@inproceedings{yin-etal-2026-repodistill,
title = "{R}epo{D}istill: Distilling Repository Knowledge through Compression-Aware Budget Allocation and Policy Optimization",
author = "Yin, Xin and
Ding, Zixiang and
Zhang, Yiang and
Wang, Qiang and
Wang, Rui and
Ni, Chao and
Cui, Zhe",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.217/",
pages = "4425--4443",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models (LLMs) have achieved strong performance on many code-related tasks, yet they still struggle with repository-level scenarios where reasoning depends on long, noisy, and structurally complex contexts. While existing retrieval methods, including both similarity-based and graph-based approaches, can identify relevant code snippets, they often retrieve excessive contexts that intensify the ``lost-in-the-middle'' phenomenon and dilute model attention with redundant contexts. To address this, we present RepoDistill, a novel framework that integrates retrieval with learned budget allocation for fine-grained context compression. RepoDistill first employs a plug-and-play lightweight GraphRAG to retrieve context that follows logical flows. It then applies Compression-Aware Budget Allocation guided by Compression-Aware Policy Optimization, which formulates context management as a multi-step decision problem and learns allocation policies for contexts. Experiments show that RepoDistill outperforms baselines, achieving gains of up to +7.00 on SWE-QA, +24.4{\%} on CoderEval, and +0.25 on LongCodeU. Furthermore, a compact 4B-parameter model trained with RepoDistill can serve as an effective context compressor for closed-source LLMs, reducing input tokens by up to 66{\%} while maintaining comparable performance. We release our code at https://anonymous.4open.science/r/RepoDistill-12B0."
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<abstract>Large Language Models (LLMs) have achieved strong performance on many code-related tasks, yet they still struggle with repository-level scenarios where reasoning depends on long, noisy, and structurally complex contexts. While existing retrieval methods, including both similarity-based and graph-based approaches, can identify relevant code snippets, they often retrieve excessive contexts that intensify the “lost-in-the-middle” phenomenon and dilute model attention with redundant contexts. To address this, we present RepoDistill, a novel framework that integrates retrieval with learned budget allocation for fine-grained context compression. RepoDistill first employs a plug-and-play lightweight GraphRAG to retrieve context that follows logical flows. It then applies Compression-Aware Budget Allocation guided by Compression-Aware Policy Optimization, which formulates context management as a multi-step decision problem and learns allocation policies for contexts. Experiments show that RepoDistill outperforms baselines, achieving gains of up to +7.00 on SWE-QA, +24.4% on CoderEval, and +0.25 on LongCodeU. Furthermore, a compact 4B-parameter model trained with RepoDistill can serve as an effective context compressor for closed-source LLMs, reducing input tokens by up to 66% while maintaining comparable performance. We release our code at https://anonymous.4open.science/r/RepoDistill-12B0.</abstract>
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%0 Conference Proceedings
%T RepoDistill: Distilling Repository Knowledge through Compression-Aware Budget Allocation and Policy Optimization
%A Yin, Xin
%A Ding, Zixiang
%A Zhang, Yiang
%A Wang, Qiang
%A Wang, Rui
%A Ni, Chao
%A Cui, Zhe
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yin-etal-2026-repodistill
%X Large Language Models (LLMs) have achieved strong performance on many code-related tasks, yet they still struggle with repository-level scenarios where reasoning depends on long, noisy, and structurally complex contexts. While existing retrieval methods, including both similarity-based and graph-based approaches, can identify relevant code snippets, they often retrieve excessive contexts that intensify the “lost-in-the-middle” phenomenon and dilute model attention with redundant contexts. To address this, we present RepoDistill, a novel framework that integrates retrieval with learned budget allocation for fine-grained context compression. RepoDistill first employs a plug-and-play lightweight GraphRAG to retrieve context that follows logical flows. It then applies Compression-Aware Budget Allocation guided by Compression-Aware Policy Optimization, which formulates context management as a multi-step decision problem and learns allocation policies for contexts. Experiments show that RepoDistill outperforms baselines, achieving gains of up to +7.00 on SWE-QA, +24.4% on CoderEval, and +0.25 on LongCodeU. Furthermore, a compact 4B-parameter model trained with RepoDistill can serve as an effective context compressor for closed-source LLMs, reducing input tokens by up to 66% while maintaining comparable performance. We release our code at https://anonymous.4open.science/r/RepoDistill-12B0.
%U https://aclanthology.org/2026.findings-acl.217/
%P 4425-4443
Markdown (Informal)
[RepoDistill: Distilling Repository Knowledge through Compression-Aware Budget Allocation and Policy Optimization](https://aclanthology.org/2026.findings-acl.217/) (Yin et al., Findings 2026)
ACL
- Xin Yin, Zixiang Ding, Yiang Zhang, Qiang Wang, Rui Wang, Chao Ni, and Zhe Cui. 2026. RepoDistill: Distilling Repository Knowledge through Compression-Aware Budget Allocation and Policy Optimization. In Findings of the Association for Computational Linguistics: ACL 2026, pages 4425–4443, San Diego, California, United States. Association for Computational Linguistics.