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 2451–2460 of 15113 papers

TitleStatusHype
Rendering-Aware Reinforcement Learning for Vector Graphics Generation—0
Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning—0
Breaking the Performance Ceiling in Complex Reinforcement Learning requires Inference Strategies—0
What Can RL Bring to VLA Generalization? An Empirical Study—0
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement LearningCode0
MT^3: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning—0
MedDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support—0
Interleaved Reasoning for Large Language Models via Reinforcement Learning—0
Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured Multi-Turn Decomposition—0
Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RLCode0
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Benchmark Results

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