SeeDNorm: Self-Rescaled Dynamic Normalization

ICLR 2026 Conference SubmissionAnonymous Authors

OpenReview Score: 6.5 Download Report PDF

Normalization Layer

Normalization layer constitutes an essential component in neural networks. In transformers, the predominantly used RMSNorm constrains vectors to a unit hypersphere, followed by dimension-wise rescaling through a learnable scaling coefficient $\gamma$ to maintain the representational capacity of the model. However, RMSNorm discards the input norm information in forward pass and a static scaling factor $\gamma$ may be insufficient to accommodate the wide variability of input data and distributional shifts, thereby limiting further performance improvements, particularly in zero-shot scenarios that large language models routinely encounter. To address this limitation, we propose SeeDNorm, which enhances the representational capability of the model by dynamically adjusting the scaling coefficient based on the current input, thereby preserving the input norm information and enabling data-dependent, self-rescaled dynamic normalization. During backpropagation, SeeDNorm retains the ability of RMSNorm to dynamically adjust gradient according to the input norm. We provide a detailed analysis of the training optimization for SeedNorm and proposed corresponding solutions to address potential instability issues that may arise when applying SeeDNorm. We validate the effectiveness of SeeDNorm across models of varying sizes in large language model pre-training as well as supervised and unsupervised computer vision tasks. By introducing a minimal number of parameters and with negligible impact on model efficiency, SeeDNorm achieves consistently superior performance compared to previously commonly used normalization layers such as RMSNorm and LayerNorm, as well as element-wise activation alternatives to normalization layers like DyT.

Abstract:

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.

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Overview

Overall Novelty Assessment

The paper proposes SeeDNorm, a dynamic normalization layer that adjusts scaling coefficients based on current input rather than using static parameters. It resides in the 'Dynamic and Adaptive Scaling Mechanisms' leaf of the taxonomy, which contains four papers including the original work. This leaf sits within the broader 'Core Normalization Mechanisms and Architectures' branch, indicating a moderately populated research direction focused on learnable or input-dependent normalization parameters. The taxonomy shows this is an active but not overcrowded area, with sibling papers exploring related adaptive scaling strategies.

The taxonomy reveals several neighboring research directions that contextualize SeeDNorm's contribution. Adjacent leaves include 'Batch-Free and Online Normalization' (2 papers), 'Switchable and Multi-Scope Normalization' (2 papers), and 'Normalization-Activation Integration' (3 papers), suggesting the field explores diverse approaches to adaptive normalization beyond dynamic scaling. The 'Domain Adaptation and Transfer Learning' branch (5 papers across 3 leaves) addresses distribution shifts through different mechanisms, while 'Time Series and Temporal Data Processing' (5 papers) tackles temporal dynamics. SeeDNorm's focus on input-dependent scaling distinguishes it from these parallel directions, which emphasize scope selection, domain transfer, or temporal adaptation rather than dynamic coefficient adjustment.

Among 30 candidates examined through semantic search and citation expansion, none clearly refute any of the three contributions: the SeeDNorm mechanism itself (10 candidates examined, 0 refutable), the theoretical stability analysis (10 candidates, 0 refutable), and empirical validation across language and vision tasks (10 candidates, 0 refutable). This limited search scope suggests that within the examined literature, the specific combination of input-dependent scaling with norm preservation appears relatively unexplored. However, the analysis explicitly notes this is not an exhaustive search, and the sibling papers in the same taxonomy leaf indicate related adaptive scaling work exists in the broader field.

Based on the limited search of 30 semantically similar papers, SeeDNorm appears to occupy a distinct position within dynamic normalization research. The taxonomy structure shows it contributes to an active but not saturated research direction, with clear boundaries separating it from domain adaptation, temporal processing, and architecture-specific methods. The absence of refuting candidates among examined papers suggests novelty within the search scope, though the analysis acknowledges this does not constitute comprehensive coverage of all prior work in adaptive normalization mechanisms.

