# 37% on ARC-AGI-2 Kaggle Without LLMs > Source: > Published: 2026-11-12 00:58:20+00:00 Mithil Vakde on X: "I got 37% on ARC-AGI-2 Kaggle without LLMs! 100M looped transformer trained from scratch - Likely highest score without an LLM - Peaked at 6th place - Beats last year's winning LLM - Looping, added in the last week: 33% → 35% → 37% thanks to @arcprize @fchollet @mikeknoop and @GregKamradt for the competition! Link: https://t.co/iDBUrHhmdI (writeup for the paper track)" I got 37% on ARC-AGI-2 Kaggle without LLMs! 100M looped transformer trained from scratch - Likely highest score without an LLM - Peaked at 6th place - Beats last year's winning LLM - Looping, added in the last week: 33% → 35% → 37% thanks to @arcprize@fchollet@mikeknoop and @GregKamradt for the competition! Link: mvakde.github.io/blog/looped-md… (writeup for the paper track) I got 37% on ARC-AGI-2 Kaggle without LLMs! 100M looped transformer trained from scratch - Likely highest score without an LLM - Peaked at 6th place - Beats last year's winning LLM - Looping, added in the last week: 33% → 35% → 37% thanks to @arcprize@fchollet@mikeknoop and @GregKamradt for the competition! Link: mvakde.github.io/blog/looped-md… (writeup for the paper track) What's new and different: Looping the middle layers All demo pairs in context Hub-and-spoke sparse attention (to fit in 4xL4s) Custom synthetic data (inspired by NVARC by @JFPuget and Ivan Sorkin) Looping is a huge win: At equal depth, it beat my non-looped stacks with up to 4× the params, by 2.3–7.6% (even at equal depth) Even sub 1M models perform very well! Room for growth -- I ran out of time, so final model is severely undertrained. Performance can go much higher How does the looping differ from HRM/TRM: 1) They claim 7M/25M params, but actually train 430M+ params. mdlARC beats their scores at TRUE 1M params 2) mdlARC is autoregressive (1 pixel at a time), so justification for looping is MDL. HRM/TRM use loops to rewrite drafts of the whole answer (diffusion like) 3) No hierarchical looping needed. Didnt help at all 4) MUCH cheaper and faster to train Why do it without an LLM? Because I care about sample efficiency. LLMs see so much data, ARC puzzles included, that you can't measure it. We now know that with enough data, ARC-1/2 become trivial So the interesting question is how far a model gets on a few ARC puzzles alone 44% on ARC-AGI-1 in 67 cents! Trained from scratch in 2hrs on a 5090 Matches TRM, beats HRM and is way faster & cheaper No recursion, just a transformer Also, 7% on ARC-2 🧵