arXiv:2607.07050v1 Announce Type: new Abstract: 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 mo
The rapid development and deployment of agentic language models necessitate more effective and nuanced training strategies for complex behaviors like tool use.
Understanding and addressing 'behavior leverage imbalance' in multi-teacher distillation is crucial for developing robust, reliable, and more autonomous AI agents, impacting their deployment across various industries.
The focus shifts from merely improving aggregate losses in AI training to deeply analyzing and correcting specific behavioral biases introduced by multi-teacher systems, leading to more performant and trustworthy agents.
- · AI Agent Developers
- · Enterprises Adopting Agentic AI
- · AI Research Labs
- · AI Models trained sub-optimally
- · Developers unaware of distillation biases
Improved tool-use capabilities and reliability of agentic AI models.
Faster adoption of AI agents in complex, multi-step workflows across various sectors.
Enhanced trust in autonomous AI systems, potentially accelerating the development of highly specialized and self-correcting agents.
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Read at arXiv cs.CL