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 176–200 of 15113 papers

TitleStatusHype
Rulebook: bringing co-routines to reinforcement learning environmentsCode2
CaRL: Learning Scalable Planning Policies with Simple RewardsCode2
FlowReasoner: Reinforcing Query-Level Meta-AgentsCode2
Stop Summation: Min-Form Credit Assignment Is All Process Reward Model Needs for ReasoningCode2
Generative Auto-Bidding with Value-Guided ExplorationsCode2
Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement LearningCode2
NoisyRollout: Reinforcing Visual Reasoning with Data AugmentationCode2
MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement LearningCode2
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language ModelsCode2
Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning IncentivizationCode2
Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation SchemeCode2
GPG: A Simple and Strong Reinforcement Learning Baseline for Model ReasoningCode2
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1Code2
UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement LearningCode2
Unlocking Efficient Long-to-Short LLM Reasoning with Model MergingCode2
Surrogate Learning in Meta-Black-Box Optimization: A Preliminary StudyCode2
OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-ImprovementCode2
Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-TuningCode2
Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor controlCode2
Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language ModelsCode2
Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse RewardsCode2
V-Max: A Reinforcement Learning Framework for Autonomous DrivingCode2
Agent models: Internalizing Chain-of-Action Generation into Reasoning modelsCode2
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model LearningCode2
What Makes a Good Diffusion Planner for Decision Making?Code2
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Benchmark Results

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