To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing

TL;DR

This paper introduces structure-aware diff formats and an adaptive editing strategy for LLM-based code editing, significantly improving efficiency and maintaining accuracy in long-code editing tasks.

Contribution

It proposes BlockDiff, FuncDiff, and AdaEdit, novel methods that enhance code editing efficiency by leveraging structured diff formats and adaptive token-efficient strategies.

Findings

AdaEdit reduces latency and cost by over 30% on long-code editing tasks.

Structure-aware diff formats improve generation naturalness and accuracy.

Adaptive format selection maintains full-code accuracy while enhancing efficiency.

Abstract

Large Language Models (LLMs) are increasingly used for code editing, yet the prevalent full-code generation paradigm suffers from severe efficiency bottlenecks, posing challenges for interactive coding assistants that demand low latency and cost. Despite the predominant focus on scaling model capabilities, the edit format itself has been largely overlooked in model training. In this paper, we begin with a systematic study of conventional diff formats and reveal that fragile offsets and fragmented hunks make generation highly unnatural for LLMs. To address it, we introduce BlockDiff and FuncDiff, two structure-aware diff formats that represent changes as block-level rewrites of syntactically coherent units such as control structures and functions. Furthermore, we propose AdaEdit, a general adaptive edit strategy that trains LLMs to dynamically choose the most token-efficient format between a given diff format and full code. Extensive experiments demonstrate that AdaEdit paired with structure-aware diff formats consistently matches the accuracy of full-code generation, while reducing both latency and cost by over 30% on long-code editing tasks.