---
title: "Logic Distillation: Learning from Code Function by Function for Decision-making Tasks"
authors:
  - "Dong Chen"
  - "Shilin Zhang"
  - "Fei Gao"
  - "Yueting Zhuang"
  - "Siliang Tang"
  - "Qidong Liu"
  - "Mingliang Xu"
doi: "10.24963/ijcai.2025/816"
url: "https://ijcai.org/proceedings/2025/816"
pdf: "https://ijcai.org/proceedings/2025/0816.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "7338-7346"
track: "Main Track"
keywords:
  - "Knowledge Representation and Reasoning: KRR: Learning and reasoning"
  - "Natural Language Processing: NLP: Language models"
  - "Multidisciplinary Topics and Applications: MTA: Game playing"
---

# Logic Distillation: Learning from Code Function by Function for Decision-making Tasks

Dong Chen, Shilin Zhang, Fei Gao, Yueting Zhuang, Siliang Tang, Qidong Liu, Mingliang Xu

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 7338-7346. DOI: [10.24963/ijcai.2025/816](https://doi.org/10.24963/ijcai.2025/816)

## Abstract

Large language models (LLMs) have garnered increasing attention owing to their powerful comprehension and generation capabilities. Generally, larger LLMs (L-LLMs) that require paid interfaces exhibit significantly superior performance compared to smaller LLMs (S-LLMs) that can be deployed on a variety of devices. Knowledge distillation (KD) aims to empower S-LLMs with the capabilities of L-LLMs, while S-LLMs merely mimic the outputs of L-LLMs, failing to get the powerful decision-making capability for new situations. Consequently, S-LLMs are helpless when it comes to continuous decision-making tasks that require logical reasoning. To tackle the identified challenges, we propose a novel framework called Logic Distillation (LD). Initially, LD employs L-LLMs to instantiate complex instructions into discrete functions and illustrates their usage to establish a function base. Subsequently, LD fine-tunes S-LLMs based on the function base to learn the logic employed by L-LLMs in decision-making. During testing, S-LLMs will yield decision-making outcomes, function by function, based on current states. Experiments demonstrate that with the assistance of LD, S-LLMs can achieve outstanding results in continuous decision-making tasks, comparable to, or even surpassing, those of L-LLMs. The code and data for the proposed method are provided for research purposes https://github.com/Anfeather/Logic-Distillation.