Scalable Training of Language Models using JAX pjit and TPUv4

Joanna Yoo; Jo\~ao G.M. Ara\'ujo; Kuba Perlin; Siddhartha Rao Kamalakara

Scalable Training of Language Models using JAX pjit and TPUv4

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2204.06514 v1 pith:7VNYXEIO submitted 2022-04-13 cs.LG cs.CL

Joanna Yoo , Kuba Perlin , Siddhartha Rao Kamalakara , João G.M. Araújo This is my paper

classification cs.LGcs.CL

keywords trainingchallengeshardwarelanguagemodelsscalablesoftwareadopting

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original abstract

Modern large language models require distributed training strategies due to their size. The challenges of efficiently and robustly training them are met with rapid developments on both software and hardware frontiers. In this technical report, we explore challenges and design decisions associated with developing a scalable training framework, and present a quantitative analysis of efficiency improvements coming from adopting new software and hardware solutions.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

One Tokenizer To Rule Them All: Emergent Language Plasticity via Multilingual Tokenizers
cs.CL 2025-06 conditional novelty 6.0 of 10

A tokenizer trained on more languages than the model's main pretraining set makes later language adaptation faster and better, with minimal loss on the pretraining languages.

Pith tools