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 681–690 of 15113 papers

TitleStatusHype
Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments—0
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks—0
Pretrained Bayesian Non-parametric Knowledge Prior in Robotic Long-Horizon Reinforcement LearningCode0
Reward Design for Reinforcement Learning AgentsCode0
Controlling Large Language Model with Latent ActionsCode0
Video-R1: Reinforcing Video Reasoning in MLLMsCode4
ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented GenerationCode1
UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement LearningCode2
Reasoning Beyond Limits: Advances and Open Problems for LLMs—0
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Benchmark Results

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