How to Train Your Long-Context Visual Document Model
📰 ArXiv cs.AI
Comprehensive study on training long-context visual document models for visual question answering
Action Steps
- Continue pretraining of long-context vision language models to improve performance
- Apply supervised finetuning to adapt models to specific tasks
- Investigate preference optimization for better transfer learning
- Evaluate model performance on long-document visual question answering tasks
Who Needs to Know This
AI engineers and ML researchers benefit from this study as it provides insights into training large-scale vision language models, while product managers can apply these findings to develop more accurate visual question answering systems
Key Insight
💡 Systematic study of training recipes and data pipelines is crucial for reproducible results in long-context vision language models
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📚 Train long-context visual document models for accurate visual question answering!
Key Takeaways
Comprehensive study on training long-context visual document models for visual question answering
Full Article
Title: How to Train Your Long-Context Visual Document Model
Abstract:
arXiv:2602.15257v2 Announce Type: replace-cross Abstract: We present the first comprehensive, large-scale study of training long-context vision language models up to 344K context, targeting long-document visual question answering with measured transfer to long-context text. While several such strong are open-weight, namely Qwen3 VL and GLM 4.5/6V, their training recipes and data pipelines are not reproducible. We systematically study continued pretraining, supervised finetuning, and preference o
Abstract:
arXiv:2602.15257v2 Announce Type: replace-cross Abstract: We present the first comprehensive, large-scale study of training long-context vision language models up to 344K context, targeting long-document visual question answering with measured transfer to long-context text. While several such strong are open-weight, namely Qwen3 VL and GLM 4.5/6V, their training recipes and data pipelines are not reproducible. We systematically study continued pretraining, supervised finetuning, and preference o
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