Training Language Models on Synthetic Edit Sequences Improves Code Synthesis

• URL: http://arxiv.org/abs/2410.02749v2
• Date: Tue, 15 Oct 2024 03:42:01 GMT
• Title: Training Language Models on Synthetic Edit Sequences Improves Code Synthesis
• Authors: Ulyana Piterbarg, Lerrel Pinto, Rob Fergus,
• Abstract summary: Large language models (LLMs) autoresourcedly synthesize programs in a single pass. We develop a synthetic data generation algorithm called LintSeq to generate high-quality code edit data. We show that edit sequence finetuned models produce more diverse programs than baselines synthesis.
• Score: 33.13471417703669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Software engineers mainly write code by editing existing programs. In contrast, large language models (LLMs) autoregressively synthesize programs in a single pass. One explanation for this is the scarcity of open-sourced edit data. While high-quality instruction data for code synthesis is already scarce, high-quality edit data is even scarcer. To fill this gap, we develop a synthetic data generation algorithm called LintSeq. This algorithm refactors existing code into a sequence of code edits by using a linter to procedurally sample across the error-free insertions that can be used to sequentially write programs. It outputs edit sequences as text strings consisting of consecutive program diffs. To test LintSeq, we use it to refactor a dataset of instruction + program pairs into instruction + program-diff-sequence tuples. Then, we instruction finetune a series of smaller LLMs ranging from 2.6B to 14B parameters on both the re-factored and original versions of this dataset, comparing zero-shot performance on code synthesis benchmarks. We show that during repeated sampling, edit sequence finetuned models produce more diverse programs than baselines. This results in better inference-time scaling for benchmark coverage as a function of samples, i.e. the fraction of problems "pass@k" solved by any attempt given "k" tries. For example, on HumanEval pass@50, small LLMs finetuned on synthetic edit sequences are competitive with GPT-4 and outperform models finetuned on the baseline dataset by +20% (+/-3%) in absolute score. Finally, we also pretrain our own tiny LMs for code understanding. We show that finetuning tiny models on synthetic code edits results in state-of-the-art code synthesis for the on-device model class. Our 150M parameter edit sequence LM matches or outperforms code models with twice as many parameters, both with and without repeated sampling, including Codex and AlphaCode.

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