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Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models

2025-01-20 · Zhiqi Li, Guo Chen, Shilong Liu, Shihao Wang, Vibashan VS, Yishen Ji, Shiyi Lan, Hao Zhang, Yilin Zhao, Subhashree Radhakrishnan, Nadine Chang, Karan Sapra, Amala Sanjay Deshmukh, Tuomas Rintamaki, Matthieu Le, Ilia Karmanov, Lukas Voegtle, Philipp Fischer, De-An Huang, Timo Roman, Tong Lu, Jose M. Alvarez, Bryan Catanzaro, Jan Kautz, Andrew Tao, Guilin Liu, Zhiding Yu

Recently, promising progress has been made by open-source vision-language models (VLMs) in bringing their capabilities closer to those of proprietary frontier models. However, most open-source models only publish their final model weights, leaving the critical details of data strategies and implementation largely opaque. In this work, we address VLM post-training from a data-centric perspective, showing the key role of data strategy in developing frontier VLMs. By studying and building our post-training data strategy from scratch, we share detailed insights into the development processes, aiming to benefit the development of competitive models for the open-source community. Our introduced data strategy, together with training recipes and model design, leads to a family of performant VLMs named Eagle2. Specifically, Eagle2-9B achieves state-of-the-art results across various multimodal benchmarks, matching certain competitive models with up to 70B parameters.

📄 PDF Abstract BibTeX arXiv:2501.14818

Code (1)

nvlabs/eagle 공식 구현 pytorch

Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models

2025-04-21 · Guo Chen, Zhiqi Li, Shihao Wang, Jindong Jiang 외

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, intr…

MME Video MME Video Understanding

EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees

2024-06-24 · Yuhui Li, Fangyun Wei, Chao Zhang, Hongyang Zhang

Inference with modern Large Language Models (LLMs) is expensive and time-consuming, and speculative sampling has proven to be an effective solution. Most speculative sampling methods such as EAGLE use a static draft tree…

EAGLE: A Domain Generalization Framework for AI-generated Text Detection

2024-03-23 · Amrita Bhattacharjee, Raha Moraffah, Joshua Garland, Huan Liu

With the advancement in capabilities of Large Language Models (LLMs), one major step in the responsible and safe use of such LLMs is to be able to detect text generated by these models. While supervised AI-generated text…

Contrastive Learning Domain Generalization Text Detection

Informative Perturbation Selection for Uncertainty-Aware Post-hoc Explanations

2026-03-16 · Sumedha Chugh, Ranjitha Prasad, Nazreen Shah arxiv

Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model explanations. Post-hoc model-agnostic explanation methods addresses this challenge…

Feature Importance Active Learning

EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test

2025-03-03 · Yuhui Li, Fangyun Wei, Chao Zhang, Hongyang Zhang

The sequential nature of modern LLMs makes them expensive and slow, and speculative sampling has proven to be an effective solution to this problem. Methods like EAGLE perform autoregression at the feature level, reusing…

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