AdderBoard
Challenge: Build the smallest transformer that can add two 10-digit numbers with >= 99% accuracy on a held-out 10K test set.
This started with Addition Under Pressure, where I gave Claude Code and Codex the same prompt: train the smallest possible transformer that can do 10-digit addition with at least 99% accuracy. Claude Code came back with 6,080 parameters and Codex came back with 1,644. The community has since pushed this dramatically lower.
Maintained by Dimitris Papailiopoulos (@dimitrispapail).
We track two categories:
Trained — weights learned from data by any training algorithm (SGD, Adam, evolutionary search, etc.). The algorithm must be generic — it should work with any model and dataset, not just this specific problem. This encourages creative ideas around data format, tokenization, curriculum learning, and architecture search.
— weights learned from data by any training algorithm (SGD, Adam, evolutionary search, etc.). The algorithm must be generic — it should work with any model and dataset, not just this specific problem. This encourages creative ideas around data format, tokenization, curriculum learning, and architecture search. Hand-coded — weights set analytically. This is a constructive proof that the architecture can represent addition, regardless of whether SGD would find it.
Both are valid. Both are interesting.
Leaderboard
Hand-Coded Weights (Constructive Proofs)
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