alt Hacker NewsThere's an implication that other companies are improving because they're scraping Anthropic, not because they're investing in better architecture, compute efficiency, or their own synthetic data pipelines. I often see Chinese labs' progress dismissed as "they just distilled Anthropic" and I find it hard to reconcile that with all of the interesting research and open-source tooling that they release.
Is there actually that much capability transfer from non-logit-matched distillation, or is Anthropic just another unwilling source of data?
There is, in fact, "that much capability transfer from non-logit-matched distillation".
Even the early papers on distillation techniques found that surprisingly small distillation datasets can improve task performance noticeably on some specific task types - and that valuable adaptations like SFT/RLHF instruction following can be distilled from one-hot non-logit traces.
A big part of what distillation really gets you is: paving over the mismatch between pre-training and final performance. A base model is trained to spit out fitting text, but not to instruction follow, reason autoregressively, self-check or use tool calls - like an AI has to. There is transfer straight from the "text prediction" pre-training objective, and pre-training sets the foundation for all that follows - but the capabilities you get "out of the box" with it are often unrefined and fragile. Which makes some sense - internet text doesn't often include raw chain-of-thought autoregressive reasoning. It's not the kind of thing humans tend to write.
Reasoning traces? They let an AI learn proven techniques and adaptations directly, from an AI that was already taught "how to be an AI" in other ways.
It's why this kind of distillation typically plugs into mid-training and post-training, not pre-training.
Now, I'm not saying that all Chinese companies do is eat tokens, distill and lie. That just isn't the case. They developed or refined numerous training techniques and architectural adaptations - like deep fusion for high performance visual input, RLVR with GRPO, trunked MoE, storage-efficient and bandwidth-efficient attention formulations, or residual routing techniques like AttnRes. Some of those are used widely now, and some are still on the uptake but show good promise.
But Chinese labs are enjoying massive efficiency gains from being able to distill from the frontier instead of doing things the hard way. It's a leg up. It lets them put their supply of R&D effort and RL compute elsewhere. They wouldn't be nearly as advanced if they couldn't do it.