@inproceedings{ye-etal-2025-disentangling,
title = "Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning",
author = "Ye, Ziang and
Zhang, Zhenru and
Zhang, Yang and
Ma, Jianxin and
Lin, Junyang and
Feng, Fuli",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1078/",
doi = "10.18653/v1/2025.findings-acl.1078",
pages = "20939--20957",
ISBN = "979-8-89176-256-5",
abstract = "When using agent-task datasets to enhance agent capabilities for Large Language Models (LLMs), current methodologies often treat all tokens within a sample equally. However, we argue that tokens serving different roles{---}specifically, reasoning tokens versus boilerplate tokens (e.g., those governing output format){---}differ significantly in importance and learning complexity, necessitating their disentanglement and distinct treatment. To address this, we propose a novel Shuffle-Aware Discriminator (SHAD) for adaptive token discrimination. SHAD classifies tokens by exploiting predictability differences observed after shuffling input-output combinations across samples: boilerplate tokens, due to their repetitive nature among samples, maintain predictability, whereas reasoning tokens do not. Using SHAD, we propose the Reasoning-highlighted Fine-Tuning (RFT) method, which adaptively emphasizes reasoning tokens during fine-tuning, yielding notable performance gains over common Supervised Fine-Tuning (SFT)."
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<abstract>When using agent-task datasets to enhance agent capabilities for Large Language Models (LLMs), current methodologies often treat all tokens within a sample equally. However, we argue that tokens serving different roles—specifically, reasoning tokens versus boilerplate tokens (e.g., those governing output format)—differ significantly in importance and learning complexity, necessitating their disentanglement and distinct treatment. To address this, we propose a novel Shuffle-Aware Discriminator (SHAD) for adaptive token discrimination. SHAD classifies tokens by exploiting predictability differences observed after shuffling input-output combinations across samples: boilerplate tokens, due to their repetitive nature among samples, maintain predictability, whereas reasoning tokens do not. Using SHAD, we propose the Reasoning-highlighted Fine-Tuning (RFT) method, which adaptively emphasizes reasoning tokens during fine-tuning, yielding notable performance gains over common Supervised Fine-Tuning (SFT).</abstract>
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%0 Conference Proceedings
%T Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning
%A Ye, Ziang
%A Zhang, Zhenru
%A Zhang, Yang
%A Ma, Jianxin
%A Lin, Junyang
%A Feng, Fuli
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F ye-etal-2025-disentangling
%X When using agent-task datasets to enhance agent capabilities for Large Language Models (LLMs), current methodologies often treat all tokens within a sample equally. However, we argue that tokens serving different roles—specifically, reasoning tokens versus boilerplate tokens (e.g., those governing output format)—differ significantly in importance and learning complexity, necessitating their disentanglement and distinct treatment. To address this, we propose a novel Shuffle-Aware Discriminator (SHAD) for adaptive token discrimination. SHAD classifies tokens by exploiting predictability differences observed after shuffling input-output combinations across samples: boilerplate tokens, due to their repetitive nature among samples, maintain predictability, whereas reasoning tokens do not. Using SHAD, we propose the Reasoning-highlighted Fine-Tuning (RFT) method, which adaptively emphasizes reasoning tokens during fine-tuning, yielding notable performance gains over common Supervised Fine-Tuning (SFT).
%R 10.18653/v1/2025.findings-acl.1078
%U https://aclanthology.org/2025.findings-acl.1078/
%U https://doi.org/10.18653/v1/2025.findings-acl.1078
%P 20939-20957
Markdown (Informal)
[Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning](https://aclanthology.org/2025.findings-acl.1078/) (Ye et al., Findings 2025)
ACL