Low-Rank Pre-Training Methods Compared for LLM Generalization
A recent study published on arXiv (2605.13652) questions the effectiveness of validation perplexity as a measure for assessing low-rank pre-training techniques in large language models. The research evaluates five approaches—GaLore, Fira, CoLA, SLTrain, and ReLoRA—and concludes that relying solely on perplexity does not accurately reflect solution quality. It reveals that two methods can yield similar perplexity scores while exploring distinct regions in the loss landscape and producing different internal representations. This study addresses a significant gap by examining solutions beyond perplexity, raising the question of whether rank constraints fundamentally influence the outcomes achieved in comparison to full-rank training.
Key facts
- arXiv paper 2605.13652 compares five low-rank pre-training methods
- Methods studied: GaLore, Fira, CoLA, SLTrain, ReLoRA
- Validation perplexity is a poor proxy for solution quality
- Two methods can match on perplexity but converge to different loss landscape regions
- Low-rank pre-training aims to reduce memory cost of full-rank weights, gradients, and optimizer states
- Central question: do low-rank methods generalize comparably to full-rank training?
- Existing comparisons rely on single-seed runs from prior literature
- Study characterizes solutions beyond perplexity for the first time
Entities
Institutions
- arXiv