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 451–500 of 15113 papers

TitleStatusHype
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement LearningCode3
Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning—0
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles—0
The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning—0
DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic RewardCode0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Kalman Filter Enhanced GRPO for Reinforcement Learning-Based Language Model ReasoningCode1
Measuring General Intelligence with Generated GamesCode1
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem SolvingCode2
INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning—0
Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning—0
DanceGRPO: Unleashing GRPO on Visual GenerationCode5
Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains—0
Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search AgentCode2
Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems—0
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots—0
Learning Value of Information towards Joint Communication and Control in 6G V2X—0
Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control—0
X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real—0
LineFlow: A Framework to Learn Active Control of Production LinesCode0
REFINE-AF: A Task-Agnostic Framework to Align Language Models via Self-Generated Instructions using Reinforcement Learning from Automated Feedback—0
Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving—0
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach—0
Interaction-Aware Parameter Privacy-Preserving Data Sharing in Coupled Systems via Particle Filter Reinforcement Learning—0
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients—0
Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning—0
Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach—0
Reinforcement Learning for Game-Theoretic Resource Allocation on Graphs—0
On Corruption-Robustness in Performative Reinforcement Learning—0
RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles—0
Taming OOD Actions for Offline Reinforcement Learning: An Advantage-Based Approach—0
USPR: Learning a Unified Solver for Profiled RoutingCode0
Flow-GRPO: Training Flow Matching Models via Online RLCode7
Enhancing Reinforcement Learning for the Floorplanning of Analog ICs with Beam Search—0
Multi-agent Embodied AI: Advances and Future Directions—0
Large Language Models are Autonomous Cyber DefendersCode0
Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers—0
ZeroSearch: Incentivize the Search Capability of LLMs without SearchingCode5
Extending a Quantum Reinforcement Learning Exploration Policy with Flags to Connect Four—0
Fight Fire with Fire: Defending Against Malicious RL Fine-Tuning via Reward Neutralization—0
Risk-sensitive Reinforcement Learning Based on Convex Scoring Functions—0
Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems—0
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
Actor-Critics Can Achieve Optimal Sample Efficiency—0
The Steganographic Potentials of Language Models—0
Online Phase Estimation of Human Oscillatory Motions using Deep Learning—0
R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement LearningCode3
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Benchmark Results

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