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 801–810 of 15113 papers

TitleStatusHype
Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning—0
MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale Reinforcement LearningCode4
Probabilistic Shielding for Safe Reinforcement Learning—0
Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba ModelsCode0
Agent models: Internalizing Chain-of-Action Generation into Reasoning modelsCode2
Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language ModelsCode5
A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game—0
Automated Proof of Polynomial Inequalities via Reinforcement LearningCode0
GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks—0
Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction—0
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Benchmark Results

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