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Evidence record 12310 · automatically gathered

Diagnosing and Calibrating Tool-Call Boundary Drift in Multi-Teacher On-Policy Distillation

Agentic language models must learn when to call tools, when to consume tool responses, and when to answer directly. This makes multi-teacher on-policy distillation a natural training strategy: one teacher can specialize in tool calls, another in direct responses, and the student can learn from both on its own generated distribution. We show that this strategy can induce a behavior shift that is invisible from aggregate losses alone. In a two-teacher tool-use setting, vanilla generalized knowledg

Record details

Published: 14 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Regulation · Healthcare · Children & education · Agents & autonomy
Retrieved: 22 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Regulation · Shared topic: Healthcare

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Shared topic: Regulation · Shared topic: Children & education

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Shared topic: Regulation · Shared topic: Healthcare

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Shared topic: Regulation · Shared topic: Healthcare

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How to cite this record

ethics.ai (14 July 2026), “Diagnosing and Calibrating Tool-Call Boundary Drift in Multi-Teacher On-Policy Distillation,” evidence record 12310, https://ethics.ai/record/12310 (originally published by HuggingFace Daily Papers).

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