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

TL;DR

Eagle 2 demonstrates that carefully designed post-training data strategies can significantly enhance open-source vision-language models, achieving state-of-the-art results comparable to larger proprietary models.

Contribution

This work introduces a novel data-centric post-training strategy for VLMs, providing detailed insights and recipes to develop competitive open-source models from scratch.

Findings

Eagle2-9B achieves state-of-the-art results on multiple benchmarks.

The data strategy enables smaller models to match larger proprietary models.

Detailed development process benefits open-source VLM community.

Abstract

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.