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    <abstract>Large language models (LLMs) have advanced code generation from single-function tasks to competitive-programming problems, but existing multi-agent solutions either rely on costly large-scale (&gt; 30 B) models or collapse when downsized to small open-source models. We present MapCoder-Lite, a framework for distilling the complex reasoning of large, multi-agent coding systems into a single 7B model. Our contribution is a novel, three-pillar methodology that synergistically generates, refines, and encodes multi-agent knowledge: (i) pass-based trajectory distillation from strong LLMs fixes format fragility in retrieval and reduces failures in debugging, (ii) supervisor-guided correction with global feedback strengthens planning and coding agents, and (iii) agent-wise LoRA fine-tuning delivers memory-efficient specialisation.Comprehensive evaluation on xCodeEval, APPS, and CodeContests shows that MapCoder-Lite more than doubles xCodeEval accuracy (13.2% → 28.3%), eliminates all format failures, while reducing GPU memory and token-generation time by 4\times compared to a 32B model. It also achieves over 10% gains on simpler coding benchmarks, demonstrating broad improvements beyond competitive programming. These results demonstrate that careful agent-wise fine-tuning unleashes high-quality multi-agent coding on a small language model. Our code is publicly available at https://github.com/aiha-lab/MapCoder-Lite.</abstract>
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%0 Conference Proceedings
%T MapCoder-Lite: Distilling Multi-Agent Coding into a Single Small LLM
%A Lee, Woongkyu
%A Cho, Junhee
%A Choi, Jungwook
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F lee-etal-2026-mapcoder
%X Large language models (LLMs) have advanced code generation from single-function tasks to competitive-programming problems, but existing multi-agent solutions either rely on costly large-scale (> 30 B) models or collapse when downsized to small open-source models. We present MapCoder-Lite, a framework for distilling the complex reasoning of large, multi-agent coding systems into a single 7B model. Our contribution is a novel, three-pillar methodology that synergistically generates, refines, and encodes multi-agent knowledge: (i) pass-based trajectory distillation from strong LLMs fixes format fragility in retrieval and reduces failures in debugging, (ii) supervisor-guided correction with global feedback strengthens planning and coding agents, and (iii) agent-wise LoRA fine-tuning delivers memory-efficient specialisation.Comprehensive evaluation on xCodeEval, APPS, and CodeContests shows that MapCoder-Lite more than doubles xCodeEval accuracy (13.2% → 28.3%), eliminates all format failures, while reducing GPU memory and token-generation time by 4\times compared to a 32B model. It also achieves over 10% gains on simpler coding benchmarks, demonstrating broad improvements beyond competitive programming. These results demonstrate that careful agent-wise fine-tuning unleashes high-quality multi-agent coding on a small language model. Our code is publicly available at https://github.com/aiha-lab/MapCoder-Lite.
%U https://aclanthology.org/2026.findings-eacl.346/
%P 6569-6596

Markdown (Informal)

[MapCoder-Lite: Distilling Multi-Agent Coding into a Single Small LLM](https://aclanthology.org/2026.findings-eacl.346/) (Lee et al., Findings 2026)

• MapCoder-Lite: Distilling Multi-Agent Coding into a Single Small LLM (Lee et al., Findings 2026)

ACL

• Woongkyu Lee, Junhee Cho, and Jungwook Choi. 2026. MapCoder-Lite: Distilling Multi-Agent Coding into a Single Small LLM. In Findings of the Association for Computational Linguistics: EACL 2026, pages 6569–6596, Rabat, Morocco. Association for Computational Linguistics.