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 251–260 of 15113 papers

TitleStatusHype
Unveiling the Compositional Ability Gap in Vision-Language Reasoning ModelCode0
Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback—0
MedDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support—0
Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured Multi-Turn Decomposition—0
What Can RL Bring to VLA Generalization? An Empirical Study—0
Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning—0
Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RLCode0
Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network—0
MT^3: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning—0
DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical DialogueCode2
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Benchmark Results

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