@inproceedings{choi-etal-2026-mentor,
title = "{MENTOR}: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation",
author = "Choi, ChangSu and
Song, Hoyun and
Kim, Dongyeon and
Cho, Minkyung and
Jung, WooHyeon and
Park, SunJin and
Bae, NohHyeob and
Yu, Seona and
Lim, KyungTae",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1286/",
pages = "29289--29319",
ISBN = "979-8-89176-459-0",
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), is an off-policy distillation method that suffers from poor out-of-domain (OOD) generalization because it rigidly aligns 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 constraints. To bridge this capacity-driven gap, we propose MENTOR, an on-policy distillation framework that introduces a flexible yet process-aware reward structure. Instead of enforcing rigid replication, MENTOR uses the teacher{'}s reference to guide tool-use behavior, balancing behavioral alignment with downstream performance. Extensive experiments on controlled executable-tool benchmarks demonstrate that MENTOR improves OOD tool-use performance compared to SFT and strict RL baselines. Our findings suggest that within verifiable tool-use environments, flexible tool-use alignment offers a more effective approach than strict trajectory replication for developing adaptable small models."
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<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), is an off-policy distillation method that suffers from poor out-of-domain (OOD) generalization because it rigidly aligns 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 constraints. To bridge this capacity-driven gap, we propose MENTOR, an on-policy distillation framework that introduces a flexible yet process-aware reward structure. Instead of enforcing rigid replication, MENTOR uses the teacher’s reference to guide tool-use behavior, balancing behavioral alignment with downstream performance. Extensive experiments on controlled executable-tool benchmarks demonstrate that MENTOR improves OOD tool-use performance compared to SFT and strict RL baselines. Our findings suggest that within verifiable tool-use environments, flexible tool-use alignment offers a more effective approach than strict trajectory replication for developing adaptable small models.</abstract>
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%0 Conference Proceedings
%T MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation
%A Choi, ChangSu
%A Song, Hoyun
%A Kim, Dongyeon
%A Cho, Minkyung
%A Jung, WooHyeon
%A Park, SunJin
%A Bae, NohHyeob
%A Yu, Seona
%A Lim, KyungTae
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F choi-etal-2026-mentor
%X 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), is an off-policy distillation method that suffers from poor out-of-domain (OOD) generalization because it rigidly aligns 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 constraints. To bridge this capacity-driven gap, we propose MENTOR, an on-policy distillation framework that introduces a flexible yet process-aware reward structure. Instead of enforcing rigid replication, MENTOR uses the teacher’s reference to guide tool-use behavior, balancing behavioral alignment with downstream performance. Extensive experiments on controlled executable-tool benchmarks demonstrate that MENTOR improves OOD tool-use performance compared to SFT and strict RL baselines. Our findings suggest that within verifiable tool-use environments, flexible tool-use alignment offers a more effective approach than strict trajectory replication for developing adaptable small models.
%U https://aclanthology.org/2026.emnlp-main.1286/
%P 29289-29319
Markdown (Informal)
[MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation](https://aclanthology.org/2026.emnlp-main.1286/) (Choi et al., EMNLP 2026)
ACL
- ChangSu Choi, Hoyun Song, Dongyeon Kim, Minkyung Cho, WooHyeon Jung, SunJin Park, NohHyeob Bae, Seona Yu, and KyungTae Lim. 2026. MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 29289–29319, Budapest, Hungary. Association for Computational Linguistics.