fly51fly (@fly51fly)

Google DeepMind가 바이트 단위 언어모델에서 패치 크기와 연산량을 분리하는 ‘Scratchpad Patching’ 기법을 공개했다. 더 유연한 계산 제어를 통해 효율적인 바이트 레벨 언어모델 설계를 가능하게 하는 새로운 방법론이다.

https://x.com/fly51fly/status/2054312634514915426

#deepmind #languagemodel #bytelevel #efficiency #arxiv

fly51fly (@fly51fly) on X

[CL] Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models L Zheng, V Bashlovkina, T Dozat, D Garrette… [Google DeepMind] (2026) https://t.co/KQc96FcHPp

X (formerly Twitter)

New Bolmo architecture lets you train language models directly on bytes, cutting tokenization overhead and boosting multilingual performance. Built on Ai2’s Dolma 3 and Olmo pipelines, it promises faster, cheaper LLM research. Curious how byte‑level training reshapes AI? Dive into the details. #Bolmo #ByteLevel #Ai2 #Multilingual

🔗 https://aidailypost.com/news/bolmo-architecture-enables-efficient-bytelevel-lm-training-easing-ai