Editing vs. Note-Taking for AI Fine-Tunes
Daniel wants a daily AI assistant that writes like him, and he's weighing two ways to get the training data: hand-editing the model's responses, or leaving written notes about what to change. This episode unpacks the research behind both — user-edit fine-tuning (Gao et al., NeurIPS 2024) and critique-and-revise (Jin et al., Amazon) — and asks which actually produces better stylistic results. The evidence is genuinely split: critique-and-revise wins on reasoning benchmarks and data efficiency, while edit-based training unifies supervised, preference, and cost signals from a single gesture…
The skinny
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