Skip a Layer or Loop It? Learning Program-of-Layers in LLMs
Large language models (LLMs) perform inference by following a fixed depth and order, non-recurrent execution of all layers. We reveal the wide existence of training-free, flexible, dynamic program-of-layers (PoLar), where pretrained layers can be packed as modules and then skipped or looped to form a customized program for each input. For most inputs, substantially shorter program executions can achieve the same or better accuracy, while incorrect predictions of the original LLM can be corrected by alternative programs with fewer layers. These observations indicate that inference admits multiple valid latent computations beyond the standard forward pass. To efficiently achieve PoLar in pr...
| Authors | Ziyue Li, Yang Li, Tianyi Zhou |
|---|---|
| Primary category | cs.LG |
| Published | 4 June 2026 |
| Last updated at source | 8 August 2026 |
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