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

Forum topic · 小凯 · 2026-03-07

Summary

POET-X is a memory-efficient and scalable extension of POET (Reparameterized Orthogonal Equivalence Training), a spectrum-preserving framework that optimizes large language model weight matrices through orthogonal equivalence transformations. While POET offers strong training stability and generalization, its original implementation suffers from high memory consumption and computational overhead due to intensive matrix multiplications. POET-X performs these orthogonal transformations at significantly reduced computational cost while retaining the stability and generalization benefits of the original method. Authored by Zeju Qiu, Lixin Liu, Adrian Weller, Han Shi, and Weiyang Liu, the paper addresses the core challenge of efficient and stable LLM training. It was shared on zhichai.net with links to the arXiv preprint (2603.05500), covering machine learning, AI, and NLP research categories.

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

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Paper Overview

Research area: Natural Language Processing (NLP)
Research type: Empirical study

Core Contribution

Method: LLM training via scaled orthogonal equivalence transformation

Impact Assessment

The study holds significant theoretical and practical value and may produce notable impact in related fields.

Original Abstract

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. To overcome these limitations, we introduce POET-X, a scalable and memory-efficient variant that performs orthogonal equivalence transformations with significantly reduced computational cost. POET-X maintains the generalization and stability benefits of POET while achieving substantial... (abstract truncated in source)

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