Distilling LLM Agent into Small Models with Retrieval and Code Tools
Authors: name: Minki Kang, name: Jongwon Jeong, name: Seanie Lee, name: Jaewoong Cho, name: Sung Ju Hwang

Large language models (LLMs) excel at complex reasoning tasks but remain
computationally expensive, limiting their practical deployment. To address
this, recent works have focused on distilling reasoning capabilities into
smaller language models (sLMs) using chain-of-thought (CoT) traces from teacher
LLMs. However, this approach struggles in scenarios requiring rare factual
knowledge or precise computation, where sLMs often hallucinate due to limited
capability. In this work, we propose Agent Distillation, a framework for
transferring not only reasoning capability but full task-solving behavior from
LLM-based agents into sLMs with retrieval and code tools. We improve agent
distillation along two complementary axes: (1) we introduce a prompting method
called first-thought prefix to enhance the quality of teacher-generated
trajectories; and (2) we propose a self-consistent action generation for
improving test-time robustness of small agents. We evaluate our method on eight
reasoning tasks across factual and mathematical domains, covering both
in-domain and out-of-domain generalization. Our results show that sLMs as small
as 0.5B, 1.5B, 3B parameters can achieve performance competitive with next-tier
larger 1.5B, 3B, 7B models fine-tuned using CoT distillation, demonstrating the
potential of agent distillation for building practical, tool-using small
agents. Our code is available at https://github.com/Nardien/agent-distillation.