I'd bet "re-based" model ala https://huggingface.co/jxm/gpt-oss-20b-base when instruction-tuned would do same as similarly sized Qwen models.
It's provided the current time together with other 20k sys-prompt tokens, so substantially more diluted influence on the behaviours..?
Folks like this guy hit it on hyperspeed -
https://www.facebook.com/reel/1130046385837121/?mibextid=rS40aB7S9Ucbxw6v
I still remember university teacher explaining how early TV transmission were very often including/displaying ghosts of dead people, especially dead relatives.
As the tech matures from art these phenomena or hallucinations evaporate.
you seem to report one OOM less than this picture in https://alexiglad.github.io/blog/2025/ebt/#:~:text=a%20log%20function).-,Figure%208,-%3A%20Scaling%20for
Link to Induction section on https://www.lesswrong.com/lw/dhg/an_intuitive_explanation_of_solomonoff_induction/#induction seems broken on mobile Chrome, @habryka
I've heard that hypothesis in a review of that blog post of Anthropic, likely by
AI Explained
maybe by
bycloud
.
They've called it "Chekov's gun".
What's your view on sceptic claims about RL on transformer LMs like https://arxiv.org/abs/2504.13837v2 or one that CoT instruction yields better results than <thinking> training?
Not the content I expect labeled AIb Capabilities,
although I see how that'd be vindicated.
By the way, if I write an article about LMs generating SVG, that's a plaintext and if I put an SVG illustration up, that's an image, not a plaintext?
1
My friends(M.K.,he's on Github) honorable aim to establish a term in the AI evals field: The cognitive asymetry, generating-verifying complexity gap for model-as-judge evals.
Various tasks that have a clear intelligence-to-solve vs. intelligence-to-verify-a-solution gap, ie. only X00-B LMs have a shot, but X-B model is strong on verifying are desired.
It fits nicely to the incremental iterative alighnment scaling playbook, I hope.