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

Hugging Face Daily Papers Papers
tool-call boundary-drift multi-teacher knowledge-distillation agentic calibration language-models

Summary

This paper diagnoses and proposes SoftClamp, a calibration method that reduces tool-call boundary drift in multi-teacher on-policy distillation for agentic language models, decreasing over-calling while maintaining accuracy.

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 knowledge distillation improves tool-call recall but also moves the model toward over-calling, where it calls tools on examples that should be answered directly. Aggregate explanations are insufficient: tool-call samples do not receive more token exposure, and full-sequence per-token divergence is not larger for the tool-call teacher. We instead analyze behavior leverage imbalance: local token-level signals at mode- entry and structural positions, such as <tool_call> and function names, can have disproportionate control over the global generation mode. We propose Soft Clamp, a per-token divergence calibration method that dynamically compresses extreme token-level Jensen-Shannon divergence while preserving nonzero gradients. On APIGen-MT, Soft Clamp reduces over-calling from 13.7% to 9.0% relative to vanilla GKD while matching its decision accuracy. In a BFCL multi-turn diagnostic, it also lowers tool-call loops and repeated calls among GKD variants. These results suggest that multi-teacher OPD should monitor where teacher signals act, not only how large they are in aggregate.
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Paper page - Diagnosing and Calibrating Tool-Call Boundary Drift in Multi-Teacher On-Policy Distillation

Source: https://huggingface.co/papers/2607.07050 Published on Jul 15

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Submitted byhttps://huggingface.co/jiabin

shenon Jul 21

Abstract

Agenticlanguagemodelsmustlearnwhentocalltools,whentoconsumetoolresponses,andwhentoanswerdirectly.Thismakesmulti-teacheron-policydistillationanaturaltrainingstrategy:oneteachercanspecializeintoolcalls,anotherindirectresponses,andthestudentcanlearnfrombothonitsowngenerateddistribution.Weshowthatthisstrategycaninduceabehaviorshiftthatisinvisiblefromaggregatelossesalone.Inatwo-teachertool-usesetting,vanillageneralizedknowledgedistillationimprovestool-callrecallbutalsomovesthemodeltowardover-calling,whereitcallstoolsonexamplesthatshouldbeanswereddirectly.Aggregateexplanationsareinsufficient:tool-callsamplesdonotreceivemoretokenexposure,andfull-sequenceper-tokendivergenceisnotlargerforthetool-callteacher.Weinsteadanalyzebehaviorleverageimbalance:localtoken-levelsignalsatmode-entryandstructuralpositions,suchas<tool_call>andfunctionnames,canhavedisproportionatecontrolovertheglobalgenerationmode.WeproposeSoftClamp,aper-tokendivergencecalibrationmethodthatdynamicallycompressesextremetoken-levelJensen-Shannondivergencewhilepreservingnonzerogradients.OnAPIGen-MT,SoftClampreducesover-callingfrom13.7%to9.0%relativetovanillaGKDwhilematchingitsdecisionaccuracy.InaBFCLmulti-turndiagnostic,italsolowerstool-callloopsandrepeatedcallsamongGKDvariants.Theseresultssuggestthatmulti-teacherOPDshouldmonitorwhereteachersignalsact,notonlyhowlargetheyareinaggregate.

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This paper identifies a failure mode in multi-teacher on-policy distillation for agentic language models where the student becomes over-reliant on tool-calling, and proposes Soft Clamp, a per-token divergence calibration method that reduces over-calling without sacrificing accuracy.