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 2351–2400 of 15113 papers

TitleStatusHype
Eliciting Reasoning in Language Models with Cognitive Tools—0
PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier—0
Magistral—0
Shapley Machine: A Game-Theoretic Framework for N-Agent Ad Hoc TeamworkCode0
Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy RegularizationCode0
Automatic Treatment Planning using Reinforcement Learning for High-dose-rate Prostate Brachytherapy—0
A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications—0
Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flows—0
Bridging Continuous-time LQR and Reinforcement Learning via Gradient Flow of the Bellman Error—0
Optimal Operating Strategy for PV-BESS Households: Balancing Self-Consumption and Self-Sufficiency—0
Policy-Based Trajectory Clustering in Offline Reinforcement Learning—0
Exploration by Random Reward Perturbation—0
Reinforcement Learning Teachers of Test Time Scaling—0
TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy OptimizationCode0
Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)—0
MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning—0
How to Provably Improve Return Conditioned Supervised Learning?—0
Offline RL with Smooth OOD Generalization in Convex Hull and its NeighborhoodCode0
DeepForm: Reasoning Large Language Model for Communication System Formulation—0
Through the Valley: Path to Effective Long CoT Training for Small Language Models—0
DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO—0
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking—0
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—0
Reinforcement Pre-Training—0
Bingo: Boosting Efficient Reasoning of LLMs via Dynamic and Significance-based Reinforcement Learning—0
LUCIFER: Language Understanding and Context-Infused Framework for Exploration and Behavior Refinement—0
QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine—0
Reliable Critics: Monotonic Improvement and Convergence Guarantees for Reinforcement Learning—0
CARoL: Context-aware Adaptation for Robot Learning—0
Learning to Clarify by Reinforcement Learning Through Reward-Weighted Fine-Tuning—0
Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression—0
On the Generalization of Data-Assisted Control in port-Hamiltonian Systems (DAC-pH)—0
Prompting Wireless Networks: Reinforced In-Context Learning for Power Control—0
CodeContests+: High-Quality Test Case Generation for Competitive Programming—0
Towards Infant Sleep-Optimized Driving: Synergizing Wearable and Vehicle Sensing in Intelligent Cruise Control—0
Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement LearningCode0
On the Mechanism of Reasoning Pattern Selection in Reinforcement Learning for Language Models—0
Safe Planning and Policy Optimization via World Model Learning—0
Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models—0
Dissecting Long Reasoning Models: An Empirical StudyCode0
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning—0
Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning—0
Learning-at-Criticality in Large Language Models for Quantum Field Theory and Beyond—0
A Lyapunov Drift-Plus-Penalty Method Tailored for Reinforcement Learning with Queue Stability—0
CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design—0
SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL—0
Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning—0
Latent Guided Sampling for Combinatorial OptimizationCode0
Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments—0
Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games—0
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Benchmark Results

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