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 651–675 of 15113 papers

TitleStatusHype
Multi-SWE-bench: A Multilingual Benchmark for Issue ResolvingCode3
Inference-Time Scaling for Generalist Reward Modeling—0
GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical ReasoningCode1
De Novo Molecular Design Enabled by Direct Preference Optimization and Curriculum Learning—0
ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement LearningCode1
Probabilistic Curriculum Learning for Goal-Based Reinforcement Learning—0
Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?Code1
How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study—0
Probabilistically safe and efficient model-based Reinforcement LearningCode1
Grounding Multimodal LLMs to Embodied Agents that Ask for Help with Reinforcement Learning—0
MPCritic: A plug-and-play MPC architecture for reinforcement learningCode1
Value Iteration for Learning Concurrently Executable Robotic Control TasksCode0
JudgeLRM: Large Reasoning Models as a Judge—0
Nuclear Microreactor Control with Deep Reinforcement Learning—0
A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective—0
HACTS: a Human-As-Copilot Teleoperation System for Robot Learning—0
Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning—0
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1Code2
Noise-based reward-modulated learning—0
Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning—0
Reinforcement Learning for Safe Autonomous Two Device Navigation of Cerebral Vessels in Mechanical Thrombectomy—0
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection—0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior—0
Reinforcement Learning-based Token Pruning in Vision Transformers: A Markov Game ApproachCode0
Reinforcement Learning for Active Matter—0
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Benchmark Results

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