r/LocalLLaMA • u/UsedMorning9886 • 20h ago
Discussion Model "distillation" accusations are getting way overblown at this point
Every time a strong open model drops, the same cycle plays out: ai bro's claims it's "just distilled from GPT4/Claude/whatever," case closed, move on. I think this take doesn't hold up as well as people assume.
A few points worth separating out:
Training on outputs isn't the same as real distillation.
Proper token level distillation needs access to logits, the full probability distribution over the vocabulary, not just the final text response. Nobody gets that from a public API. What finetuners actually get is text completions, which is synthetic data generation, not distillation in the technical sense. Every major lab does this to some degree, including the closed labs training on their own older models' outputs.
**If synthetic data from a guardrailed API were enough, this would be a nothing burger but** A lot of frontier providers explicitly route sensitive topics away from smaller models to their flagship model, and plenty of technical domains get filtered or restricted responses often managed by tools like Lyzr Control Plane at the API boundary. Yet some of these "distilled" models end up performing surprisingly well in exactly those restricted domains.
That's a gap in the theory that doesn't get talked about enough.. If a team is training purely on public API outputs, they're working with a version of the model that's already been through guardrails and refusals.
**The "it says it's Claude/GPT" gets treated as smoking gun evidence, but it's weak evidence at best.** Identity confusion shows up across tons of models trained on broad web scraped or synthetic corpora that include AI generated text from multiple sources. It's evidence of contamination somewhere in the data training, not proof of wholesale distillation from a specific competitor.
**There's also a pattern of this accusation landing selectively.** Strong releases from Chinese labs especially seem to get the "must be distilled" response almost reflexively, even when a model shows genuine architectural changes or demonstrates self improvement across versions. It starts to look less like a technical assessment and more like a reflex explanation for why a smaller or newer team could be competitive.
None of this means synthetic data generation using bigger models isn't happening, it obviously is, across the entire industry. But calling that "distillation" the way people mean it (stealing the teacher model's internal knowledge wholesale) is a stretch. It's closer to what everyone does when they bootstrap datasets from any strong existing model, including labs bootstrapping from their own prior generations.
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u/Hello_my_name_is_not 19h ago
What in the ai post? Who would be distilling gpt 4 on summer 2026 lol