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 461–470 of 15113 papers

TitleStatusHype
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
Kalman Filter Enhanced GRPO for Reinforcement Learning-Based Language Model ReasoningCode1
INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning—0
Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains—0
DanceGRPO: Unleashing GRPO on Visual GenerationCode5
Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems—0
Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control—0
Learning Value of Information towards Joint Communication and Control in 6G V2X—0
X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real—0
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots—0
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Benchmark Results

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