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Heterogeneous Parallelism for Multimodal Large Language Model Training

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arXiv:2605. 27678v1 Announce Type: new Abstract: Foundation model training is becoming multimodal, from post-training pipelines to large-scale pretraining.

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Authors: Yashaswi Karnati, Kamran Jafari, Akash Mehra, Li Ding, Pranav Prashant Thombre, Ali Roshan Ghias, Shifang Xu, Parth Mannan, Yu Yao, Hao Wu, Eric Harper, Ashwath Aithal, Nima Tajbakhsh
Released: 2026-05-28 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Heterogeneous Parallelism for Multimodal Large Language Model Training overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, eval, source, token, line, implement, sweep. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.27678

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