Taxonomy

Core-task Taxonomy Papers

Claimed Contributions

Contribution Candidate Papers Compared

Refutable Paper

Research Landscape Overview

Core task: dynamic normalization for neural networks. The field encompasses a diverse set of strategies for adaptively adjusting feature statistics during training and inference, aiming to improve convergence, generalization, and robustness across varying data distributions. The taxonomy organizes these approaches into several main branches: Core Normalization Mechanisms and Architectures focuses on foundational techniques such as adaptive scaling, learnable parameters, and novel normalization layers (e.g., Layer Normalization[33], Switchable Normalization[30]); Domain Adaptation and Transfer Learning addresses methods that adjust normalization to handle distribution shifts (e.g., Transferable Normalization[23]); Time Series and Temporal Data Processing targets sequential and spatiotemporal contexts (e.g., Dynamic Spatiotemporal[10], Temporal Effective Batch[13]); Input Preprocessing and Data Enhancement explores normalization at the data level; Specialized Architecture Applications tailors normalization to specific network designs; Multi-Task and Multi-Objective Learning examines normalization in settings with multiple objectives (e.g., GradNorm[16]); and Domain-Specific Applications applies these ideas to fields like medical imaging, gesture recognition, and anomaly detection. Within the Core Normalization Mechanisms branch, a particularly active line of work centers on dynamic and adaptive scaling mechanisms that learn or compute normalization parameters on-the-fly rather than relying on fixed statistics. SeeDNorm[0] exemplifies this direction by introducing a mechanism that dynamically adjusts normalization based on input characteristics, positioning it alongside works like Dynamic Normalization[15] and Differentiable Dynamic[35], which similarly emphasize learnable or context-sensitive scaling. In contrast, Learnable Adaptive[41] explores parameterized normalization strategies that balance flexibility with computational efficiency, while Dual Domain Dynamic[3] extends adaptive normalization to handle multiple feature domains simultaneously. A recurring theme across these studies is the trade-off between expressiveness and stability: highly adaptive schemes can better capture input variability but may introduce training instability or overfitting, whereas more constrained approaches sacrifice some flexibility for robustness. SeeDNorm[0] navigates this landscape by proposing a scaling mechanism that remains computationally tractable while offering richer adaptivity than earlier fixed-parameter methods, situating it as a middle ground between fully static normalization layers and more complex, domain-specific adaptive schemes.

Claimed Contributions

SeeDNorm: Self-Rescaled Dynamic Normalization

10 retrieved papers

The authors introduce SeeDNorm, a novel normalization layer that dynamically adjusts its scaling coefficient conditioned on the input. Unlike RMSNorm, which uses a static scaling factor, SeeDNorm preserves input norm information in the forward pass while maintaining the ability to adaptively adjust gradients during backpropagation.

10 retrieved papers

Theoretical analysis and stability solutions for SeeDNorm

10 retrieved papers

The authors conduct a comprehensive theoretical analysis of SeeDNorm's forward and backward propagation properties, including scale invariance and gradient behavior. They propose techniques such as multi-head SeeDNorm and weight decay strategies to enhance training stability.

10 retrieved papers

Empirical validation across language and vision tasks

10 retrieved papers

The authors demonstrate SeeDNorm's effectiveness through extensive experiments on large language models (both dense and MoE architectures) and computer vision tasks including image generation, supervised classification, and self-supervised learning. SeeDNorm achieves superior performance compared to RMSNorm, LayerNorm, and DyT with minimal parameter overhead.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

[15] Dynamic normalization PDF

Liu Chuan, Chuan Liu, Gao Yi, Yi Gao, Lv, Jiancheng, Jiancheng Lv (2020)

[35] Differentiable dynamic normalization for learning deep representation PDF

Ping Luo, Zhanglin Peng, Wenqi Shao, Ruimao Zhang, Jiamin Ren, Ling-Yun Wu (2019)

[41] Enhancing deep neural network training through learnable adaptive normalization PDF

Jan Benedikt Ruhland, Iraj Masoudian, Benedikt Ruhland, Dominik Heider (2025)

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution