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Pion optimizer preserves spectrum for stable LLM training

Researchers have introduced Pion, a novel spectrum-preserving optimizer designed for training large language models. Unlike traditional additive optimizers like Adam, Pion utilizes orthogonal transformations to update weight matrices, maintaining their singular values and spectral norm. This approach offers a stable and competitive alternative for both LLM pretraining and finetuning, as demonstrated by empirical results. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a new optimization method that could improve LLM training stability and performance.

RANK_REASON The cluster contains a research paper detailing a new optimization technique for LLMs.

Read on arXiv stat.ML →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 · Kexuan Shi, Hanxuan Li, Zeju Qiu, Yandong Wen, Simon Buchholz, Weiyang Liu ·

    Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation

    arXiv:2605.12492v1 Announce Type: cross Abstract: We introduce Pion, a spectrum-preserving optimizer for large language model (LLM) training based on orthogonal equivalence transformation. Unlike additive optimizers such as Adam and Muon, Pion updates each weight matrix through l…

  2. arXiv stat.ML TIER_1 · Weiyang Liu ·

    Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation

    We introduce Pion, a spectrum-preserving optimizer for large language model (LLM) training based on orthogonal equivalence transformation. Unlike additive optimizers such as Adam and Muon, Pion updates each weight matrix through left and right orthogonal transformations, preservi…