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LoopICL introduces a novel architecture that decouples parameter count from computational depth by looping a single transformer block, significantly reducing redundancy in tabular foundation models. This approach allows for efficient trade-offs between inference cost and performance without sacrificing accuracy on standard benchmarks like TabArena. The work highlights the potential of recurrent designs to optimize resource usage while maintaining competitive predictive power against gradient-boosted trees and existing foundation models.
LoopICL: Looping a single transformer block to solve tabular tasks
arXiv:2609.36108v1 Announce Type: new Abstract: Tabular foundation models using in-context learning have recently surpassed gradient-boosted trees on predictive tabular tasks. However, recent mechanistic insights suggest that parameters in these models are largely redundant. We introduce LoopICL, a looped transformer whose core design decouples parameter count from computational depth. LoopICL consists of a single block, processing data through two coupled streams: a cell stream capturing per-cell feature representations and a row stream capturing in-context example representations, jointly refined through within-column and cross-column attention. During pre-training, we vary loop counts, allowing the block to be unrolled for a varying number of iterations at test-time and use a learned exit-gate to automatically exit. In its standard setting, LoopICL performs competitively with TabICLv2 on TabArena and TALENT at the same computational cost (FLOPs), while using nearly 90% fewer parameters. Furthermore, its recurrent design enables users to also trade off inference cost and performance, providing a resource-aware TFM.
Abstract:Tabular foundation models using in-context learning have recently surpassed gradient-boosted trees on predictive tabular tasks. However, recent mechanistic insights suggest that parameters in these models are largely redundant. We introduce LoopICL, a looped transformer whose core design decouples parameter count from computational depth. LoopICL consists of a single block, processing data through two coupled streams: a cell stream capturing per-cell feature representations and a row stream capturing in-context example representations, jointly refined through within-column and cross-column attention. During pre-training, we vary loop counts, allowing the block to be unrolled for a varying number of iterations at test-time and use a learned exit-gate to automatically exit. In its standard setting, LoopICL performs competitively with TabICLv2 on TabArena and TALENT at the same computational cost (FLOPs), while using nearly 90% fewer parameters. Furthermore, its recurrent design enables users to also trade off inference cost and performance, providing a resource-aware TFM.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2609.36108 [cs.LG]
(or arXiv:2609.36108v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.36108 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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