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 501–550 of 15113 papers

TitleStatusHype
R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement LearningCode3
EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-TuningCode0
Automated Hybrid Reward Scheduling via Large Language Models for Robotic Skill Learning—0
Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study—0
Prompt-responsive Object Retrieval with Memory-augmented Student-Teacher Learning—0
A Generalised and Adaptable Reinforcement Learning Stopping MethodCode0
World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks—0
Analytic Energy-Guided Policy Optimization for Offline Reinforcement Learning—0
Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion—0
Directly Forecasting Belief for Reinforcement Learning with DelaysCode0
A General Approach of Automated Environment Design for Learning the Optimal Power Flow—0
Implicit Neural-Representation Learning for Elastic Deformable-Object Manipulations—0
MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion—0
SmallPlan: Leverage Small Language Models for Sequential Path Planning with Simulation-Powered, LLM-Guided DistillationCode0
T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoTCode4
Leveraging Partial SMILES Validation Scheme for Enhanced Drug Design in Reinforcement Learning Frameworks—0
Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models—0
Enhancing New-item Fairness in Dynamic Recommender SystemsCode0
Phi-4-reasoning Technical Report—0
Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math—0
Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning—0
A Survey on GUI Agents with Foundation Models Enhanced by Reinforcement Learning—0
PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations—0
Token-Efficient RL for LLM Reasoning—0
Reinforcement Learning-Based Heterogeneous Multi-Task Optimization in Semantic Broadcast Communications—0
AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning—0
Rulebook: bringing co-routines to reinforcement learning environmentsCode2
Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving—0
An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination—0
LLMs for Engineering: Teaching Models to Design High Powered Rockets—0
BQSched: A Non-intrusive Scheduler for Batch Concurrent Queries via Reinforcement LearningCode0
Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson DiseaseCode1
Explainable AI for UAV Mobility Management: A Deep Q-Network Approach for Handover Minimization—0
Depth-Constrained ASV Navigation with Deep RL and Limited Sensing—0
LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN—0
CaRL: Learning Scalable Planning Policies with Simple RewardsCode2
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement LearningCode7
Training Large Language Models to Reason via EM Policy Gradient—0
SAPO-RL: Sequential Actuator Placement Optimization for Fuselage Assembly via Reinforcement Learning—0
Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control—0
Monte Carlo Planning with Large Language Model for Text-Based Game Agents—0
Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator—0
Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows—0
Natural Policy Gradient for Average Reward Non-Stationary RL—0
Hybrid Reinforcement Learning and Model Predictive Control for Adaptive Control of Hydrogen-Diesel Dual-Fuel Combustion—0
Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints—0
TTRL: Test-Time Reinforcement LearningCode7
Tina: Tiny Reasoning Models via LoRACode3
SLiM-Gym: Reinforcement Learning for Population Genetics—0
Policy-Based Radiative Transfer: Solving the 2-Level Atom Non-LTE Problem using Soft Actor-Critic Reinforcement Learning—0
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Benchmark Results

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