Fully open multimodal large language models (MLLMs) currently lag behind proprietary counterparts, primarily due to a significant gap in data quality for supervised fine-tuning (SFT). Existing open-source datasets are often plagued by widespread noise and a critical deficit in complex reasoning data, such as Chain-of-Thought (CoT), which hinders the development of advanced model capabilities. Addressing these challenges, our work makes three primary contributions. First, we introduce Honey-Data-15M, a new SFT dataset comprising approximately 15 million QA pairs, processed through multiple cleaning techniques and enhanced with a novel dual-level (short and long) CoT enrichment strategy. Second, we introduce HoneyPipe, the data curation pipeline, and its underlying framework DataStudio, providing the community with a transparent and adaptable methodology for data curation that moves beyond static dataset releases. Finally, to validate our dataset and pipeline, we train Bee-8B, an 8B model on Honey-Data-15M. Experiments show that Bee-8B establishes a new state-of-the-art (SOTA) for fully open MLLMs, achieving performance that is competitive with, and in some cases surpasses, recent semi-open models such as InternVL3.5-8B. Our work delivers to the community a suite of foundational resources, including: the Honey-Data-15M corpus; the full-stack suite comprising HoneyPipe and DataStudio; training recipes; an evaluation harness; and the model weights. This effort demonstrates that a principled focus on data quality is a key pathway to developing fully open MLLMs that are highly competitive with their semi-open counterparts.

翻译：当前，完全开源的多模态大语言模型（MLLMs）在性能上落后于其专有模型，主要原因在于监督微调（SFT）阶段的数据质量存在显著差距。现有的开源数据集普遍存在广泛的噪声，并且严重缺乏复杂推理数据（例如思维链（CoT）），这阻碍了模型高级能力的发展。针对这些挑战，我们的工作做出了三项主要贡献。首先，我们引入了Honey-Data-15M，这是一个包含约1500万个问答对的新SFT数据集，它通过多种清洗技术进行处理，并采用了一种新颖的双层（短链与长链）CoT增强策略进行优化。其次，我们引入了数据整理流水线HoneyPipe及其底层框架DataStudio，为社区提供了一种透明且可适配的数据整理方法论，超越了静态数据集发布的局限。最后，为了验证我们的数据集和流水线，我们在Honey-Data-15M上训练了Bee-8B，一个拥有80亿参数的模型。实验表明，Bee-8B为完全开源的MLLMs确立了新的性能最优（SOTA）水平，其表现与近期半开源模型（如InternVL3.5-8B）相当，并在某些方面实现了超越。我们的工作为社区提供了一套基础资源，包括：Honey-Data-15M语料库；包含HoneyPipe和DataStudio的全栈套件；训练方案；评估工具集；以及模型权重。这项努力表明，对数据质量的原则性关注是开发能够与半开源模型高度竞争的完全开源MLLMs的关键途径。