The October 14, 2025 paper is an excerpt from a research paper introducing **Dr.LLM**, a novel, retrofittable framework designed to improve the efficiency and accuracy of Large Language Models (LLMs). The core problem addressed is the wasteful static processing where every input token passes through all transformer layers, which the authors solve by equipping frozen, pretrained LLMs with **lightweight, per-layer routers**. These routers dynamically decide whether to **skip, execute, or repeat** a layer, allocating compute based on input difficulty. The routers are trained efficiently using **explicit supervision generated offline by Monte Carlo Tree Search (MCTS)**, which finds optimal layer configurations that either maintain or boost accuracy while adhering to a compute budget. Empirically, Dr.LLM demonstrates **significant accuracy improvements** (up to +4.0%p on reasoning tasks like DART) and **substantial layer savings** during inference, outperforming prior adaptive-depth methods without requiring costly architectural changes or large-scale retraining.

Source:

https://arxiv.org/pdf/2510.12773