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

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Showing 451–475 of 15113 papers

TitleStatusHype
Probabilistically safe and efficient model-based Reinforcement LearningCode1
ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented GenerationCode1
NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic ScenariosCode1
Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-TrainingCode1
Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical InvestigationCode1
TERL: Large-Scale Multi-Target Encirclement Using Transformer-Enhanced Reinforcement LearningCode1
Regulatory DNA sequence Design with Reinforcement LearningCode1
VisRL: Intention-Driven Visual Perception via Reinforced ReasoningCode1
Reinforcement learning with combinatorial actions for coupled restless banditsCode1
Discrete Codebook World Models for Continuous ControlCode1
Distilling Reinforcement Learning Algorithms for In-Context Model-Based PlanningCode1
VEM: Environment-Free Exploration for Training GUI Agent with Value Environment ModelCode1
Generating π-Functional Molecules Using STGG+ with Active LearningCode1
Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?Code1
Learning to Sample Effective and Diverse Prompts for Text-to-Image GenerationCode1
Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing ProblemsCode1
DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM GuardrailsCode1
Analytical Lyapunov Function Discovery: An RL-based Generative ApproachCode1
GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic EnvironmentsCode1
SHARPIE: A Modular Framework for Reinforcement Learning and Human-AI Interaction ExperimentsCode1
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic LearningCode1
xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM JailbreakingCode1
An Attentive Graph Agent for Topology-Adaptive Cyber DefenceCode1
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingCode1
Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning PoliciesCode1
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Benchmark Results

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