Random Recursive Models
Quick summary
arXiv:2610.00541v1 Announce Type: cross Abstract: Recursive models create computational depth through parameter reuse, offering a parameter-efficient alternative to increasing model size. However, most recursive models repeatedly apply one learned transformation or a prescribed sequence of transformations, restricting computation to a fixed layer order. We introduce the Random Recursive Model (RRM), which maintains a pool of $L$ learned layers and performs $T$ recursive steps by sampling one layer independently with replacement for each example and step. This enables flexible layer reuse while
Key takeaways
- arXiv:2610.00541v1 Announce Type: cross Abstract: Recursive models create computational depth through parameter reuse, offering a parameter-efficient alternative to increasing model size.
- However, most recursive models repeatedly apply one learned transformation or a prescribed sequence of transformations, restricting computation to a fixed layer order.
- We introduce the Random Recursive Model (RRM), which maintains a pool of $L$ learned layers and performs $T$ recursive steps by sampling one layer independently with replacement for each example and step.
Why it matters
“Random Recursive Models” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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