@inproceedings{shin-etal-2026-dynamixsft,
title = "{D}ynamix{SFT}: Dynamic Mixture Optimization of Instruction Tuning Collections",
author = "Shin, Haebin and
Ji, Lei and
Liu, Xiao and
Yu, Zhiwei and
Yoo, Hyunwoo and
Chen, Qi and
Gong, Yeyun",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1972/",
pages = "39590--39603",
ISBN = "979-8-89176-395-1",
abstract = {As numerous instruction-tuning datasets continue to emerge, dynamically balancing and optimizing their mixtures has become a criticalchallenge. To address this, we propose DynamixSFT, a dynamic and automated method for instruction-tuning dataset mixture optimization. We formulate the problem as a multi-armed bandit setup and introduce a Prior-scaled Boltzmann Exploration that softly anchors the updated sampling distribution to the original dataset proportions, thereby preserving the inherent diversity and coverage of the collection. Sampling probabilities are updated using a lightweight 1-Step Look-ahead Reward, reflecting how much the dataset contributes to improving the model{'}s performance at its current state. We demonstrate that DynamixSFT effectively optimizes the T{\"U}LU-2-mixture andT{\"U}LU-3-mixture collections across 10 benchmarks, while introducing minimal computational overhead over naive sampling. Furthermore, we provide a comprehensive analysis and visualizations to offer deeper insights into the adaptive dynamics of our method.}
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<abstract>As numerous instruction-tuning datasets continue to emerge, dynamically balancing and optimizing their mixtures has become a criticalchallenge. To address this, we propose DynamixSFT, a dynamic and automated method for instruction-tuning dataset mixture optimization. We formulate the problem as a multi-armed bandit setup and introduce a Prior-scaled Boltzmann Exploration that softly anchors the updated sampling distribution to the original dataset proportions, thereby preserving the inherent diversity and coverage of the collection. Sampling probabilities are updated using a lightweight 1-Step Look-ahead Reward, reflecting how much the dataset contributes to improving the model’s performance at its current state. We demonstrate that DynamixSFT effectively optimizes the TÜLU-2-mixture andTÜLU-3-mixture collections across 10 benchmarks, while introducing minimal computational overhead over naive sampling. Furthermore, we provide a comprehensive analysis and visualizations to offer deeper insights into the adaptive dynamics of our method.</abstract>
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%0 Conference Proceedings
%T DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections
%A Shin, Haebin
%A Ji, Lei
%A Liu, Xiao
%A Yu, Zhiwei
%A Yoo, Hyunwoo
%A Chen, Qi
%A Gong, Yeyun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F shin-etal-2026-dynamixsft
%X As numerous instruction-tuning datasets continue to emerge, dynamically balancing and optimizing their mixtures has become a criticalchallenge. To address this, we propose DynamixSFT, a dynamic and automated method for instruction-tuning dataset mixture optimization. We formulate the problem as a multi-armed bandit setup and introduce a Prior-scaled Boltzmann Exploration that softly anchors the updated sampling distribution to the original dataset proportions, thereby preserving the inherent diversity and coverage of the collection. Sampling probabilities are updated using a lightweight 1-Step Look-ahead Reward, reflecting how much the dataset contributes to improving the model’s performance at its current state. We demonstrate that DynamixSFT effectively optimizes the TÜLU-2-mixture andTÜLU-3-mixture collections across 10 benchmarks, while introducing minimal computational overhead over naive sampling. Furthermore, we provide a comprehensive analysis and visualizations to offer deeper insights into the adaptive dynamics of our method.
%U https://aclanthology.org/2026.findings-acl.1972/
%P 39590-39603
Markdown (Informal)
[DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections](https://aclanthology.org/2026.findings-acl.1972/) (Shin et al., Findings 2026)
ACL
- Haebin Shin, Lei Ji, Xiao Liu, Zhiwei Yu, Hyunwoo Yoo, Qi Chen, and Yeyun Gong. 2026. DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections. In Findings of the Association for Computational Linguistics: ACL 2026, pages 39590–39603, San Diego, California, United States. Association for Computational Linguistics.