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 2691–2700 of 15113 papers

TitleStatusHype
LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning—0
Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustment—0
Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL—0
OTC: Optimal Tool Calls via Reinforcement Learning—0
Relation-R1: Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relational Comprehension—0
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning—0
Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment—0
Unlearning Works Better Than You Think: Local Reinforcement-Based Selection of Auxiliary Objectives—0
Improving RL Exploration for LLM Reasoning through Retrospective Replay—0
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents—0
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Benchmark Results

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