arXiv AI
ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillation
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3d ago
ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillation
Iterative self-distillation enables LLM agents to learn from successive deployments, offering a path toward recursive self-improvement (RSI). Yet our experiments with existing methods reveal a collaps...
arXiv AI
Jun 11
HERO: Hindsight-Enhanced Reflection from Environment Observations for Agentic Self-Distillation
arXiv:2606. 11559v1 Announce Type: new Abstract: Reinforcement learning typically improves multi-turn agent capabilities through the terminal outcome of the trajectories, which makes it difficult to determine credit assignments for each intermediate turns.
arXiv AI
2d ago
ComputerSD: Online Self-Distillation from Real-Time Feedback for Computer-Use Agents
ComputerSD is an online self‑distillation method for computer‑use agents that leverages real‑time feedback from executed GUI transitions. It uses a fine‑tuned GUI analyzer to generate guidance and a step‑level value score after each action, combining token‑level OPSD with trajectory‑level GRPO in an asynchronous training framework. On the OSWorld‑Verified benchmark, ComputerSD improves performance over outcome‑only GRPO by 1.9 and 4.1 percentage points on Qwen3‑VL‑8B‑Thinking and EvoCUA‑8B backbones, and shows strong generalizability in out‑of‑distribution tests.
arXiv Machine Learning
Aug 14
Latent On-Policy Self-Distillation
arXiv:2608. 13040v1 Announce Type: new Abstract: Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI.
Hugging Face Trending Papers
3d ago
ComputerSD: Online Self-Distillation from Real-Time Feedback for Computer-Use Agents
Online training enables computer-use agents (CUAs) to improve through interaction with executable environments. However, existing methods primarily rely on sparse outcome rewards, which provide no sup...
Hugging Face Trending Papers
Aug 13
Latent On-Policy Self-Distillation
Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI. On-policy self-distillation (OPSD) offers an effective pathway by using a privileged self-teacher to provide dense supervision on the student's own trajectories; however, existing methods still rely heavily on designer-specified privileged artifacts (e.