Speedrunning Relational Foundation Model Pretraining

The paper introduces Speedrunning Relational Foundation Model Pretraining, a new leaderboard called modded‑nanoRT for Relational Transformers that decentralizes the search for efficient pretraining recipes. By calibrating the benchmark and ranking candidates based on a 45‑minute learning curve, the authors demonstrate a recipe that achieves the target in 305.63 seconds on a single NVIDIA H200—8.11× faster than the reference implementation—and wins 13 of 15 comparisons outside the development database. The authors plan to release the benchmark, frozen artifacts, and record runs, with code available on GitHub.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• benchmarks