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 2551–2575 of 15113 papers

TitleStatusHype
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning—0
DGRO: Enhancing LLM Reasoning via Exploration-Exploitation Control and Reward Variance Management—0
Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning—0
Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach—0
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis—0
Counterfactual Explanations for Continuous Action Reinforcement LearningCode0
Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs—0
ToTRL: Unlock LLM Tree-of-Thoughts Reasoning Potential through Puzzles Solving—0
Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization—0
Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning—0
Benchmarking MOEAs for solving continuous multi-objective RL problemsCode0
UIShift: Enhancing VLM-based GUI Agents through Self-supervised Reinforcement Learning—0
AbFlowNet: Optimizing Antibody-Antigen Binding Energy via Diffusion-GFlowNet Fusion—0
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios—0
A Finite-Sample Analysis of Distributionally Robust Average-Reward Reinforcement Learning—0
Observe-R1: Unlocking Reasoning Abilities of MLLMs with Dynamic Progressive Reinforcement LearningCode0
Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents—0
Resolving Latency and Inventory Risk in Market Making with Reinforcement Learning—0
Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling—0
Online Iterative Self-Alignment for Radiology Report Generation—0
Retrospex: Language Agent Meets Offline Reinforcement Learning CriticCode0
AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning—0
Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning—0
J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge—0
An agentic system with reinforcement-learned subsystem improvements for parsing form-like documentsCode0
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Benchmark Results

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