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 701–725 of 15113 papers

TitleStatusHype
NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic ScenariosCode1
Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking—0
AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models—0
Option Discovery Using LLM-guided Semantic Hierarchical Reinforcement Learning—0
Evolutionary Policy Optimization—0
Mining-Gym: A Configurable RL Benchmarking Environment for Truck Dispatch SchedulingCode0
Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer LearningCode0
RLCAD: Reinforcement Learning Training Gym for Revolution Involved CAD Command Sequence Generation—0
MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the MetaverseCode3
Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning—0
Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-TrainingCode1
SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the WildCode7
Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation—0
Sample-Efficient Reinforcement Learning of Koopman eNMPC—0
Adaptive Multi-Fidelity Reinforcement Learning for Variance Reduction in Engineering Design Optimization—0
Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization—0
ViVa: Video-Trained Value Functions for Guiding Online RL from Diverse Data—0
Surrogate Learning in Meta-Black-Box Optimization: A Preliminary StudyCode2
Optimizing Navigation And Chemical Application in Precision Agriculture With Deep Reinforcement Learning And Conditional Action Tree—0
Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots—0
A Roadmap Towards Improving Multi-Agent Reinforcement Learning With Causal Discovery And Inference—0
ComfyGPT: A Self-Optimizing Multi-Agent System for Comprehensive ComfyUI Workflow Generation—0
Causally Aligned Curriculum Learning—0
Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management ProblemCode0
OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-ImprovementCode2
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Benchmark Results

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