Domain-adaptive post-training of large language models (LLMs) has emerged as a promising approach for specialized domains such as medicine and finance. However, significant challenges remain in identifying optimal adaptation criteria and training strategies across varying data and model configurations. To address these challenges, we introduce FINDAP, a systematic and fine-grained investigation into domain-adaptive post-training of LLMs for the finance domain. Our approach begins by identifying the core capabilities required for the target domain and designing a comprehensive evaluation suite aligned with these needs. We then analyze the effectiveness of key post-training stages, including continual pretraining, instruction tuning, and preference alignment. Building on these insights, we propose an effective training recipe centered on a novel preference data distillation method, which leverages process signals from a generative reward model. The resulting model, Llama-Fin, achieves state-of-the-art performance across a wide range of financial tasks. Our analysis also highlights how each post-training stage contributes to distinct capabilities, uncovering specific challenges and effective solutions, providing valuable insights for domain adaptation of LLMs. Project page: https://github.com/SalesforceAIResearch/FinDap

本研究针对金融领域大语言模型的领域自适应后训练面临的挑战，提出FINDAP作为一种系统化的研究方法。通过识别目标领域所需的核心能力并构建评估体系，研究提出一种基于新颖偏好数据蒸馏方法的有效训练方案，使得生成的模型Llama-Fin在多项金融任务中展现了卓越表现，从而为LLM的领域适应提供了重要的见解和解决方案。

揭示金融大语言模型的领域自适应后训练