title: Eagle 2.5: Boosting Long-Context Post-Training for Frontier
    Vision-Language Models
summary: We introduce Eagle 2.5, a family of frontier vision-language models (VLMs)
  for long-context multimodal learning. Our work addresses the challenges in long
  video comprehension and high-resolution image understanding, introducing a
  generalist framework for both tasks. The proposed training framework
  incorporates Automatic Degrade Sampling and Image Area Preservation, two
  techniques that preserve contextual integrity and visual details. The framework
  also includes numerous efficiency optimizations in the pipeline for
  long-context data training. Finally, we propose Eagle-Video-110K, a novel
  dataset that integrates both story-level and clip-level annotations,
  facilitating long-video understanding. Eagle 2.5 demonstrates substantial
  improvements on long-context multimodal benchmarks, providing a robust solution
  to the limitations of existing VLMs. Notably, our best model Eagle 2.5-8B
  achieves 72.4% on Video-MME with 512 input frames, matching the results of
  top-tier commercial model such as GPT-4o and large-scale open-source models
  like Qwen2.5-VL-72B and InternVL2.5-78B.
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paper_authors.name: Guo Chen
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paper_authors.name: Matthieu Le
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paper_authors.name: Tuomas Rintamaki
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paper_authors.name: Jan Kautz
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paper_title: Eagle 2.5: Boosting Long-Context Post-Training for Frontier
    Vision-Language Models
paper_summary: We introduce Eagle 2.5, a family of frontier vision-language models (VLMs)
  for long-context multimodal learning. Our work addresses the challenges in long
  video comprehension and high-resolution image understanding, introducing a
  generalist framework for both tasks. The proposed training framework
  incorporates Automatic Degrade Sampling and Image Area Preservation, two
  techniques that preserve contextual integrity and visual details. The framework
  also includes numerous efficiency optimizations in the pipeline for
  long-context data training. Finally, we propose Eagle-Video-110K, a novel
  dataset that integrates both story-level and clip-level annotations,
  facilitating long-video understanding. Eagle 2.5 demonstrates substantial
  improvements on long-context multimodal benchmarks, providing a robust solution
  to the limitations of existing VLMs. Notably, our best model Eagle 2.5-8B
  achieves 72.4% on Video-MME with 512 input frames, matching the results of
  top-tier commercial model such as GPT-4o and large-scale open-source models
  like Qwen2.5-VL-72B and InternVL2.5-78B.
paper_ai_summary: Eagle 2.5 improves long-context multimodal learning through Automatic Degrade Sampling and Image Area Preservation techniques, enhancing VLMs for video and image understanding, and matches state-of-the-art performance on benchmarks.
paper_ai_keywords: vision-language models long-context multimodal learning long video comprehension high-resolution image understanding Automatic Degrade Sampling Image Area Preservation efficiency optimizations long-context data training Eagle-Video-110K multimodal benchmarks Video-MME