针对金融大模型微调，提出系统化适配方案并实现顶尖性能
Demystifying Domain-adaptive Post-training for Financial LLMs
- 构建金融能力框架与联合训练配方，融合生成奖励信号优化指令遵循
- 在多个金融任务上超越现有模型，关键指标提升显著
- 适合金融AI研发人员参考，尤其关注领域适配策略设计
领域自适应后训练是医学、金融等专业领域大语言模型的重要方法。然而，在不同数据与模型配置下，如何确定最优适配标准与训练策略仍面临挑战。为此，本文提出FINDAP，一个面向金融领域的系统性、细粒度研究框架。包含四大组件：FinCap定义目标领域核心能力；FinRec提出联合持续预训练与指令跟随的高效训练配方，并引入基于生成奖励模型过程信号的新偏好数据蒸馏方法；FinTrain提供经过筛选的训练数据集；FinEval构建与FinCap对齐的全面评估套件。由此得到的Llama-Fin模型在多种金融任务中达到当前最优表现。分析揭示各后训练阶段对特定能力的贡献，识别出关键挑战与有效解决方案，为领域适配提供宝贵洞见。
原文摘要 · Abstract (English)
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 consists of four key components: FinCap, which defines the core capabilities required for the target domain; FinRec, an effective training recipe that jointly optimizes continual pre-training and instruction-following, along with a novel preference data distillation method leveraging process signals from a generative reward model; FinTrain, a curated set of training datasets supporting FinRec; and FinEval, a comprehensive evaluation suite aligned with FinCap. 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
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