[Submitted on 20 Oct 2026]
Title:Speedrunning Relational Foundation Model Pretraining
View a PDF of the paper titled Speedrunning Relational Foundation Model Pretraining, by Gabriel Wedson Mendon\c{c}a de Souza Veloso and 3 other authors
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Abstract:Progress on foundation model pretraining is gated by who can afford a trial, and relational pretraining is no exception. Recipes are developed inside individual labs, reported under private protocols, and do not accumulate. Speedrun leaderboards remove that barrier by decentralizing the search rather than the training run. The problem is fixed, the stack is open, entries are ranked by wall-clock time to a quality target, and one GPU is enough to hold the record. We introduce modded-nanoRT, the first such leaderboard for Relational Transformers. Which database to hold out decides whether the benchmark can resolve improvements at all, so we fix it by calibration, ranking six candidates by how sharply a 45-minute learning curve separates improvement from seed noise. We open the leaderboard with typed trilinear attention, a relational operator that conditions attention scores and values on the foreign key traversed, together with Muon and SIGReg. The recipe reaches the target in 305.63 seconds on one NVIDIA H200, 8.11x faster than the reference implementation, and wins 13 of 15 pairs outside the development database. We will release the benchmark, its frozen artifacts, and the record runs. Our code is available at this https URL.
Comments: 9 pages, 4 figures. Accepted at the CODEC-FM Workshop at NeurIPS 2026
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.28855 [cs.AI]
(or arXiv:2610.28855v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.28855
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Gabriel Veloso [view email] [v1] Tue, 20 Oct 2026 19:29:31 UTC (24 KB)
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