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ARXIV:2603.12478 · MULTIMODAL OPTIMIZATION · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE
ARXIV:2603.12478MULTIMODAL OPTIMIZATIONSUBMITTED 19 MAR · 21:31 UTCFRESHNESS STALEarXiv
GDO optimizes data usage for multimodal instruction tuning, achieving faster convergence with fewer samples.
Opportunity summary
Pain GDO optimizes data usage for multimodal instruction tuning, achieving faster convergence with fewer samples.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence partial
GDO optimizes data usage for multimodal instruction tuning, achieving faster convergence with fewer samples. We present Goal-Driven Data Optimization (GDO), a framework that computes six sample descriptors for each candidate and constructs optimized 1$\times$…
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…
ScienceToStartup currently rates this 9.0/10 on the public viability pass. 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…
Multimodal Optimization moved forward this cycle; last verified April 2026. Public score 9.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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GDO optimizes data usage for multimodal instruction tuning, achieving faster convergence with fewer samples.
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10.48550/arXiv.2603.12478GDO optimizes data usage for multimodal instruction tuning, achieving faster convergence with fewer samples.
Abstract
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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Proof status
partial0 refs; 0 sources; 33% coverage.
What was readable
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Viability
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Preparing verified analysis
Dimensions overall score 9.0
PROBLEM
GDO optimizes data usage for multimodal instruction tuning, achieving faster convergence with fewer samples. We present Goal-Driven Data Optimization (GDO), a framework that computes six sample descriptors for each candidate and constructs optimized 1$\times$ training subsets fo...
METHOD
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...
RESULT
ScienceToStartup currently rates this 9.0/10 on the public viability pass. 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 tra...
WHY NOW
Multimodal Optimization moved forward this cycle; last verified April 2026. Public score 9.0/10.
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.
Implication not extracted yet.
partial
GDO uses far fewer training samples than the Uni-10x baseline while converging faster and achieving higher accuracy.
Implication not extracted yet.
partial
GDO reaches the Uni-10x reference after 35.4k samples on MVBench
Implication not extracted yet.
partial
while improving Accuracy by +1.38 percentage points, respectively.
Implication not extracted yet.
partial
while improving Accuracy by +1.67 percentage points, respectively.
Implication not extracted yet.
partial
while improving Accuracy by +3.08 percentage points, respectively.
Implication not extracted yet.
partial
while improving Accuracy by +0.84 percentage points, respectively.
Implication not extracted yet.
partial
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.
Implication not extracted yet.
partial
GDO uses far fewer training samples than the Uni-10x baseline while converging faster and achieving higher accuracy.
Directly stated in abstract with supporting numeric results.
partial
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
Exact numbers provided in abstract.
partial
improving Accuracy by +1.38, +1.67, +3.08, and +0.84 percentage points, respectively.
Exact numbers provided in abstract.
partial
The gains are largest on MVBench and MLVU, while LVBench improves more modestly
Directly stated in abstract with explanation.
partial
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GDO optimizes data usage for multimodal instruction tuning, achieving faster convergence with fewer samples.
Segment
Multimodal Optimization
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