← Back to all news
arXiv AI By Prateek Kumar Sikdar

LayerRoute: Adaptive Layer-Skipping with LoRA-Preserved Quality for Efficient LLM Inference

Read the original on arXiv AI →

LayerRoute is a parameter‑efficient technique that enables adaptive skipping of transformer layers in large language models. It adds a lightweight per‑layer router (~21.5K parameters) and LoRA adapters (rank 8, ~1.08M parameters) to each of the 24 blocks in Qwen2.5‑0.5B‑Instruct, training them jointly with a gate‑regularized language‑modeling objective. Across ten independent runs, the method consistently identifies nine middle layers as skip‑eligible, achieves a verified wall‑clock speedup of 1.02x–1.06x, and improves perplexity by an average of 1.16 points, while the router’s decisions vary per input, confirming genuine adaptive behavior.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Sep 1

A rigor-matched audit of periodic-step layer skipping for efficient llm inference: conflayers versus swift, with a supplemental analysis of trained routing alternatives

The paper presents a rigor‑matched audit comparing two periodic‑step, search‑based layer‑skipping methods for efficient large language model inference: a confidence‑gated early‑exit baseline (ConfLayers) and a self‑speculative decoding approach (SWIFT). Across two Qwen2.5 model scales and tasks (GSM8K reasoning and CNN/DailyMail summarization), SWIFT consistently outperforms ConfLayers in accuracy and, after separating search overhead, achieves faster true inference speed in most settings. The study also evaluates two trained‑routing methods (LayerRoute and LayerDrop), finding modest speedups but significantly lower accuracy, especially for LayerRoute on GSM8K at 1.5B.

By Prateek Kumar Sikdar, Arpan Ghosh
llmsnlpefficiency
More like this →
arXiv AI
Sep 7

ACE: Adaptive Calibration-Free Expert Skipping for MoE-based LLMs

ACE introduces a training‑free, calibration‑free framework for adaptive expert skipping in Mixture‑of‑Experts LLMs. It combines a Global Spectral Proxy that estimates global transformation capacity with a Router‑Conditioned Refinement that builds expert‑specific direction prototypes, enabling the model to skip low‑contribution experts while always keeping the top‑1 expert. Offline computation of expert statistics leaves only lightweight table lookups during inference, and experiments on three MoE‑based LLMs show ACE outperforms static and dynamic baselines, especially at high skipping ratios.

By Zukang Xu, Zhixiong Zhao, Xing Hu, Jiangyong Yu, Houji Wen, Jun Li, Zhe Jiang, Dawei Yang
llmsbenchmarks
More like this →