Hi! Author here. Surprised to see this on HN now. Happy to answer any questions!

Some context about this:

- This is NOT an LLM. its a small ar transformer trained from scratch. One of the points was that extremely complex problems can be tackled without LLMs

- Till the v1 of this result, this benchmark was only scaled by LLMs or their finetunes (ofc w enormous training costs). Other attempts performed okayish but used v complex architectures or extremely high amounts of training compute. No one expected a simple AR transformer to perform this well, at this low cost and w these few training samples.

- Sample Efficiency is one of the most important unsolved problems today in AI. That's what I was targetting with this work. We know it is easy to increase SE by increasing compute/params, so it was important to constrain cost as much as possible (also why OpenAI's Parameter Golf had fixed compute and why Modded NanoGPT is considered very sample efficient)

- Can the perf be improved? Yes but the competition is ongoing so can't talk about it

- Personally I think today's frontier models can be beat by training from scratch. Haven't proved this yet tho

- Fun: I was new to ML when I posted this first (dec '25). I basically used ARC as a way to learn ML