Comment by johndough

I am not arguing that Kimi did not distill Opus or Sonnet (which they almost certainly have, as opposed to Fable or Sol). I am arguing that even if they did not distill, the model could still identify itself as Opus or Sonnet.

That being said, I'd like to point out a few things:

- The second link (to claude-sonnet-4-2025051) had 17k matches, not just 3k.

- grep.app does not index the entirety of GitHub, so the real number of occurrences of model identifiers is even higher. SourceGraph has a larger index, but the number is so high that it exceeds their result limit of 10k: https://sourcegraph.com/search?q=claude-opus-4-5-20251101&pa...

- 1000 occurrences are already plenty for memorization. Here is a paper that shows over 80% "extractability" with 1000 occurrences for a 6B model. It also shows that extractability scales with model size: https://arxiv.org/pdf/2202.07646

- LLMs have a "mosaic memory", which means that they can combine information from different parts of the training data. https://arxiv.org/pdf/2405.15523 For example, one piece of training data might mention "I am Opus", another might mention "Opus (claude-opus-4-5-20251101)" and another 10000 "claude-opus-4-5-20251101", where the later occurrences strengthen the generation probability of "I am Opus".

- RL can change the model identity easily, so all of the above may not matter at all.