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 411–420 of 15113 papers

TitleStatusHype
Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning—0
Retrospex: Language Agent Meets Offline Reinforcement Learning CriticCode0
J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge—0
Online Iterative Self-Alignment for Radiology Report Generation—0
Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling—0
Reinforcement Learning Finetunes Small Subnetworks in Large Language Models—0
Time-R1: Towards Comprehensive Temporal Reasoning in LLMsCode0
An agentic system with reinforcement-learned subsystem improvements for parsing form-like documentsCode0
Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents—0
Spectral Policy Optimization: Coloring your Incorrect Reasoning in GRPO—0
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Benchmark Results

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