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 11–20 of 15113 papers

TitleStatusHype
Exploring the robustness of TractOracle methods in RL-based tractographyCode0
Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across ModalitiesCode0
Real-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction—0
Local Pairwise Distance Matching for Backpropagation-Free Reinforcement Learning—0
Personalized Exercise Recommendation with Semantically-Grounded Knowledge TracingCode0
Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light—0
Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data ContaminationCode1
Deep Reinforcement Learning with Gradient Eligibility TracesCode1
A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement LearningCode1
Scaling RL to Long VideosCode0
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Benchmark Results

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