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 601650 of 15113 papers

TitleStatusHype
DUMP: Automated Distribution-Level Curriculum Learning for RL-based LLM Post-trainingCode1
Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes0
Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making0
Development of a PPO-Reinforcement Learned Walking Tripedal Soft-Legged Robot using SOFACode0
Efficient Implementation of Reinforcement Learning over Homomorphic Encryption0
Towards Optimal Differentially Private Regret Bounds in Linear MDPs0
Spectral Normalization for Lipschitz-Constrained Policies on Learning Humanoid Locomotion0
Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and ChallengesCode0
Deep Distributional Learning with Non-crossing Quantile Network0
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language ModelsCode2
RL-based Control of UAS Subject to Significant Disturbance0
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes0
Boosting Universal LLM Reward Design through the Heuristic Reward Observation Space Evolution0
Echo Chamber: RL Post-training Amplifies Behaviors Learned in PretrainingCode1
VLM-R1: A Stable and Generalizable R1-style Large Vision-Language ModelCode9
Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems0
Perception-R1: Pioneering Perception Policy with Reinforcement LearningCode3
Kimi-VL Technical ReportCode5
Fast Adaptation with Behavioral Foundation Models0
Harnessing Equivariance: Modeling Turbulence with Graph Neural NetworksCode1
Better Decisions through the Right Causal World Model0
Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement LearningCode1
TW-CRL: Time-Weighted Contrastive Reward Learning for Efficient Inverse Reinforcement Learning0
Trust-Region Twisted Policy ImprovementCode0
Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning IncentivizationCode2
xMTF: A Formula-Free Model for Reinforcement-Learning-Based Multi-Task Fusion in Recommender Systems0
Stratified Expert Cloning with Adaptive Selection for User Retention in Large-Scale Recommender Systems0
Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models0
The Role of Environment Access in Agnostic Reinforcement Learning0
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning0
Concise Reasoning via Reinforcement LearningCode1
Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement LearningCode1
Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning0
Impact of Price Inflation on Algorithmic Collusion Through Reinforcement Learning Agents0
OrbitZoo: Multi-Agent Reinforcement Learning Environment for Orbital Dynamics0
Decision SpikeFormer: Spike-Driven Transformer for Decision Making0
Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models0
Improving Mixed-Criticality Scheduling with Reinforcement Learning0
Offline and Distributional Reinforcement Learning for Wireless Communications0
DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world EnvironmentsCode4
Enhanced Penalty-based Bidirectional Reinforcement Learning Algorithms0
Dexterous Manipulation through Imitation Learning: A Survey0
Learning Dual-Arm Coordination for Grasping Large Flat Objects0
Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation SchemeCode2
Adapting World Models with Latent-State Dynamics Residuals0
Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language ModelsCode0
Multi-SWE-bench: A Multilingual Benchmark for Issue ResolvingCode3
MAD: A Magnitude And Direction Policy Parametrization for Stability Constrained Reinforcement LearningCode0
Inference-Time Scaling for Generalist Reward Modeling0
Integrating Human Knowledge Through Action Masking in Reinforcement Learning for Operations Research0
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Benchmark Results

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