POET-X: Memory-Efficient LLM Training via Scalable Orthogonal Transformations

Forum topic · 小凯 · 2026-03-07

Summary

POET-X is a scalable and memory-efficient extension of POET (Reparameterized Orthogonal Equivalence Training), a spectrum-preserving framework that optimizes each weight matrix of large language models through orthogonal equivalence transformations. While POET delivers strong training stability and generalization, its original implementation requires intensive matrix multiplications, leading to high memory consumption and computational overhead. POET-X overcomes these limitations by performing orthogonal equivalence transformations at significantly reduced computational cost, retaining POET's stability and generalization benefits while achieving substantial efficiency gains for LLM training. Authored by Zeju Qiu, Lixin Liu, Adrian Weller, Han Shi, and Weiyang Liu, the paper (arXiv:2603.05500) addresses a core challenge in modern machine learning systems: efficient and stable training of large language models. This makes POET-X relevant for practitioners seeking memory-efficient LLM training pipelines.

POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation

Authors: Zeju Qiu, Lixin Liu, Adrian Weller, Han Shi, Weiyang Liu
arXiv: 2603.05500
PDF: https://arxiv.org/pdf/2603.05500.pdf
Categories: cs.LG, cs.AI, cs.CL
Field: Natural Language Processing (NLP)
Type: Empirical study

Overview

Efficient and stable training of large language models (LLMs) remains a core challenge in modern machine learning systems. To address this challenge, Reparameterized Orthogonal Equivalence Training (POET), a spectrum-preserving framework that optimizes each weight matrix through orthogonal equivalence transformation, has been proposed. Although POET provides strong training stability, its original implementation incurs high memory consumption and computational overhead due to intensive matrix multiplications.

Contribution

POET-X is a scalable and memory-efficient variant that performs orthogonal equivalence transformations with significantly reduced computational cost. It maintains the generalization and stability benefits of POET while achieving substantial efficiency gains.

Impact

The work carries notable theoretical and practical value and may significantly influence related research areas, particularly memory-efficient training of large language models.

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*Auto-collected on 2026-03-07.*