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 276–300 of 15113 papers

TitleStatusHype
VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy OptimizationCode0
TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis—0
The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training—0
Step-level Reward for Free in RL-based T2I Diffusion Model Fine-tuningCode1
SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and Verifiable RewardsCode1
FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers—0
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning—0
GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning—0
Steering LLM Reasoning Through Bias-Only Adaptation—0
On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization—0
Enhancing Efficiency and Exploration in Reinforcement Learning for LLMsCode1
AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware BudgetingCode0
Hybrid Latent Reasoning via Reinforcement LearningCode0
Diffusion Blend: Inference-Time Multi-Preference Alignment for Diffusion ModelsCode0
VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement LearningCode3
Guided by Guardrails: Control Barrier Functions as Safety Instructors for Robotic Learning—0
One Policy but Many Worlds: A Scalable Unified Policy for Versatile Humanoid Locomotion—0
Reinforcement Speculative Decoding for Fast Ranking—0
WiNGPT-3.0 Technical ReportCode0
One RL to See Them All: Visual Triple Unified Reinforcement Learning—0
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement LearningCode4
Diffusion Self-Weighted Guidance for Offline Reinforcement Learning—0
Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey—0
Towards Revealing the Effectiveness of Small-Scale Fine-tuning in R1-style Reinforcement LearningCode1
Thinking Fast and Right: Balancing Accuracy and Reasoning Length with Adaptive RewardsCode0
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Benchmark Results

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