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 2261–2270 of 15113 papers

TitleStatusHype
Playing Atari with Deep Reinforcement LearningCode1
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink PaintingCode1
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback—0
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)—0
QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation—0
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning—0
VAR-MATH: Probing True Mathematical Reasoning in Large Language Models via Symbolic Multi-Instance Benchmarks—0
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities—0
Fly, Fail, Fix: Iterative Game Repair with Reinforcement Learning and Large Multimodal Models—0
Kevin: Multi-Turn RL for Generating CUDA Kernels—0
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Benchmark Results

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