arXiv Computer Vision
Oct 16

Decision-FLIP: Budget-Free Structured Pruning for Gaussian Occupancy Models

Decision‑FLIP is a budget‑free pruning framework for Gaussian occupancy models that scores each persistent Gaussian row by how many voxel decisions change when the row is removed. It uses a guarded Otsu split on the log‑score distribution to select survivors without a target, then compiles the survivors into a smaller checkpoint, reducing bank size and latency. Across multiple model‑dataset settings, the method removes 12–79% of rows, cuts latency by 7–45%, and saves up to 8.0% of whole‑model compute while keeping semantic mIoU within 0.18 points of the original.

By Jingxing Li, Anurag Paul, Subhasis Das, Cindy Xu
arXiv AI
Sep 7

Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference

The paper demonstrates that layer dropout, also known as stochastic depth, can be effectively used in state‑of‑the‑art large language model (LLM) training. By optimizing the layer distribution, schedule, and optimizer settings, the authors show that layer dropout can reduce training loss while saving up to 25 % of training FLOPs. Additionally, layer dropout enables post‑training optimizations such as early exit and self‑speculative decoding, achieving up to 1.5× inference speedup with negligible accuracy loss across models ranging from 271 M to 8.2 B parameters and datasets up to 160 B tokens.

By Mostafa Elhoushi, Alex Pretko, Nolan Dey, Bin Claire Zhang, Gavia Gray, Gurpreet Gosal, Abdulrahman Mahmoud, Shane Bergsma, Joel Hestness