MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation
arXiv:2510.18383v3 Announce Type: replace Abstract: Distilling the tool-use capabilities of large language models (LLMs) into small language models (SLMs) is essential for their practical application. The predominant approach, supervised fine-tuning (SFT), suffers from poor out-of-domain (OOD) generalization due to its rigid alignment with static teacher trajectories. While reinforcement learning (RL) offers an alternative, the capacity limitations of SLMs pose a severe dilemma: sparse outcome rewards provide insufficient guidance, whereas strict trajectory matching imposes overly restrictive
The continuous drive to optimize and decentralize AI deployment necessitates efficient methods for transferring powerful AI capabilities to smaller, more practical models.
This research provides a more effective pathway for making advanced AI tool-use capabilities accessible in resource-constrained environments, broadening the practical application of AI.
The ability to distill complex LLM tool-use into SLMs more effectively will accelerate the development and deployment of specialized, efficient AI agents.
- · Small Language Model developers
- · Edge computing platforms
- · Enterprises adopting custom AI agents
- · AI agents sector
- · Companies reliant solely on massive LLM infrastructure
Improved tool-use capabilities of SLMs will lead to more robust and specialized AI applications.
Increased adoption of smaller, more power-efficient AI models will reduce computational costs and energy demands for certain tasks.
A wider accessibility of advanced AI functionalities could foster a new wave of innovation in AI product development and service automation.
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Read at arXiv cs.CL