Vladislav Lialin

Vladislav Lialin has 2 papers in our library, in 2023; often with Anna Rumshisky.

2 papers in our library · 2023

The profile is the library's match of the name; two people with one name are not always told apart.

Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

Of its works in our library: the number of works on each and its rating among them.

The most cited first (OpenAlex and our library).

Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning (2023 · 128 citations)
Language models have grown far faster than single-GPU memory, so full fine-tuning of multibillion-parameter models is out of reach for most practitioners.
ReLoRA: High-Rank Training Through Low-Rank Updates (2023 · 74 citations)
Pre-training large language models requires a great deal of memory and compute. Parameter-efficient fine-tuning methods such as LoRA cut these costs, but on their own they cannot pre-train a model well, because each LoRA adapter can only make a low-rank change. ReLoRA addresses this by repeatedly merging the LoRA…

1 co-author with a page, the most papers together first.

Papers of our library cite their works 125 times (of the 141,840 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated 24 October 2026