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 2571–2580 of 15113 papers

TitleStatusHype
Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning—0
Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling—0
Online Iterative Self-Alignment for Radiology Report Generation—0
Retrospex: Language Agent Meets Offline Reinforcement Learning CriticCode0
An agentic system with reinforcement-learned subsystem improvements for parsing form-like documentsCode0
Time-R1: Towards Comprehensive Temporal Reasoning in LLMsCode0
Reinforcement Learning for AMR Charging Decisions: The Impact of Reward and Action Space Design—0
Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents—0
Is PRM Necessary? Problem-Solving RL Implicitly Induces PRM Capability in LLMs—0
Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes—0
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Benchmark Results

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