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Stabilizing Native Low-Rank LLM Pretraining

arXiv:2602.12429v1 Announce Type: new Abstract: Foundation models have achieved remarkable success, yet their growing parameter counts pose significant computational and memory challenges. Low-rank factorization offers a promising route to reduce training and inference costs, but the community lacks a stable recipe for training models from scratch using exclusively low-rank weights while matching the performance of the dense model. We demonstrate that Large Language Models (LLMs) can be trained from scratch using exclusively low-rank factorized weights for all non-embedding matrices without auxiliary "full-rank" guidance required by prior methods. While native low-rank training often suffers from instability and loss spikes, we identify uncontrolled growth in the spectral norm (largest singular value) of the weight matrix update as the dominant factor. To address this, we introduce Spectron: Spectral renormalization with orthogonalization, which dynamically bounds the resultant weight

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Paul Janson, Edouard Oyallon, Eugene Belilovsky · · 1 min read · 2 views
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arXiv:2602.12429v1 Announce Type: new Abstract: Foundation models have achieved remarkable success, yet their growing parameter counts pose significant computational and memory challenges. Low-rank factorization offers a promising route to reduce training and inference costs, but the community lacks a stable recipe for training models from scratch using exclusively low-rank weights while matching the performance of the dense model. We demonstrate that Large Language Models (LLMs) can be trained from scratch using exclusively low-rank factorized weights for all non-embedding matrices without auxiliary "full-rank" guidance required by prior methods. While native low-rank training often suffers from instability and loss spikes, we identify uncontrolled growth in the spectral norm (largest singular value) of the weight matrix update as the dominant factor. To address this, we introduce Spectron: Spectral renormalization with orthogonalization, which dynamically bounds the resultant weight updates based on the current spectral norms of the factors. Our method enables stable, end-to-end factorized training with negligible overhead. Finally, we establish compute-optimal scaling laws for natively low-rank transformers, demonstrating predictable power-law behavior and improved inference efficiency relative to dense models.

Executive Summary

The article 'Stabilizing Native Low-Rank LLM Pretraining' addresses the computational and memory challenges posed by the increasing parameter counts in foundation models. The authors propose a method for training Large Language Models (LLMs) from scratch using exclusively low-rank factorized weights, which reduces training and inference costs. They identify uncontrolled growth in the spectral norm of the weight matrix update as a primary cause of instability in native low-rank training. To mitigate this, they introduce Spectron, a spectral renormalization technique with orthogonalization that dynamically bounds weight updates. The study demonstrates stable, end-to-end factorized training with negligible overhead and establishes compute-optimal scaling laws for natively low-rank transformers, showing improved inference efficiency compared to dense models.

Key Points

  • ▸ Low-rank factorization reduces training and inference costs for LLMs.
  • ▸ Uncontrolled spectral norm growth causes instability in native low-rank training.
  • ▸ Spectron stabilizes training by dynamically bounding weight updates.
  • ▸ Compute-optimal scaling laws for natively low-rank transformers are established.
  • ▸ Improved inference efficiency is demonstrated relative to dense models.

Merits

Innovative Solution

The introduction of Spectron provides a novel and effective method to stabilize low-rank training, addressing a significant challenge in the field.

Comprehensive Analysis

The study thoroughly investigates the causes of instability in low-rank training and provides a detailed solution, backed by empirical evidence.

Practical Implications

The demonstrated improvements in inference efficiency and the establishment of scaling laws have direct practical applications in deploying LLMs.

Demerits

Generalizability

The study focuses on LLMs, and the generalizability of the findings to other types of foundation models remains to be explored.

Implementation Complexity

The implementation of Spectron may introduce additional complexity, which could be a barrier for some practitioners.

Long-term Stability

While the study demonstrates short-term stability, the long-term stability of the proposed method in extended training scenarios is not fully addressed.

Expert Commentary

The article presents a significant advancement in the field of large language models by addressing the critical issue of training stability in low-rank factorization. The identification of uncontrolled spectral norm growth as a primary cause of instability is a valuable contribution, as it provides a clear target for intervention. The introduction of Spectron is particularly noteworthy, as it offers a practical and effective solution to this problem. The study's demonstration of stable, end-to-end factorized training with negligible overhead is a testament to the robustness of the proposed method. Furthermore, the establishment of compute-optimal scaling laws for natively low-rank transformers provides a solid foundation for future research and practical applications. However, the study's focus on LLMs raises questions about the generalizability of the findings to other types of foundation models. Additionally, while the short-term stability of the proposed method is well-demonstrated, the long-term stability in extended training scenarios remains an open question. Overall, the article makes a compelling case for the adoption of low-rank factorization in training LLMs, with the potential to significantly reduce computational and memory costs while maintaining model performance.

Recommendations

  • ✓ Further research should explore the generalizability of the proposed method to other types of foundation models.
  • ✓ Future studies should investigate the long-term stability of the proposed method in extended training scenarios.
  • ✓ Practitioners should consider adopting the proposed method for training LLMs, particularly in resource-constrained environments.

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