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LionVote enhances Lion optimizer with per-layer learning rate adaptation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed LionVote, a novel per-layer learning rate adaptation mechanism designed to optimize the Lion optimizer. Diagnostics revealed that Lion's default learning rate is too high for certain parameters in Vision Transformer (ViT) architectures, leading to a disparity in effective scale across different layer types. LionVote addresses this by maintaining a compound level for each parameter tensor, updated based on gradient direction stability and momentum health, with validation loss as a tiebreaker. On ViT-Tiny/CIFAR-100, LionVote achieved a slight but statistically significant improvement in top-1 accuracy compared to the standard Lion optimizer and AdamW. AI

IMPACT Introduces a method to fine-tune learning rates per layer, potentially improving training efficiency and performance for specific model architectures.

RANK_REASON The cluster describes a new research paper detailing a novel optimization technique for machine learning models.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LionVote enhances Lion optimizer with per-layer learning rate adaptation

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Kris Atallah (New York University, New York, USA) ·

    LionVote: Per-Layer Learning Rate Adaptation for Lion

    arXiv:2607.09266v1 Announce Type: new Abstract: Per-layer diagnostics reveal that, at the prescribed learning rate, Lion's effective scale is 2.6-2.8x too high for attention and MLP parameters and ~2x too high for normalization layers on ViT-Tiny/CIFAR-100; this 32% cross-layer-t…

  2. arXiv cs.LG TIER_1 English(EN) · Kris Atallah ·

    LionVote: Per-Layer Learning Rate Adaptation for Lion

    Per-layer diagnostics reveal that, at the prescribed learning rate, Lion's effective scale is 2.6-2.8x too high for attention and MLP parameters and ~2x too high for normalization layers on ViT-Tiny/CIFAR-100; this 32% cross-layer-type disparity cannot be reproduced by a single g…

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