

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