FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning
  for Financial Trading
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.
67b54a644508bd0617598c21
Guojun Xiong
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2025-02-19T10:14:19.641Z
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Guojun Xiong
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Zhiyang Deng
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2025-02-19T10:14:39.220Z
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Zhiyang Deng
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zdeng10
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Keyi Wang
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Yupeng Cao
admin_assigned
2025-02-19T10:15:07.509Z
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Yupeng Cao
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YupengCao
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Haohang Li
admin_assigned
2025-02-19T10:15:15.689Z
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Haohang Li
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Acatsama
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Yangyang Yu
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2025-02-19T10:15:22.050Z
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Yangyang Yu
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ShirleyY
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Xueqing Peng
admin_assigned
2025-02-19T10:15:29.692Z
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Xueqing Peng
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Xueqing
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Mingquan Lin
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2025-02-19T10:15:52.483Z
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Mingquan Lin
user
mq0051
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Kaleb E Smith
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Xiao-Yang Liu
67b54a644508bd0617598c2b
Jimin Huang
claimed_verified
2025-02-19T09:01:03.181Z
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Jimin Huang
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jiminHuang
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Sophia Ananiadou
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2025-02-19T10:16:09.788Z
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Sophia Ananiadou
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Effoula
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Qianqian Xie
admin_assigned
2025-02-19T10:16:24.340Z
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Xie
user
QianqianXie1994
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning
  for Financial Trading
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.
A unified architecture combining LLMs and RL improves trading performance and other financial tasks through parameter-efficient fine-tuning and policy gradient optimization.
LLMs multimodal financial data gradient-driven reinforcement learning policy network parameter-efficient fine-tuning policy gradient optimization trading rewards