Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance

원문 초록

While large language models (LLMs) have shown strong general reasoning capabilities, their effectiveness in financial reasoning, which is crucial for real-world financial applications remains underexplored. In this study, we conduct a comprehensive evaluation of 24 state-of-the-art general and reasoning-focused LLMs across four complex financial reasoning tasks involving financial text, tabular data, and equations. We assess key capabilities such as numerical reasoning, tabular interpretation, financial terminology comprehension, long-context understanding, and equation-based problem solving. Our analysis reveals that while data quality and pretraining contribute to performance, general techniques like chain-of-thought (CoT) fine-tuning offer limited gains in financial tasks. To address this, we propose two domain-adapted models, Fino1-8B and Fino1-14B, trained with CoT fine-tuning and reinforcement learning using domain-specific reasoning paths. Our models are trained on a carefully curated dataset integrating high-quality examples from diverse sources, covering financial reports, tables, equations, and structured XBRL texts. Despite limited training data, they achieve an 7-9% performance improvement, outperforming several advanced LLMs, including GPT-o1, GPT-o3-mini, GPT-4.5, and comparable with DeepSeek models (V3 and R1), demonstrating strong practical value in resource, constrained scenarios. Our findings highlight the need for domain-specific adaptations in financial reasoning, and we release all datasets, models, and code for future research.