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 2421–2430 of 15113 papers

TitleStatusHype
DIP-R1: Deep Inspection and Perception with RL Looking Through and Understanding Complex Scenes—0
Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning—0
Fine-Tuning Next-Scale Visual Autoregressive Models with Group Relative Policy Optimization—0
Unsupervised Transcript-assisted Video Summarization and Highlight Detection—0
Contextual Integrity in LLMs via Reasoning and Reinforcement Learning—0
Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Control—0
Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization—0
Diversity-Aware Policy Optimization for Large Language Model Reasoning—0
LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Trainin—0
Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability—0
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Benchmark Results

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