Eagle 2.5: Boosting Long-Context Post-Training for Frontier
  Vision-Language Models
Authors: name: Guo Chen, name: Zhiqi Li, name: Shihao Wang, name: Jindong Jiang, name: Yicheng Liu, name: Lidong Lu, name: De-An Huang, name: Wonmin Byeon, name: Matthieu Le, name: Tuomas Rintamaki, name: Tyler Poon, name: Max Ehrlich, name: Tuomas Rintamaki, name: Tyler Poon, name: Tong Lu, name: Limin Wang, name: Bryan Catanzaro, name: Jan Kautz, name: Andrew Tao, name: Zhiding Yu, name: Guilin Liu

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.