@inproceedings{xiong-etal-2025-flag,
    title = "{FLAG}-{TRADER}: Fusion {LLM}-Agent with Gradient-based Reinforcement Learning for Financial Trading",
    author = "Xiong, Guojun  and
      Deng, Zhiyang  and
      Wang, Keyi  and
      Cao, Yupeng  and
      Li, Haohang  and
      Yu, Yangyang  and
      Peng, Xueqing  and
      Lin, Mingquan  and
      Smith, Kaleb E  and
      Liu, Xiao-Yang  and
      Huang, Jimin  and
      Ananiadou, Sophia  and
      Xie, Qianqian",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.716/",
    pages = "13921--13934",
    ISBN = "979-8-89176-256-5",
    abstract = "Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks. However, they often struggle with multi-step, goal-oriented scenarios in interactive financial markets, such as trading, where complex agentic approaches are required to improve decision-making. To address this, we propose FLAG-Trader, a unified architecture integrating linguistic processing (via LLMs) with gradient-driven reinforcement learning (RL) policy optimization, in which a partially fine-tuned LLM acts as the policy network, leveraging pre-trained knowledge while adapting to the financial domain through parameter-efficient fine-tuning. Through policy gradient optimization driven by trading rewards, our framework not only enhances LLM performance in trading but also improves results on other financial-domain tasks. We present extensive empirical evidence to validate these enhancements."
}