Editing Files at 1000 Tokens per Second

Frontier models like GPT-4o face challenges with large code edits, displaying issues such as laziness, inaccuracy, and high latency, which disrupt programmers' workflow. These models struggle with accurately editing extensive code segments, often requiring multiple calls and sometimes entering infinite loops. To address these issues, a specialized "fast apply" model was developed, which divides difficult code edits into planning and applying stages, with a chat interface facilitating the planning phase and instant application of changes. This model outperforms GPT-4 and GPT-4o in accuracy and latency, achieving significant speed improvements using a speculative-decoding approach tailored for code edits. Evaluation against prompted models using Claude-3 Opus as a grader reveals that the Claude models perform better than GPT-4 variants, likely due to post-training enhancements. The fast-apply model employs a full-file rewrite strategy instead of diff-based edits, which are problematic for language models due to tokenization and distribution issues. Speculative edits, a custom algorithm, provide a dramatic speed increase, and the model is continuously improved with long-context training and potential knowledge distillation. This development represents a critical advancement in code-generation systems, promising enhanced performance and utility for programmers.

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