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 501–525 of 15113 papers

TitleStatusHype
RM-R1: Reward Modeling as ReasoningCode2
Automated Hybrid Reward Scheduling via Large Language Models for Robotic Skill Learning—0
EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-TuningCode0
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
Analytic Energy-Guided Policy Optimization for Offline Reinforcement Learning—0
World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks—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
MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion—0
Implicit Neural-Representation Learning for Elastic Deformable-Object Manipulations—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-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math—0
Phi-4-reasoning Technical Report—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
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Benchmark Results

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