Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning | Signal Canvas | ScienceToStartup

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

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Proof freshness: stale
Proof status: partial
Display score: 9/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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

Canonical ID less-data-faster-convergence-goal-driven-data-optimization-for-multimodal-instruction-tuning | Route /signal-canvas/less-data-faster-convergence-goal-driven-data-optimization-for-multimodal-instruction-tuning

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MCP example

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source_context

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Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
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.
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Evidencepartial
GDO uses far fewer training samples than the Uni-10x baseline while converging faster and achieving higher accuracy.
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Evidencepartial
GDO reaches the Uni-10x reference after 35.4k samples on MVBench
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while improving Accuracy by +1.38 percentage points, respectively.
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Evidencepartial
while improving Accuracy by +1.67 percentage points, respectively.
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Evidencepartial
while improving Accuracy by +3.08 percentage points, respectively.
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Evidencepartial
while improving Accuracy by +0.84 percentage points, respectively.
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Evidencepartial
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.
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Implication not extracted yet.
Verificationpartial
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Evidencepartial
GDO uses far fewer training samples than the Uni-10x baseline while converging faster and achieving higher accuracy.
Implicationpartial
Directly stated in abstract with supporting numeric results.
Verificationpartial
partial
Evidencepartial
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
Implicationpartial
Exact numbers provided in abstract.
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Evidencepartial
improving Accuracy by +1.38, +1.67, +3.08, and +0.84 percentage points, respectively.
Implicationpartial
Exact numbers provided in abstract.
Verificationpartial
partial
Evidencepartial
The gains are largest on MVBench and MLVU, while LVBench improves more modestly
Implicationpartial
Directly stated in abstract with explanation.
Verificationpartial
partial