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Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models (2504.15271v1)

Published 21 Apr 2025 in cs.CV

Abstract: We introduce Eagle 2.5, a family of frontier vision-LLMs (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.

Summary

Essay on "Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-LLMs"

The paper "Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-LLMs" presents an advanced framework for enhancing the capabilities of Vision-LLMs (VLMs) in processing and understanding long-context data. This research underscores the significance of an integrated approach comprising novel training strategies and a comprehensive dataset to advance the state of VLMs.

Overview of Eagle 2.5

Eagle 2.5 is designed as a generalist VLM framework that addresses the challenges inherent in long video comprehension and high-resolution image understanding. The authors introduce a training paradigm that incorporates Automatic Degrade Sampling (ADS) and Image Area Preservation (IAP) techniques, coupled with efficiency optimizations tailored for long-context data training. These elements are crucial for maintaining contextual integrity and preserving visual details in both video and image tasks.

The performance of Eagle 2.5 is assessed across several long-context multimodal benchmarks, demonstrating substantial improvements over existing VLMs. Notably, Eagle 2.5-8B achieves a 72.4% accuracy on the Video-MME benchmark with 512 input frames, which is competitive with commercial models like GPT-4o and large-scale open-source models such as Qwen2.5-VL-72B and InternVL2.5-78B, despite a significantly smaller parameter footprint.

Training Strategy

The primary innovation reported in the Eagle 2.5 framework is its information-first sampling strategy, alongside a progressive mixed post-training schedule. The information-first sampling optimizes data retention by maintaining essential visual and semantic information, pivotal for tasks requiring extensive detail analysis. The ADS, a unique strategy to balance visual and textual input, prioritizes text retention while adaptively optimizing visual sampling, thereby maximizing context length utilization.

Progressive training augments the model's ability to handle diverse input sizes by gradually increasing the context length during training. This method enhances information density compared to static sampling strategies, ensuring consistent performance over varied input types and lengths.

Eagle-Video-110K Dataset

Eagle-Video-110K dataset is another cornerstone of this work, designed explicitly to bolster long video understanding capabilities. The dataset integrates open-source media with a new dataset that explores the narrative context through hierarchical annotation strategies. The dual-level annotation approach combines story-level and clip-level data, utilizing both human-curated and automated annotation methods to ensure a broad coverage of narrative structures and fine-grained details.

Implications and Future Prospects

This paper's contributions have critical implications for the development of VLMs with enhanced comprehension of prolonged visual and textual content, which is vital in fields such as automated video analysis, interactive media, and beyond. By addressing common barriers in long-context multimodal learning—such as inefficiencies in training strategies and dataset limitations—Eagle 2.5 paves the way for practical applications requiring robust video and image processing capabilities.

Future developments in AI, prompted by this research, may focus on further optimizing model architectures to handle increasingly detailed and extended contexts. Additionally, as the demand for real-time and contextually aware AI systems grows, Eagle 2.5’s approach offers a scalable framework for subsequent innovations in vision-language AI models.

In summary, Eagle 2.5 represents a significant stride in VLM development, offering a scalable and effective solution for long-context multimodal learning and setting a high bar for future research and applications in this domain.