MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation

2025-10-21 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

MENTOR is a novel reinforcement learning approach designed to distill the tool-use capabilities of large language models (LLMs) into smaller language models (SLMs). This method addresses the limitations of traditional supervised fine-tuning (SFT), which often results in poor out-of-domain (OOD) generalization due to its rigid adherence to static teacher trajectories. While reinforcement learning (RL) offers an alternative, SLMs face a dilemma between sparse outcome rewards and overly restrictive trajectory matching. MENTOR introduces a flexible, process-aware reward structure that leverages the teacher's reference to guide tool-use behavior, effectively balancing behavioral alignment with overall downstream performance. Extensive experiments conducted on controlled executable-tool benchmarks demonstrate that MENTOR significantly improves OOD tool-use performance compared to both SFT and strict RL baselines. The findings suggest that flexible tool-use alignment is more effective than strict trajectory replication for developing adaptable small models in verifiable tool-use environments.

Key takeaway

For Machine Learning Engineers distilling LLM tool-use into smaller models, you should reconsider strict supervised fine-tuning. MENTOR demonstrates that flexible, process-aware reinforcement learning rewards significantly improve out-of-domain generalization for SLMs. Prioritize guiding tool-use behavior with teacher references. This is more effective than rigid trajectory replication. It leads to adaptable, robust small models for verifiable environments, enhancing practical SLM applications.

Key insights

Flexible, process-aware reward structures in RL effectively distill LLM tool-use into SLMs, improving out-of-domain generalization over rigid methods.

Principles

• Flexible tool-use alignment enhances SLM adaptability.
• Rigid trajectory matching limits OOD generalization.
• Balance behavioral alignment with downstream performance.

Method

MENTOR uses a flexible, process-aware reward structure in RL. It guides SLM tool-use behavior by referencing teacher trajectories, balancing alignment with downstream performance, rather than enforcing strict replication.

In practice

• Apply flexible RL rewards for SLM tool-use.
• Prioritize OOD generalization in distillation.
• Use teacher references for behavioral guidance.

Topics

• MENTOR
• Reinforcement Learning
• Tool-Use Distillation
• Large Language Models
• Small Language Models
• Out-of-Domain Generalization

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.