Deep Dive: Breaking the "Distillation" Stigma in Model Training
max_paperclips · x · 2026-07-17
The author points out that using outputs from Chinese models as synthetic data is now an industry norm and highly valuable. For US open-source labs, embracing this "distillation" shortcut can significantly accelerate R&D.
The author argues that obsessing over the "distillation" label is pointless because model training fundamentally just needs high-quality tokens. Rather than outsourcing to data factories for initial conversational data, leveraging existing model outputs makes more sense. However, this doesn't mean teams can slack off, because the real moats and hard work in AI development lie in building verifiers, setting up environments, executing robust evaluations, and having technical taste. Furthermore, distillation cannot replace the core mechanics during the current reinforcement learning (RL) phase.
Related event: Analysis: Chinese AI Labs Leverage Distillation to Compete(3 posts)→
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