@inproceedings{lyu-etal-2026-embarrassingly,
title = "An Embarrassingly Simple Data Selection Strategy for Million-Scale Multi-Modal Instruction-Tuning",
author = "Lyu, Mengyao and
Li, Yan and
Zhong, Huasong and
Chen, Hui and
Han, Jungong and
Ding, Guiguang and
Yang, Zhenheng",
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.2380/",
pages = "54056--54099",
ISBN = "979-8-89176-459-0",
abstract = "Recent research demonstrates that pretrained large language models (LLMs) require minimal but carefully curated data for instruction-tuning. Building on LLMs, multi-modal models further contend with far larger data volumes, heterogeneous sources, and cross-modal alignment, amplifying both the necessity and the complexity of data selection. However, existing methods rely on deeply extracted embeddings, gradients or narrow quality scores that are time-consuming to compute and limited in expressiveness, frequently failing to surpass a simple random-sampling baseline. To overcome these limitations, we step back from complex embedding spaces and target the fundamental objective of instruction tuning: how and what knowledge should be elicited from the pretrained model and exposed to users. We leverage interaction Styles as a proxy for structural diversity, and decompose data quality into capability-specific Scores, yielding an explicit yet Rich (SSR) profile for each sample. We further cast the selection as submodular coverage maximization over the capability-style grid, providing an approximation guarantee. SSR consistently delivers superior performance across two distinct architecture stacks, LLaVA-1.5 (Vicuna + CLIP) and LLaVA-OneVision (Qwen2 + SigLIP), two large-scale data pools, six data budget settings, and 14 multi-modal benchmarks. To our knowledge, SSR is the first data selection method validated at million-scale, achieving 99.1{\%} of full-data performance using only 30{\%} of a 2.6M pool. Furthermore, the SSR profile models and selected subsets transfer training-free across data pools and model architectures with capability extensibility."
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<abstract>Recent research demonstrates that pretrained large language models (LLMs) require minimal but carefully curated data for instruction-tuning. Building on LLMs, multi-modal models further contend with far larger data volumes, heterogeneous sources, and cross-modal alignment, amplifying both the necessity and the complexity of data selection. However, existing methods rely on deeply extracted embeddings, gradients or narrow quality scores that are time-consuming to compute and limited in expressiveness, frequently failing to surpass a simple random-sampling baseline. To overcome these limitations, we step back from complex embedding spaces and target the fundamental objective of instruction tuning: how and what knowledge should be elicited from the pretrained model and exposed to users. We leverage interaction Styles as a proxy for structural diversity, and decompose data quality into capability-specific Scores, yielding an explicit yet Rich (SSR) profile for each sample. We further cast the selection as submodular coverage maximization over the capability-style grid, providing an approximation guarantee. SSR consistently delivers superior performance across two distinct architecture stacks, LLaVA-1.5 (Vicuna + CLIP) and LLaVA-OneVision (Qwen2 + SigLIP), two large-scale data pools, six data budget settings, and 14 multi-modal benchmarks. To our knowledge, SSR is the first data selection method validated at million-scale, achieving 99.1% of full-data performance using only 30% of a 2.6M pool. Furthermore, the SSR profile models and selected subsets transfer training-free across data pools and model architectures with capability extensibility.</abstract>
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%0 Conference Proceedings
%T An Embarrassingly Simple Data Selection Strategy for Million-Scale Multi-Modal Instruction-Tuning
%A Lyu, Mengyao
%A Li, Yan
%A Zhong, Huasong
%A Chen, Hui
%A Han, Jungong
%A Ding, Guiguang
%A Yang, Zhenheng
%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 lyu-etal-2026-embarrassingly
%X Recent research demonstrates that pretrained large language models (LLMs) require minimal but carefully curated data for instruction-tuning. Building on LLMs, multi-modal models further contend with far larger data volumes, heterogeneous sources, and cross-modal alignment, amplifying both the necessity and the complexity of data selection. However, existing methods rely on deeply extracted embeddings, gradients or narrow quality scores that are time-consuming to compute and limited in expressiveness, frequently failing to surpass a simple random-sampling baseline. To overcome these limitations, we step back from complex embedding spaces and target the fundamental objective of instruction tuning: how and what knowledge should be elicited from the pretrained model and exposed to users. We leverage interaction Styles as a proxy for structural diversity, and decompose data quality into capability-specific Scores, yielding an explicit yet Rich (SSR) profile for each sample. We further cast the selection as submodular coverage maximization over the capability-style grid, providing an approximation guarantee. SSR consistently delivers superior performance across two distinct architecture stacks, LLaVA-1.5 (Vicuna + CLIP) and LLaVA-OneVision (Qwen2 + SigLIP), two large-scale data pools, six data budget settings, and 14 multi-modal benchmarks. To our knowledge, SSR is the first data selection method validated at million-scale, achieving 99.1% of full-data performance using only 30% of a 2.6M pool. Furthermore, the SSR profile models and selected subsets transfer training-free across data pools and model architectures with capability extensibility.
%U https://aclanthology.org/2026.emnlp-main.2380/
%P 54056-54099
Markdown (Informal)
[An Embarrassingly Simple Data Selection Strategy for Million-Scale Multi-Modal Instruction-Tuning](https://aclanthology.org/2026.emnlp-main.2380/) (Lyu et al., EMNLP 2026)
ACL