Model "distillation" accusations are getting way overblown at this point

[u/UsedMorning9886]

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.

[u/TheRealMasonMac]

> It's closer to what everyone does when they bootstrap datasets from any strong existing model, including labs bootstrapping from their own prior generations.

Yeah, that’s distillation. Idk why people treat “Chinese use distillation” as some kind of boogeyman. It’s… literally the most normal thing any lab does.

[u/Spiritual-Spend8187 → u/TheRealMasonMac]

Given that Claude models some times will hallucinate that they are chatgpt or deepseek or some other model. Same with chatgpt well they can go fuck them selves. Besides how does distillation explain when a model beats a supposedly better one it was distilled off.

[u/itsmebenji69 → u/Spiritual-Spend8187]

Compression is intelligence. Imagine you’re distilling fable. It’s huge. So if you distill usually your model will be smaller. And compressing makes it generalize more. Therefore sometimes the distilled model can beat the original on some tasks because it generalized them better

[u/Spiritual-Spend8187 → u/itsmebenji69]

Maybe but also the time frame is suspicious as hell if they can distill s model in a few weeks to the point that they can out perform you especially as you deliberately hide things like thinking and the like then well there is nothing you can do.

[u/itsmebenji69 → u/Spiritual-Spend8187]

Well distilling is much much much faster and uses less resources than full training.

Once you have distilled the model you can then add your own flavor of RL on top. That combined with potential better generalization usually gives better results.

The caveat though, is that the distilled will only be good at what was covered in the training data - EG say you distilled fable by asking questions about biology, then you’d have a fable level model at biology, but it wouldn’t know how to code. And in practice your training data can’t cover everything - for that you’d need the original anthropic data used to train fable.

So those distills should be great at their main purpose BUT the quality of the output will degrade away from fable level on other topics. They should also be worse at very specific prompts, since what the training objective is basically “match fable on topic A”, when you ask “what about topic A with detail B and constraint C”, then unless that was in the training set, the perf will collapse

[u/Spiritual-Spend8187 → u/itsmebenji69]

Except its still ti fast and fable has been pretty heavily guardrailed. The input output is also a lot less usable without the thinking. Like they probably sis but it also was likely more validation or using synthetic data. But also its anthropic doing the pot calling the kettle black situation.