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

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

Read the original on arXiv AI →

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.

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.

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