Post-training shift makes model distillation nearly impossible to detect, researcher argues
maksym_andr · x · 2026-09-15
The author argues the compute shift from pretraining to post-training explains why open-weight models keep closing the gap: post-training gains are far easier to distill.
Key points:
- Access to a capable model in a domain lets you generate RL environments for that domain, effectively distilling it;
- Such distillation is hard to detect because generating environments looks like legitimate use;
- Only a large volume of environment-generation requests might give it away, but detection is post-hoc and multi-account evasion is trivial;
- Conclusion: preventing distillation in full generality seems nearly impossible.
Related event: Compute shift to post-training narrows open-closed model gap(3 posts)→
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