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 2471–2480 of 15113 papers

TitleStatusHype
A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement LearningCode0
Reinforced Latent Reasoning for LLM-based Recommendation—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
FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers—0
Reduce Computational Cost In Deep Reinforcement Learning Via Randomized Policy Learning—0
GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning—0
Hybrid Latent Reasoning via Reinforcement LearningCode0
Steering LLM Reasoning Through Bias-Only Adaptation—0
Diffusion Blend: Inference-Time Multi-Preference Alignment for Diffusion ModelsCode0
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Benchmark Results

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