Training Language Models on Synthetic Edit Sequences Improves Code
  Synthesis
Authors: name: Ulyana Piterbarg; name: Lerrel Pinto; name: Rob Fergus
Published: 2024-10-03 17:57:22

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.