Staged Continual Adaptation of Multimodal Foundation Models for Japanese Financial Documents

Key points

• 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.1
• 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.1

Sources (1)

• [1]Staged Continual Adaptation of Multimodal Foundation Models for Japanese Financial Documents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 09:19 AM

  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.

  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.

Extractive summary: sentences quoted from the sources.