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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2481–2490 of 15113 papers

TitleStatusHype
Hybrid Latent Reasoning via Reinforcement LearningCode0
On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization—0
Guided by Guardrails: Control Barrier Functions as Safety Instructors for Robotic Learning—0
GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning—0
Diffusion Blend: Inference-Time Multi-Preference Alignment for Diffusion ModelsCode0
Diffusion Self-Weighted Guidance for Offline Reinforcement Learning—0
One RL to See Them All: Visual Triple Unified Reinforcement Learning—0
Reinforcement Speculative Decoding for Fast Ranking—0
Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey—0
WiNGPT-3.0 Technical ReportCode0
Show:102550
← PrevPage 249 of 1512Next →

Benchmark Results

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