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Staged Continual Adaptation of Multimodal Foundation Models for Japanese Financial Documents

2026-10-20 09:19:36
Genshin Kakimoto, Atsushi Yanagisawa
arXiv_AI
       
Abstract

Staged post-training of multimodal foundation models chains vision-language alignment, reasoning distillation, and domain tuning, yet how each phase trades capabilities against the others remains under-characterised. Tracking an 8.4B-parameter model from an un-fine-tuned baseline through three training phases on Japanese financial documents (evaluated on five text-input benchmarks), we find the phases interact non-additively: math-only distillation overwrites the financial knowledge acquired earlier even as procedural math skill survives, and the domain phase repairs more than it installs. A skip-phase ablation is best for domain knowledge but forfeits numerical-reasoning gains, so per-phase contributions are not decomposable and the best checkpoint is task-dependent, with implications for checkpoint retention and scheduling rather than added stages.

Abstract (translated)

URL

https://arxiv.org/abs/2610.27904

PDF

https://arxiv.org/pdf/2610.27904.pdf

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