FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading
This addresses the need for better decision-making in financial trading using AI, but it appears incremental as it combines existing methods (LLMs and RL) in a novel way for a specific domain.
The authors tackled the problem of LLMs struggling with multi-step, goal-oriented scenarios in interactive financial markets like trading by proposing FLAG-Trader, a unified architecture integrating linguistic processing with gradient-driven RL policy optimization, which enhanced LLM performance in trading and improved results on other financial-domain tasks.
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 \textsc{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.