Training Data Selection

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning
Mar 12, 2026Rujie Wu, Haozhe Zhao, Hai Ci +1Video Understanding Instruction-Tuning Data Selection
School of Computer Science, Peking University, Beijing, China · Institute for Artificial Intelligence, Peking University, Beijing, China · State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China +2
QAQ: Bidirectional Semantic Coherence for Selecting High-Quality Synthetic Code Instructions
Mar 12, 2026Jiayin Lei, Ming Ma, Yunxi Duan +2Instruction-Tuning Data Selection Code Generation
Beijing University of Technology, Beijing, 100124, China · Institute of Neuroscience, State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China · University of Chinese Academy of Sciences, Beijing, 100049, China +1
VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning
Mar 1, 2026Mingkang Dong, Hongyi Cai, Jie Li +4Vision-Language Models Data Selection
SJTU · Universiti Malaya · CMU +3
Explainable Token-level Noise Filtering for LLM Fine-tuning Datasets
Feb 16, 2026Yuchen Yang, Wenze Lin, Enhao Huang +6LLM Fine-Tuning Training Data Curation
1The State Key Laboratory of Blockchain and Data Security, Zhejiang University · 2Hangzhou HighTech Zone (Binjiang) Blockchain and Data Security Research Institute, China · 3Qiuzhen College, Tsinghua University +2
Not All Preferences Deserve Gradients: Understanding Gradient Utility in Offline Reasoning Alignment
Feb 1, 2026Hui Wu, Hengyi Cai, Jinman Zhao +6Pairwise Preference Learning Training Data Selection
Baidu Inc. · University of Toronto · School of Intelligence Science and Technology, Peking University +1
Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining
Oct 1, 2025Thiziri Nait Saada, Louis Bethune, Michal Klein +3Language Model Pretraining Training Data Curation
Work done as an intern at Apple · University of Oxford · Apple
Predicting LLM Reasoning Performance with Small Proxy Model
Sep 25, 2025Woosung Koh, Juyoung Suk, Sungjun Han +2Language Model Pretraining LLM Evaluation
Trillion Labs · KAIST AI · ▽Trillion Labs, ♠KAIST AI
Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models
Oct 16, 2024Daniele Gambetta, Gizem Gezici, Fosca Giannotti +3Model Collapse Training Data Selection
University of Pisa, Pisa, Italy · ISTI-CNR, Pisa, Italy · Scuola Normale Superiore, Pisa, Italy +1

Mar 12, 2026, cs.CVJ/K move · Enter open · S save

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning

Rujie Wu, Haozhe Zhao, Hai Ci, Yizhou Wang

School of Computer Science, Peking University, Beijing, China · Institute for Artificial Intelligence, Peking University, Beijing, China · State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China+2

Video Understanding Instruction-Tuning Data Selection Training Data Selection Multimodal Optimization Multimodal Foundation Models Multimodal Instruction Tuning

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Multimodal instruction tuning is often compute-inefficient because training budgets are spread across large mixed image-video pools whose utility is highly uneven. We present Goal-Driven Data Optimization (GDO), a framework that computes six sample descriptors for each candidate and constructs optimized 1

\times

training subsets for different goals. Under a fixed one-epoch Qwen3-VL-8B-Instruct training and evaluation recipe on 8 H20 GPUs, GDO uses far fewer training samples than the Uni-10x baseline while converging faster and achieving higher accuracy. Relative to the fixed 512k-sample Uni-10x baseline, GDO reaches the Uni-10x reference after 35.4k samples on MVBench, 26.6k on VideoMME, 27.3k on MLVU, and 34.7k on LVBench, while improving Accuracy by +1.38, +1.67, +3.08, and +0.84 percentage points, respectively. The gains are largest on MVBench and MLVU, while LVBench improves more modestly, consistent with its ultra-long-video setting and the mismatch between that benchmark and the short-video/image-dominant training pool. Across MinLoss, Diverse, Temp, and Temp+, stronger temporal emphasis yields steadily better long-video understanding behavior. Overall, GDO provides a goal-driven data optimization framework that enables faster convergence with fewer training samples under a fixed training protocol. Code is available at https://github.com/rujiewu/GDO.

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