> There are about 936 tokens with very low L2 norm, centered at about 2. This likely means that they did not occur in the training process of GPT-oss and were thus depressed by some form of weight decay.

Afaik embedding and norm params are excluded from weight decay as standard practice. Is this no longer true?

E.g., they exclude them in minGPT: https://github.com/karpathy/minGPT/blob/37baab71b9abea1b76ab...

Is there any work on reverse engineering LLMs, especially the closed source API ones? For example, how can we learn about the data used in Claude Sonnet 4.5 training?

And more tricky but as important, is there any work on extrapolating the pretrained model AFTER it's RLHF'd? For example, what kinds of biases did exist in gpt-4o before it was unbiased?

Do biases go away completely or they just get suppressed down deep in the model's "mind"?

Maybe I'm misinterpreting, but the article seems (?) to be implying there's something scandalous about OpenAI training an adult websites.

I find that odd. Would anyone be surprised to know that Google indexes adult websites, and ranks them in its search algorithm? If not, what is the difference for an LLM?

Many of the crude translations of those Chinese phrases are way off to the point that it fails to understand the meaning, which makes me think the data in those matrices is inaccurate as well. The author really needs to ask a native Chinese speaker with experience in ... searching explicit content to proofread the article and examine the results.