# Once-For-All: A Train-Once and Select-Anytime Framework for Multimodal Instruction Tuning

arXiv: https://arxiv.org/abs/2605.26761 · HTML: https://mlsanity.com/paper/2605.26761/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 29 of its 36 references that are in the map.
- First posted on arXiv 26 May 2026.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 29 of 29)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [A Diagram Is Worth A Dozen Images](https://mlsanity.com/paper/1603.07396/) (2016)
- [OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://mlsanity.com/paper/2305.07895/) (2023)
- [From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning](https://mlsanity.com/paper/2308.12032/) (2023)
- [Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning](https://mlsanity.com/paper/2402.11690/) (2024)
- [PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection](https://mlsanity.com/paper/2502.12119/) (2025)
- [Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection](https://mlsanity.com/paper/2402.12501/) (2024)
- [Large-Scale Data Selection for Instruction Tuning](https://mlsanity.com/paper/2503.01807/) (2025)
- [ObjectRelator: Enabling Cross-View Object Relation Understanding Across Ego-Centric and Exo-Centric Perspectives](https://mlsanity.com/paper/2411.19083/) (2024)
- [ICONS: Influence Consensus for Vision-Language Data Selection](https://mlsanity.com/paper/2501.00654/) (2024)
- [Concept-skill Transferability-based Data Selection for Large Vision-Language Models](https://mlsanity.com/paper/2406.10995/) (2024)
- [EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Question Answering](https://mlsanity.com/paper/2508.10729/) (2025)
- [EgoNight: Towards Egocentric Vision Understanding at Night with a Challenging Benchmark](https://mlsanity.com/paper/2510.06218/) (2025)
- [Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model](https://mlsanity.com/paper/2605.14950/) (2026)
- [V2-SAM: Marrying SAM2 with Multi-Prompt Experts for Cross-View Object Correspondence](https://mlsanity.com/paper/2511.20886/) (2025)
- [Efficient Sampling for k-Determinantal Point Processes](https://mlsanity.com/paper/1509.01618/) (2015)
- [EgoSound: Benchmarking Sound Understanding in Egocentric Videos](https://mlsanity.com/paper/2602.14122/) (2026)
- [CoIDO: Efficient Data Selection for Visual Instruction Tuning via Coupled Importance-Diversity Optimization](https://mlsanity.com/paper/2510.17847/) (2025)
- [VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning](https://mlsanity.com/paper/2603.01195/) (2026)
- [Data Selection for Fine-tuning Vision Language Models via Cross Modal Alignment Trajectories](https://mlsanity.com/paper/2510.01454/) (2025)
- [Pistachio: Towards Synthetic, Balanced, and Long-Form Video Anomaly Benchmarks](https://mlsanity.com/paper/2511.19474/) (2025)
- [ScalSelect: Scalable Training-Free Multimodal Data Selection for Efficient Visual Instruction Tuning](https://mlsanity.com/paper/2602.11636/) (2026)
- [Understanding Museum Exhibits using Vision-Language Reasoning](https://mlsanity.com/paper/2412.01370/) (2024)
- [DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models](https://mlsanity.com/paper/2604.16979/) (2026)

## Built on by (most cited first, 1 of 1)

- [Multi-scale Decomposed Convolution Refinement Network for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2608.16015/) (2026)

## Abstract (arXiv)

Multimodal instruction tuning is widely used to adapt multimodal large language models (MLLMs), yet the large-scale image-text datasets it relies on are often highly redundant. Existing data selection methods are commonly tied to specific datasets, target models, or training states, requiring retraining or recomputation when transferred to new settings. We ask whether a selection signal can instead be learned once and reused across datasets and models. We propose OnceSelect, a reusable data selector trained once and directly applied to unseen datasets. OnceSelect encodes image-instruction pairs in a frozen joint multimodal space, clusters them into pseudo-labels capturing coarse semantic structure, and trains a lightweight selector using a fixed validation macro-accuracy criterion. Low-confidence samples under the learned partition are treated as informative candidates. As the selector is independent of the downstream MLLM, it can be transferred across datasets without retraining or model-dependent scoring, while selected subsets can be reused across model architectures. Using only 15% of LLaVA-625K, OnceSelect retains 99.2% of full-data aggregate performance across nine evaluation metrics. Without retraining, it achieves 103.4% and 102.5% relative performance on unseen Vision-Flan-186K and LRV-Sub-180K. The same 93.7K LLaVA subset further achieves 99.9-106.7% relative performance across Qwen3-VL and InternVL3 models without model-specific scoring or reselection. These results demonstrate efficient and reusable multimodal data selection across datasets and model architectures. Code is available at https://github.com/DMK041218/OnceSelect