EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning Paper • 2608.06197 • Published 17 days ago • 45
AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning Paper • 2608.05987 • Published 17 days ago • 100
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning Paper • 2608.06197 • Published 17 days ago • 45
AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning Paper • 2608.05987 • Published 17 days ago • 100
Distill Where You Fail: Recovering Learning Signals of Negative RL-Groups from Adaptive Teacher Guidance Paper • 2608.00782 • Published 22 days ago • 16
Distill Where You Fail: Recovering Learning Signals of Negative RL-Groups from Adaptive Teacher Guidance Paper • 2608.00782 • Published 22 days ago • 16
Distill Where You Fail: Recovering Learning Signals of Negative RL-Groups from Adaptive Teacher Guidance Paper • 2608.00782 • Published 22 days ago • 16
VAD: Attributing Visual Evidence for Target Reconstruction in Multimodal On-Policy Distillation Paper • 2607.28590 • Published 24 days ago • 46
VAD: Attributing Visual Evidence for Target Reconstruction in Multimodal On-Policy Distillation Paper • 2607.28590 • Published 24 days ago • 46
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution Paper • 2607.26784 • Published 25 days ago • 30
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution Paper • 2607.26784 • Published 25 days ago • 30
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution Paper • 2607.26784 • Published 25 days ago • 30
Pass the Baton: Trajectory-Relayed On-Policy Distillation Paper • 2607.26057 • Published 26 days ago • 33
SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Paper • 2607.14777 • Published Jul 16 • 106
SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Paper • 2607.14777 • Published Jul 16 • 106
OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning Paper • 2606.26790 • Published Jun 25 • 57
OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning Paper • 2606.26790 • Published Jun 25 • 57
MobileForge: Annotation-Free Adaptation for Mobile GUI Agents with Hierarchical Feedback-Guided Policy Optimization Paper • 2606.19930 • Published Jun 18 • 43
MemGUI-Agent: An End-to-End Long-Horizon Mobile GUI Agent with Proactive Context Management Paper • 2606.19926 • Published Jun 18 • 42