Domain-specific enhancement of Large Language Models (LLMs) within the financial context has long been a focal point of industrial application. While previous models such as BloombergGPT and Baichuan-Finance primarily focused on knowledge enhancement, the deepening complexity of financial services has driven a growing demand for models that possess not only domain knowledge but also robust financial reasoning and agentic capabilities. In this paper, we present QianfanHuijin, a financial domain LLM, and propose a generalizable multi-stage training paradigm for industrial model enhancement. Our approach begins with Continual Pre-training (CPT) on financial corpora to consolidate the knowledge base. This is followed by a fine-grained Post-training pipeline designed with increasing specificity: starting with Financial SFT, progressing to Finance Reasoning RL and Finance Agentic RL, and culminating in General RL aligned with real-world business scenarios. Empirical results demonstrate that QianfanHuijin achieves superior performance across various authoritative financial benchmarks. Furthermore, ablation studies confirm that the targeted Reasoning RL and Agentic RL stages yield significant gains in their respective capabilities. These findings validate our motivation and suggest that this fine-grained, progressive post-training methodology is poised to become a mainstream paradigm for various industrial-enhanced LLMs.

翻译：在金融领域对大型语言模型进行领域特定增强一直是工业应用的重点。尽管先前模型如BloombergGPT和百川金融主要关注知识增强，但随着金融服务复杂性的加深，市场对模型的需求已不仅限于领域知识，更要求其具备强大的金融推理与智能体能力。本文提出千帆汇金这一金融领域大语言模型，并构建了一种可推广的工业模型增强多阶段训练范式。该方法首先通过金融语料的持续预训练巩固知识基础，随后采用细粒度、逐级递进的后训练流程：从金融监督微调开始，逐步推进至金融推理强化学习与金融智能体强化学习，最终通过面向实际业务场景的通用强化学习完成对齐。实证结果表明，千帆汇金在多项权威金融基准测试中均取得优异性能。消融研究进一步证实，针对性的推理强化学习与智能体强化学习阶段能显著提升相应能力。这些发现验证了我们的设计动机，表明这种细粒度渐进式后训练方法有望成为各类工业增强大语言模型的主流范式。