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 2541–2550 of 15113 papers

TitleStatusHype
Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning—0
Think-J: Learning to Think for Generative LLM-as-a-JudgeCode0
Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models—0
Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks—0
RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning—0
UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning—0
Bellman operator convergence enhancements in reinforcement learning algorithms—0
Self-Evolving Curriculum for LLM Reasoning—0
Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning—0
ToTRL: Unlock LLM Tree-of-Thoughts Reasoning Potential through Puzzles Solving—0
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Benchmark Results

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