arXiv Machine Learning
Truncate or Not? A Scaling Study of Looped Transformers with Truncated Backpropagation
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How Model Growth, Recursion, and Boundary Operators Influence Scaling Exponents
The paper demonstrates that architectural changes—specifically looped transformers and boundary operators—can alter scaling exponents in pre‑training, yielding exponential performance gains for a given computational budget. Looping, or recursive depth, enables model growth that matches larger models (e.g., a 7.4B looped architecture matching GPT‑3 13B) with significantly less compute, while boundary operators provide additional, though smaller, efficiency improvements. In data‑constrained, multi‑epoch scenarios, increasing loops with scale serves as a useful regularizer, suggesting that deeper computational depth drives compute‑efficiency gains that grow with model size.