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About this paper
Yubo Wang, Jiarong Liang, Yuxuan Zhang, Xuye Liu, Cong Wei, Yuyu Zhang, Ping Nie, Wenhu Chen
Researchers from the University of Waterloo and collaborators introduced a "function-aware fill-in-the-middle" (FIM) mid-training stage to better align code large language models with the iterative reasoning patterns of coding agents. The method improved agent performance on SWE-Bench benchmarks by up to +5.30 points while mitigating capability erosion on non-agentic tasks, demonstrating cross-domain transfer to non-coding tool use.
Key insights
- A structural isomorphism exists between a coding agent's action-observation loop and a function call site's constituent parts (pre-call context, call, return value, downstream usage).
- Training models to reason bidirectionally about function-level dependencies can instill a strong inductive bias directly transferable to agentic reasoning.
- A dedicated mid-training stage, explicitly aligning base models with agentic conditioning, is effective for improving performance and preserving broad model capabilities.
Summary by alphaXiv.
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