标题为英语DENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-Refinement
Online agent deployments produce abundant execution traces, while task-specific verification and expert annotation are costly to scale. We study how to distill these traces into reusable feedback without post-hoc outcome labels, drawing on their evidence of local progress,…
AI 摘要：要点
- Online agent deployments produce abundant execution traces, while task-specific verification and expert annotation are costly to scale
- We study how to distill these traces into reusable feedback without post-hoc outcome labels, drawing on their evidence of local progress,…
DENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-Refinement
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