Eagle 2.5: Boosting Long-Context Post-Training for Frontier
  Vision-Language Models
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.
680748d088578d9444349293
Guo Chen
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2025-04-22T10:46:58.348Z
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Guo Chen
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Zhiqi Li
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2025-04-22T09:50:20.873Z
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ZhiqiLi
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Shihao Wang
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Jindong Jiang
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Yicheng Liu
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Lidong Lu
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2025-04-22T10:51:00.432Z
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lulidong
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De-An Huang
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2025-04-22T10:50:49.235Z
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De-An Huang
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Wonmin Byeon
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2025-04-22T10:50:43.675Z
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Wonmin Byeon
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Matthieu Le
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Tuomas Rintamaki
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2025-04-22T10:49:24.223Z
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Tuomas Rintamaki
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Tyler Poon
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2025-04-22T10:49:41.245Z
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Tyler Poon
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Max Ehrlich
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Tuomas Rintamaki
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Tyler Poon
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Tong Lu
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Limin Wang
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2025-04-22T10:48:58.836Z
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Limin Wang
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Bryan Catanzaro
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Bryan Catanzaro
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Jan Kautz
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Andrew Tao
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Zhiding Yu
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Zhiding Yu
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Guilin Liu
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2025-04-22T10:48:17.611Z
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GuilinLiu
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GuilinLiu
Eagle 2.5: Boosting Long-Context Post-Training for Frontier
  Vision-Language Models
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.
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.
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