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

Showing 801850 of 15113 papers

TitleStatusHype
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RLCode4
Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning0
Probabilistic Shielding for Safe Reinforcement Learning0
Agent models: Internalizing Chain-of-Action Generation into Reasoning modelsCode2
Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba ModelsCode0
Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language ModelsCode5
UAV-Assisted Coverage Hole Detection Using Reinforcement Learning in Urban Cellular Networks0
A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game0
GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks0
Automated Proof of Polynomial Inequalities via Reinforcement LearningCode0
Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction0
ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning0
Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security0
Vairiational Stochastic Games0
Policy Constraint by Only Support Constraint for Offline Reinforcement LearningCode0
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement LearningCode4
Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation0
Generative Multi-Agent Q-Learning for Policy Optimization: Decentralized Wireless Networks0
Tractable Representations for Convergent Approximation of Distributional HJB Equations0
Multi-Fidelity Policy Gradient Algorithms0
Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation0
Can We Optimize Deep RL Policy Weights as Trajectory Modeling?0
Energy-Weighted Flow Matching for Offline Reinforcement Learning0
Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A reviewCode0
Provably Correct Automata Embeddings for Optimal Automata-Conditioned Reinforcement Learning0
Towards Autonomous Reinforcement Learning for Real-World Robotic Manipulation with Large Language Models0
Data-Efficient Learning from Human Interventions for Mobile Robots0
Rebalanced Multimodal Learning with Data-aware Unimodal Sampling0
DreamerV3 for Traffic Signal Control: Hyperparameter Tuning and Performance0
Rewarding Doubt: A Reinforcement Learning Approach to Confidence Calibration of Large Language Models0
Quantitative Resilience Modeling for Autonomous Cyber Defense0
Accelerating Multi-Task Temporal Difference Learning under Low-Rank Representation0
What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret0
All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning0
Active Alignments of Lens Systems with Reinforcement Learning0
Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRsCode3
Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning0
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model LearningCode2
Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models0
Minimax Optimal Reinforcement Learning with Quasi-Optimism0
Reinforcement learning with combinatorial actions for coupled restless banditsCode1
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process0
Scalable Reinforcement Learning for Virtual Machine Scheduling0
Discrete Codebook World Models for Continuous ControlCode1
Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions0
What Makes a Good Diffusion Planner for Decision Making?Code2
Adaptive Reinforcement Learning for State Avoidance in Discrete Event Systems0
DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement LearningCode4
Subtask-Aware Visual Reward Learning from Segmented Demonstrations0
Hierarchical and Modular Network on Non-prehensile Manipulation in General Environments0
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Benchmark Results

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