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 51–100 of 15113 papers

TitleStatusHype
IRanker: Towards Ranking Foundation ModelCode1
Complex Model Transformations by Reinforcement Learning with Uncertain Human GuidanceCode0
Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative ControlCode0
DiffuCoder: Understanding and Improving Masked Diffusion Models for Code GenerationCode4
Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards—0
OctoThinker: Mid-training Incentivizes Reinforcement Learning ScalingCode2
Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution GridsCode0
KnowRL: Exploring Knowledgeable Reinforcement Learning for FactualityCode1
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis—0
Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction—0
Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion—0
Robots and Children that Learn Together : Improving Knowledge Retention by Teaching Peer-Like Interactive Robots—0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model—0
LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement LearningCode5
Confucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics LearningCode2
Graphs Meet AI Agents: Taxonomy, Progress, and Future OpportunitiesCode2
Accelerating Residual Reinforcement Learning with Uncertainty Estimation—0
Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking—0
Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy EvaluationCode0
Learning Dexterous Object Handover—0
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using SparsityCode0
Dual-Objective Reinforcement Learning with Novel Hamilton-Jacobi-Bellman Formulations—0
Multi-Task Lifelong Reinforcement Learning for Wireless Sensor Networks—0
From General to Targeted Rewards: Surpassing GPT-4 in Open-Ended Long-Context Generation—0
VRAIL: Vectorized Reward-based Attribution for Interpretable Learning—0
Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access—0
Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks—0
Steering Your Diffusion Policy with Latent Space Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study—0
PeRL: Permutation-Enhanced Reinforcement Learning for Interleaved Vision-Language Reasoning—0
HiLight: A Hierarchical Reinforcement Learning Framework with Global Adversarial Guidance for Large-Scale Traffic Signal Control—0
Reasoning with Exploration: An Entropy Perspective—0
Unsupervised Skill Discovery through Skill Regions Differentiation—0
IntelliLung: Advancing Safe Mechanical Ventilation using Offline RL with Hybrid Actions and Clinically Aligned Rewards—0
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs—0
Zeroth-Order Optimization is Secretly Single-Step Policy Optimization—0
Adaptive Reinforcement Learning for Unobservable Random Delays—0
Metis-RISE: RL Incentivizes and SFT Enhances Multimodal Reasoning Model LearningCode1
AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy—0
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning AttentionCode7
RL-Guided MPC for Autonomous Greenhouse Control—0
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning—0
TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement LearningCode2
Value-Free Policy Optimization via Reward PartitioningCode0
Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning—0
A Production Scheduling Framework for Reinforcement Learning Under Real-World ConstraintsCode1
StaQ it! Growing neural networks for Policy Mirror Descent—0
ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture—0
Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes—0
Overcoming Overfitting in Reinforcement Learning via Gaussian Process Diffusion PolicyCode0
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Benchmark Results

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