Drop is the
typed-deletion arm of a four-arm controlled study of training-data interventions against
over-reflection in web-search agents. It is a supervised fine-tune of
Qwen/Qwen3.5-35B-A3B on the same in-house web-search agent trajectory corpus as the
Asis baseline, except that
whole trajectories exhibiting pathological over-reflection types are deleted before training. Deletion is
typed: trajectories are labeled against an A–G taxonomy of over-reflection behaviors, and the pathological types are removed in their entirety (no editing of surviving trajectories).
Full-parameter supervised fine-tuning on a Megatron-based distributed training stack. Identical recipe to the sibling arms; only the data intervention differs.
The study's
A–G taxonomy of over-reflection behaviors categorizes pathological patterns in browsing-agent trajectories — e.g., post-answer verification loops (the agent keeps searching after it has already stated the answer), redundant re-search after sufficient evidence has been gathered, and ungrounded reasoning not supported by retrieved content. In this arm, any trajectory carrying a pathological type is
removed entirely. This is the coarsest intervention class in the study: it trades corpus size for corpus cleanliness, in contrast to the surgical editing of the
Repair arm and the outcome-based filtering of the
Correct arm.
Research artifact. Intended for studying how removing pathological demonstrations (as opposed to editing them, or filtering by outcome) affects over-reflection, stopping behavior, and search efficiency in tool-using web agents. It expects a browsing-agent tool surface compatible with its training format (function-calling tools for web search, page opening, and in-page lookup).
