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 271–280 of 15113 papers

TitleStatusHype
Semi-pessimistic Reinforcement Learning—0
SeRL: Self-Play Reinforcement Learning for Large Language Models with Limited DataCode1
Reduce Computational Cost In Deep Reinforcement Learning Via Randomized Policy Learning—0
A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement LearningCode0
Structured Reinforcement Learning for Combinatorial Decision-MakingCode1
The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training—0
TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis—0
VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy OptimizationCode0
SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and Verifiable RewardsCode1
Step-level Reward for Free in RL-based T2I Diffusion Model Fine-tuningCode1
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Benchmark Results

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