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 2461–2470 of 15113 papers

TitleStatusHype
MT^3: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning—0
Interleaved Reasoning for Large Language Models via Reinforcement Learning—0
Incentivizing Reasoning from Weak SupervisionCode0
Unveiling the Compositional Ability Gap in Vision-Language Reasoning ModelCode0
MedDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support—0
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement LearningCode0
Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback—0
VLMLight: Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning—0
Reduce Computational Cost In Deep Reinforcement Learning Via Randomized Policy Learning—0
A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement LearningCode0
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Benchmark Results

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