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 361–370 of 15113 papers

TitleStatusHype
Learn to Reason Efficiently with Adaptive Length-based Reward ShapingCode2
HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving—0
From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement LearningCode1
Normalized Cut with Reinforcement Learning in Constrained Action Space—0
RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning—0
KIPPO: Koopman-Inspired Proximal Policy Optimization—0
Bellman operator convergence enhancements in reinforcement learning algorithms—0
Self-Evolving Curriculum for LLM Reasoning—0
Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models—0
AAPO: Enhance the Reasoning Capabilities of LLMs with Advantage Momentum—0
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Benchmark Results

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