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 2401–2425 of 15113 papers

TitleStatusHype
Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments—0
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback—0
Data-assimilated model-informed reinforcement learning—0
KDRL: Post-Training Reasoning LLMs via Unified Knowledge Distillation and Reinforcement Learning—0
SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning—0
Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains—0
Trajectory First: A Curriculum for Discovering Diverse Policies—0
A Reinforcement Learning Approach for RIS-aided Fair Communications—0
DriveMind: A Dual-VLM based Reinforcement Learning Framework for Autonomous Driving—0
MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoning—0
Reinforcement Learning for Hanabi—0
ARIA: Training Language Agents with Intention-Driven Reward Aggregation—0
Balancing Profit and Fairness in Risk-Based Pricing Markets—0
Reason-SVG: Hybrid Reward RL for Aha-Moments in Vector Graphics Generation—0
How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning—0
Mixed-R1: Unified Reward Perspective For Reasoning Capability in Multimodal Large Language ModelsCode0
ROAD: Responsibility-Oriented Reward Design for Reinforcement Learning in Autonomous Driving—0
Pangu DeepDiver: Adaptive Search Intensity Scaling via Open-Web Reinforcement Learning—0
Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control—0
MOFGPT: Generative Design of Metal-Organic Frameworks using Language ModelsCode0
Contextual Integrity in LLMs via Reasoning and Reinforcement Learning—0
DIP-R1: Deep Inspection and Perception with RL Looking Through and Understanding Complex Scenes—0
LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Trainin—0
Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Control—0
Hybrid Cross-domain Robust Reinforcement Learning—0
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Benchmark Results

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