ReLoRA as stacked low-rank updates for Distribution Fine-Tuning

Summary

Independent-researcher tweet (@rosmine, 2026-05-20) arguing ReLoRA is a useful trick: train a LoRA, merge it into the base, then train a new LoRA, because a sum of low-rank updates can be full rank. Says this was used for Distribution Fine-Tuning (DFT) with a total of 13 stages; the posted text cuts off there. No paper, code, or dataset linked from the tweet. Underlying ReLoRA method is Lialin et al. 2023, not this post.

Keywords

relora · lora · dft · peft · x

Topics

LoRA, parameter-efficient fine-tuning

Research notes

• Primary: X post via fxtwitter (display_text_range 0-261; text ends at "For DFT, I used a total of 13"). No follow-through paper/repo in the tweet. Underlying ReLoRA paper https://arxiv.org/abs/2307.05695 (Lialin, Shivagunde, Rumshisky). Author site https://rosmine.ai/. Cataloged the tweet as other. Not a hosted corpus, so no datasets_local row.