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ARC benchmark performance hinges on training budgets, study finds

By PulseAugur Editorial · [1 sources] ·

A new study on the Abstraction and Reasoning Corpus (ARC) highlights the significant impact of pretraining and test-time training (TTT) budgets on model performance. Researchers investigated how varying these budgets affects the VARC and TRM models, finding that increased TTT sample presentations consistently improve ARC pass@2 scores for both. However, the benefits of TTT task-variant counts differed, with TRM showing consistent improvement while VARC performed better with fewer variants. The study also noted that VARC is competitive without pretraining, whereas TRM benefits substantially from it, underscoring the need for standardized reporting of training budgets in ARC evaluations. AI

IMPACT Highlights the critical need for standardized reporting of training budgets in AI benchmark evaluations to ensure fair comparisons.

RANK_REASON Academic paper detailing a controlled study on model training budgets for a specific benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ARC benchmark performance hinges on training budgets, study finds

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Academic paper detailing a controlled study on model training budgets for a specific benchmark. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Adrien Deliege, Marc Van Droogenbroeck ·

    Pretraining and Test-Time Training Budgets Matter for ARC: A Controlled Study of VARC and TRM

    arXiv:2610.13660v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC) is increasingly used to evaluate whether neural networks can infer task-specific transformations from few demonstrations. Recent ARC models differ in architecture, but also in broader tra…

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