title: FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning
    for Financial Trading
summary: 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.
paper_authors._id: 67b54a644508bd0617598c21
paper_authors.name: Guojun Xiong
paper_authors.status: admin_assigned
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paper_authors.name: Zhiyang Deng
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paper_authors.fullname: Zhiyang Deng
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paper_authors.name: Keyi Wang
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paper_authors.name: Yupeng Cao
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paper_authors.fullname: Yupeng Cao
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paper_authors.name: Haohang Li
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paper_authors.fullname: Haohang Li
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paper_authors.user: Acatsama
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paper_authors.name: Yangyang Yu
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paper_authors.avatarUrl: /avatars/2659ba698081265d0480b08161718013.svg
paper_authors.fullname: Yangyang Yu
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paper_authors.user: ShirleyY
paper_authors._id: 67b54a644508bd0617598c27
paper_authors.name: Xueqing Peng
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paper_authors.fullname: Xueqing Peng
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paper_authors.user: Xueqing
paper_authors._id: 67b54a644508bd0617598c28
paper_authors.name: Mingquan Lin
paper_authors.status: admin_assigned
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paper_authors.fullname: Mingquan Lin
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paper_authors.user: mq0051
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paper_authors.name: Kaleb E Smith
paper_authors._id: 67b54a644508bd0617598c2a
paper_authors.name: Xiao-Yang Liu
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paper_authors.name: Jimin Huang
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paper_authors.name: Sophia Ananiadou
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paper_authors.fullname: Sophia Ananiadou
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paper_authors.user: Effoula
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paper_authors.name: Qianqian Xie
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paper_title: FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning
    for Financial Trading
paper_summary: 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.
paper_ai_summary: A unified architecture combining LLMs and RL improves trading performance and other financial tasks through parameter-efficient fine-tuning and policy gradient optimization.
paper_ai_keywords: LLMs multimodal financial data gradient-driven reinforcement learning policy network parameter-efficient fine-tuning policy gradient optimization trading rewards