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 426–450 of 15113 papers

TitleStatusHype
Group-in-Group Policy Optimization for LLM Agent TrainingCode5
Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions—0
Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLMCode0
Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RLCode0
Developing and Integrating Trust Modeling into Multi-Objective Reinforcement Learning for Intelligent Agricultural Management—0
Attention-Based Reward Shaping for Sparse and Delayed RewardsCode0
Reinforcement Learning for AMR Charging Decisions: The Impact of Reward and Action Space Design—0
ImagineBench: Evaluating Reinforcement Learning with Large Language Model RolloutsCode1
Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps—0
Reinforcing the Diffusion Chain of Lateral Thought with Diffusion Language Models—0
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation—0
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change—0
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
Beyond 'Aha!': Toward Systematic Meta-Abilities Alignment in Large Reasoning ModelsCode2
Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systems—0
Reinforcement Learning for Individual Optimal Policy from Heterogeneous Data—0
TensorRL-QAS: Reinforcement learning with tensor networks for scalable quantum architecture search—0
CEC-Zero: Chinese Error Correction Solution Based on LLM—0
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning—0
Generalization in Monitored Markov Decision Processes (Mon-MDPs)—0
Adaptive Diffusion Policy Optimization for Robotic ManipulationCode0
DSADF: Thinking Fast and Slow for Decision Making—0
Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning—0
Preference Optimization for Combinatorial Optimization Problems—0
Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning—0
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Benchmark Results

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