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 401–425 of 15113 papers

TitleStatusHype
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
Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement LearningCode3
AbFlowNet: Optimizing Antibody-Antigen Binding Energy via Diffusion-GFlowNet Fusion—0
Observe-R1: Unlocking Reasoning Abilities of MLLMs with Dynamic Progressive Reinforcement LearningCode0
CPGD: Toward Stable Rule-based Reinforcement Learning for Language ModelsCode4
VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-TuningCode2
UIShift: Enhancing VLM-based GUI Agents through Self-supervised Reinforcement Learning—0
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise RewardCode3
AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning—0
Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning—0
Retrospex: Language Agent Meets Offline Reinforcement Learning CriticCode0
J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge—0
Online Iterative Self-Alignment for Radiology Report Generation—0
Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling—0
Reinforcement Learning Finetunes Small Subnetworks in Large Language Models—0
Time-R1: Towards Comprehensive Temporal Reasoning in LLMsCode0
Spectral Policy Optimization: Coloring your Incorrect Reasoning in GRPO—0
An agentic system with reinforcement-learned subsystem improvements for parsing form-like documentsCode0
Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents—0
Reinforcement Learning for AMR Charging Decisions: The Impact of Reward and Action Space Design—0
Is PRM Necessary? Problem-Solving RL Implicitly Induces PRM Capability in LLMs—0
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes—0
ShiQ: Bringing back Bellman to LLMs—0
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Benchmark Results

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