FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning
  for Financial Trading
Authors: name: Guojun Xiong, name: Zhiyang Deng, name: Keyi Wang, name: Yupeng Cao, name: Haohang Li, name: Yangyang Yu, name: Xueqing Peng, name: Mingquan Lin, name: Kaleb E Smith, name: Xiao-Yang Liu, name: Jimin Huang, name: Sophia Ananiadou, name: Qianqian Xie
Organization: The Fin AI

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.