RTX 5090로 1.5시간 만에 학습한 소형 트랜스포머가 다수 LLM 능가

I trained a small transformer from scratch in 1.5hrs on a 5090 Beats many LLMs, and scores the same as TRM/HRM This is an upgrade to my previous model Faster, better, cheaper and still open source. Also gets 7% on ARC-2 Discussion on Twitter , Code on github This is the 3rd blog in a series of works on ARC-AGI. Prev: Blog 2 , Blog 1 . Many ppl thought the prev result was impossible. It got attention from top researchers and went viral on X. Eg: Discussions by Lucas Beyer , Jeremy Howard , Rohan Anil , and comments by many others. Why work on this? I think sample efficiency is the most important problem in AI today and I want to solve it. The intention behind this work is to (1) find the limits of sample efficiency when restricted to transformers / today’s deep learning methods and (2) reduce costs so iteration is much faster and cheaper. ARC is a great benchmark to test this: Very few samples (only a 1000 puzzles) in a high dimensional space Its a metalearning benchmark, so each puzzle uses a different rule, with some common concepts Very few priors needed: every concept needed in the eval set is present in the train set It is incredibly easy for humans to solve, and accessible to even poor AI researchers Benchmark is still unsaturated (for data efficiency, ignore LLMs and approaches that use tons of synthetic data or human inductive biases) Next, I’ll work on new research ideas to break these limits. I’ll try to keep costs low so that anyone in the world can work on this. Tech details How does it work? The overall approach is similar to last time ( full technical details here ), but I added a bunch of upgrades. Here’s a quick summary of the approach: Each input-output pair is converted to a sequence of tokens. These sequences are autoregressively trained on by a small transformer. This is done from scratch at test time on both the train set and eval set puzzles (test labels hidden). To enable cross-task learning, each puzzle is given a separate additive embedding (learnt). Since each sequence has two 2D grids, positional are learnt using 3D RoPE embeddings. The sequences are augmented with color and dihedral permutations. During inference, the test inputs are augmented, and the inverse aug is applied on the outputs produced. The 2 most common outputs are submitted (AAIVR). Changes since last time The main goal was to find improvements to the architecture / algorithm that improve the sample efficiency of the model. The biggest increases in scores were due to Modern architecture (SwiGlu instead of GELU, RMSnorm not layernorm, etc.) More data diversity, better shuffling of data scaling up: 8 layers instead of 4, Biggest decreases in cost were due to: Way fewer augmentations (more sample efficient!) AdamW -> Normuon flash attention with varlen training + flex attention kernels for inference A major change is that I don’t train on input tokens anymore. This means the loss function only includes output tokens (which makes the approach supervised). This. performs slightly better 40% $\to$ 44% but I don’t understand why. Perhaps finite model capacity I also increased the training data by adding the non-overlapping tasks from ARC-2. I did this very carefully to ensure no leakage. You can remove the extra data if you don’t like it and it will still score ~40%, but it will need ~double the compute. Context: ARC-2 contains 773 ARC-1 puzzles and 347 new puzzles. Most eval puzzles of ARC-1 are repeated, so if you naively train on ARC-2, then its a dataleak and you will score 100%. I avoid this by carefully filtering out the 773 repeated puzzles (so no leak!) There are many other changes that gave incremental improvements in performance or speed. Find the full list of changes here . Interesting behaviour Since I am no longer training on inputs, this approach is now supervised. What’s weird is that the test loss is now worse, yet it scores better! Also it is more stable and there’s less variance in scores. Many ppl today are working on sample efficiency by aiming for the lowest val loss on a small dataset. I think that’s great, but this points out a failure mode in such an approach I do think the unsupervised style training will be better in some scenarios, and I am evaluating this. Before NorMuon, I tried vanilla Muon. Obviously it trained much faster than AdamW, but the loss (and scores) would loiter at the end instead of converging. I found that cranking down the momentum and/or LR drastically at this point helped, but I didn’t want to make manually changes like this. When I switched to NorMuon, the problem disappeared Ablations The biggest contribution to performance seems to be good representations (3D RoPE + per-task embedding). Training on inputs performs slightly worse -> ~39% Restricting training set to ARC-1+ConceptARC only performs about the same: ~40% Switching from 3D RoPE to 1D drops score to ~24% Removing the per-task embeddings drops score to ~24% Running the model CompressARC style (training from scratch on each task separately, and unsupervised), gives a drops performance down to ~18% CompressARC but supervised gets ~15% How can others contribute? The code is open source. Feel free to modify it and improve score or reduce cost. (Pls don’t increase training data) Try reaching 65% – you won’t need many modifications. Evidence: I took the union of all solved tasks from multiple runs, and got 55%. Also a bunch of other tasks are “almost” solved. Some ideas: RoPE mixes positional and content information, which probably worsens performance. PoPE should perform on par or better. Or maybe invent a new pos embedding The architecture can definitely be modernised further Costs can probably be reduced 10x with handmade GPU code. There are architectural changes that can also do this. Lastly, figure out how to remove data augmentations. (I hate that I used it, ignore everyone who thinks its okay). There are a few obvious ways to do so, but the challenge is keeping training costs low. Misc TBH, I didn’t expect to reach 45% with just the transformer, I thought this would need new ideas. I certainly didn’t expect to reach it at such low costs/flops. The ablations show that a surprising amount of perfomance is retained even without augmentations or synthetic data. Now I’m pretty sure 65% can be reached within the transformer framework I don’t understand why others didn’t figure this out. Its just a transformer with the most obvious representation. This benchmark has been open for 6 years, was high profile, and had a million dollar prize! Maybe researchers underestimate deep learning? Maybe the cost of experimentation was high enough that they couldn’t run ablations properly? Blindsided by LLMs or using harnesses? Appendix Prev criticism/validation on my approach from famous researchers My old result went viral on X and many experienced researchers debated about it, both for and against. Threads by Jeremy , Lucas , Susan , Andreas , Yoav , and many more. I’m listing all the criticisms here with my answers. Training on the eval puzzles is cheating / “training on test” No this is false . “Training on test” specifically means training on the labels of test data. The labels were not trained on. Also, ARC is a metalearning benchmark, so you’re supposed to learn from the eval puzzles. Jargon: ARC has a set of train puzzles and a set of eval puzzles. Each puzzle has example pairs and test pairs. A pair consists of an input grid + output grid. The ARC, the label is only the test pair’s output grid in an eval puzzle . These labels were not trained on. They are hidden. You can delete it beforehand if you wish Training on the inputs of eval puzzles leaks information No, this is false . Such an approach is called transductive reasoning and has been studied since the time of Vapnik. Also, this dogma of ignoring eval inputs doesn’t make sense in a world trying to solve continual learning Other approaches train a metalearning algorithm and then deploy it to learn by running a Co