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 701–750 of 15113 papers

TitleStatusHype
Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking—0
NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic ScenariosCode1
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
Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning—0
SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the WildCode7
MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the MetaverseCode3
Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-TrainingCode1
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
Optimizing Navigation And Chemical Application in Precision Agriculture With Deep Reinforcement Learning And Conditional Action Tree—0
Surrogate Learning in Meta-Black-Box Optimization: A Preliminary StudyCode2
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
OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-ImprovementCode2
Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management ProblemCode0
Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent—0
Towards Automated Semantic Interpretability in Reinforcement Learning via Vision-Language Models—0
OThink-MR1: Stimulating multimodal generalized reasoning capabilities via dynamic reinforcement learning—0
Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-TuningCode2
Reinforcement Learning-based Heuristics to Guide Domain-Independent Dynamic ProgrammingCode0
Grammar and Gameplay-aligned RL for Game Description Generation with LLMs—0
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn'tCode3
RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models—0
Stop Overthinking: A Survey on Efficient Reasoning for Large Language ModelsCode4
Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement LearningCode4
UAS Visual Navigation in Large and Unseen Environments via a Meta Agent—0
Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging—0
Behaviour Discovery and Attribution for Explainable Reinforcement Learning—0
Reinforcement Learning Environment with LLM-Controlled Adversary in D&D 5th Edition Combat—0
Good Actions Succeed, Bad Actions Generalize: A Case Study on Why RL Generalizes Better—0
Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis—0
DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning—0
LogLLaMA: Transformer-based log anomaly detection with LLaMA—0
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities—0
Reward Training Wheels: Adaptive Auxiliary Rewards for Robotics Reinforcement Learning—0
Neural Lyapunov Function Approximation with Self-Supervised Reinforcement LearningCode0
Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor controlCode2
Cosmos-Reason1: From Physical Common Sense To Embodied ReasoningCode4
CTSAC: Curriculum-Based Transformer Soft Actor-Critic for Goal-Oriented Robot Exploration—0
Pauli Network Circuit Synthesis with Reinforcement Learning—0
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Benchmark Results

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