← Back to all news
arXiv Machine Learning By Hanxin Zhang

TurboMamba: Faster Inference, Faster Training, and Better Prediction with Lexical Memory

Read the original on arXiv Machine Learning →

TurboMamba is a new language‑model architecture that combines a global Mamba‑3 model with a small Transformer to achieve faster inference, faster training, and better token prediction. It introduces three lightweight enhancements—direct access to the previous block’s last token, a small correction to next‑token scores, and a learned mixture with recent‑token frequencies—to improve local predictions. In experiments on FineWeb‑Edu, TurboMamba outperforms Transformer, Mamba‑3, Jamba, and Zamba, achieving the lowest mean test prediction loss and significantly higher training throughput and batch latency 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 Machine Learning.

Related stories

arXiv AI
Aug 11

Length-MAX Tokenizer for Language Models

arXiv:2511. 20849v2 Announce Type: replace-cross Abstract: We introduce a new tokenizer for language models that minimizes the average tokens per character, thereby reducing the number of tokens needed to represent text during training and to generate text during inference.

By Dong Dong, Weijie Su
llmsragnlpefficiency
More like this →
arXiv Machine Learning
Sep 14

Breaking the Token Ceiling: Distilling Smaller, Stronger Byte Models

The paper investigates whether small models distilled from larger ones behave similarly when using byte versus token tokenization. It introduces two methods—Marginalize‑It (approximate) and End‑Of‑Token (exact)—to convert token logits to byte logits, and conducts a large‑scale study on decoder‑only dense transformers ranging from 1 billion to 1 trillion bytes of data. Results show that while token‑based models excel early, byte‑based models eventually surpass them with more compute, achieving higher performance ceilings, greater data efficiency, and lower logit storage costs.

By Kalyani Marathe, Artidoro Pagnoni, Tomasz Limisiewicz, Margaret Li, Mike Lewis, Luke Zettlemoyer, Srinivasan Iyer
llmsnlpefficiencybenchmarks
More like this →
arXiv Computation and Language
Aug 31

Nested Byte-Level Vocabularies Are Cheap to Deploy and Expensive to Share: A Pre-Registered Negative Result

The paper demonstrates that a byte‑level BPE tokenizer can be sliced to create multiple vocabulary sizes from a single trained model, preserving exact logits while reducing deployed weights by 66%. Experiments on 30 models show that while sliced models match the full model numerically, they underperform fixed‑cap specialists by a few percentage points in bits‑per‑byte. Multi‑cap training improves robustness to typographical noise, suggesting benefits from training across multiple granularities rather than from control tokens alone.

By Christos Koutsiaris
llmsragnlpbenchmarks
More like this →