arXiv AI
Sep 3

CHASE: Cache-Hole-Adapted Skip Exit for Looped State-Space Language Models

The paper introduces CHASE, a cache‑hole‑adapted skip‑exit mechanism for looped state‑space language models, specifically Looped Mamba and Looped Hybrid Mamba‑Transformer. It shows that looping these architectures improves performance on controlled reasoning tasks and remains competitive in pre‑training benchmarks while using fewer distinct parameters. The cache‑hole adaptation allows selective skipping of recurrent steps during inference, maintaining perplexity close to full computation and achieving significant speedups.

By Zhenxuan Yu, Takeshi Kojima, Yutaka Matsuo, Yusuke Iwasawa