Market making (MM) through Reinforcement Learning (RL) has attracted significant attention in financial trading. With the development of Large Language Models (LLMs), more and more attempts are being made to apply LLMs to financial areas. A simple, direct application of LLM as an agent shows significant performance. Such methods are hindered by their slow inference speed, while most of the current research has not studied LLM distillation for this specific task. To address this, we first propose the normalized fluorescent probe to study the mechanism of the LLM's feature. Based on the observation found by our investigation, we propose Cooperative Market Making (CMM), a novel framework that decouples LLM features across three orthogonal dimensions: layer, task, and data. Various student models collaboratively learn simple LLM features along with different dimensions, with each model responsible for a distinct feature to achieve knowledge distillation. Furthermore, CMM introduces an H\'{a}jek-MoE to integrate the output of the student models by investigating the contribution of different models in a kernel function-generated common feature space. Extensive experimental results on four real-world market datasets demonstrate the superiority of CMM over the current distillation method and RL-based market-making strategies.

翻译：通过强化学习（RL）进行做市（MM）在金融交易领域引起了广泛关注。随着大语言模型（LLMs）的发展，越来越多的尝试将LLMs应用于金融领域。直接将LLM作为智能体的简单应用显示出显著性能，但此类方法受限于其缓慢的推理速度，而当前大多数研究尚未针对此特定任务探索LLM的蒸馏技术。为解决这一问题，我们首先提出归一化荧光探针来研究LLM特征的作用机制。基于我们研究发现的观察结果，我们提出了协同做市（CMM）——一种新颖的框架，将LLM特征沿三个正交维度（层、任务和数据）进行解耦。多个学生模型协作学习不同维度的简单LLM特征，每个模型负责学习特定特征以实现知识蒸馏。此外，CMM引入了Hájek-MoE模块，通过在核函数生成的公共特征空间中评估不同模型的贡献度，来整合各学生模型的输出。在四个真实市场数据集上的大量实验结果表明，CMM在现有蒸馏方法和基于RL的做市策略中均表现出优越性。