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 281–290 of 15113 papers

TitleStatusHype
FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers—0
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning—0
Steering LLM Reasoning Through Bias-Only Adaptation—0
GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning—0
Hybrid Latent Reasoning via Reinforcement LearningCode0
On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization—0
AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware BudgetingCode0
Enhancing Efficiency and Exploration in Reinforcement Learning for LLMsCode1
Diffusion Blend: Inference-Time Multi-Preference Alignment for Diffusion ModelsCode0
VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement LearningCode3
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Benchmark Results

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