用特征分解让小模型学大模型的交易能力，速度更快效果更好。
Two Heads are Better than One: Distilling Large Language Model Features Into Small Models with Feature Decomposition and Mixture
- 将大模型特征按层、任务、数据三维度解耦，由多个小模型协作学习
- 在4个真实市场数据集上，性能超越现有蒸馏方法和强化学习策略
- 适合需要快速推理的金融交易场景，尤其关注模型轻量化落地
通过强化学习进行做市（MM）在金融交易中受到广泛关注。随着大语言模型（LLMs）的发展，越来越多尝试将其应用于金融领域。直接将LLM作为代理的方法已显示出显著性能，但其推理速度慢，且当前研究大多未针对该任务开展LLM蒸馏。为此，我们首先提出归一化荧光探针，用于研究LLM特征机制。基于发现，提出协同做市（CMM）框架，将LLM特征沿层、任务、数据三个正交维度解耦，多个学生模型分别学习不同维度的简单特征，实现知识蒸馏。此外，CMM引入Hájek-MoE，通过核函数生成的公共特征空间融合学生模型输出。在四个真实市场数据集上的大量实验表明，CMM优于现有蒸馏方法和基于RL的做市策略。
原文摘要 · Abstract (English)
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 Hájek-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.
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