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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–225 of 15113 papers

TitleStatusHype
Pangu DeepDiver: Adaptive Search Intensity Scaling via Open-Web Reinforcement Learning—0
ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language ModelsCode5
The Hallucination Dilemma: Factuality-Aware Reinforcement Learning for Large Reasoning ModelsCode1
Contextual Integrity in LLMs via Reasoning and Reinforcement Learning—0
Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning—0
ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning—0
Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation—0
Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners—0
Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability—0
Grounded Reinforcement Learning for Visual Reasoning—0
LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Trainin—0
Segment Policy Optimization: Effective Segment-Level Credit Assignment in RL for Large Language ModelsCode1
Normalizing Flows are Capable Models for RLCode1
Fortune: Formula-Driven Reinforcement Learning for Symbolic Table Reasoning in Language Models—0
ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning EngineeringCode2
Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data—0
Hybrid Cross-domain Robust Reinforcement Learning—0
Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw PuzzlesCode1
Diversity-Aware Policy Optimization for Large Language Model Reasoning—0
Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization—0
DIP-R1: Deep Inspection and Perception with RL Looking Through and Understanding Complex Scenes—0
Fine-Tuning Next-Scale Visual Autoregressive Models with Group Relative Policy Optimization—0
Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software EngineeringCode1
Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Control—0
Unsupervised Transcript-assisted Video Summarization and Highlight Detection—0
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Benchmark Results

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