Paper page - FastContext: Training Efficient Repository Explorer for Coding Agents

Source: https://huggingface.co/papers/2606.14066

Abstract

FastContext separates repository exploration from code solving in LLM agents using specialized exploration models that reduce token consumption and improve resolution rates.

Large Language Model(LLM)coding agentshave achieved strong results on software engineering tasks, yetrepository explorationremains a major bottleneck: locating relevant code consumes substantial token budget and pollutes the agent’s context with irrelevant snippets. In most agents, the same model explores the repository and solves the task, leaving exploratory reads and searches in the solver’s history. We present FastContext, a dedicatedexploration subagentthat separatesrepository explorationfrom solving. Invoked on demand, FastContext issues paralleltool callsand returns concise file paths and line ranges as focused context. FastContext is powered byspecialized exploration modelsspanning 4B--30B parameters. We bootstrap them from strongreference-model trajectoriesand refine them withtask-grounded rewardsfor broadfirst-turn search,multi-turn evidence gathering, andprecise citation generation. AcrossSWE-bench Multilingual,SWE-bench Pro, andSWE-QA, integrating FastContext intoMini-SWE-Agentimproves end-to-end resolution rates up to 5.5\% while reducing coding-agent token consumption up to 60\%, with marginal overhead. These results show thatrepository explorationcan be separated from solving and handled effectively by specialized models. Code and data: https://github.com/microsoft/fastcontext

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