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 2271–2280 of 15113 papers

TitleStatusHype
Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training—0
Local Pairwise Distance Matching for Backpropagation-Free Reinforcement Learning—0
High-Throughput Distributed Reinforcement Learning via Adaptive Policy SynchronizationCode0
Real-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction—0
Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light—0
Personalized Exercise Recommendation with Semantically-Grounded Knowledge TracingCode0
Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across ModalitiesCode0
Exploring the robustness of TractOracle methods in RL-based tractographyCode0
The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs—0
Scaling RL to Long Videos—0
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Benchmark Results

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