arXiv AI
Aug 10
Stability of Transformers under Layer Normalization
arXiv:2510. 09904v2 Announce Type: replace-cross Abstract: Despite their widespread use, training deep Transformers can be unstable.
Looped Transformers with Layer Normalization Provably Learn the Power Method
arXiv:2606. 00605v1 Announce Type: new Abstract: Transformers have achieved remarkable success across a wide range of applications, and a growing body of work suggests that part of their strength comes from their ability to learn and execute algorithmic procedures.
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arXiv Machine Learning
Jun 5
SpanNorm: Reconciling Training Stability and Performance in Deep Transformers
arXiv:2601. 22580v2 Announce Type: replace-cross Abstract: The success of Large Language Models (LLMs) hinges on the stable training of deep Transformer architectures.
arXiv AI
Jul 20
In-context learning of closed form solution to simple linear regression task using transformer with linear self-attention
arXiv:2607. 15819v1 Announce Type: cross Abstract: In-context learning is a remarkable property of transformers and has recently received a lot of interest.
arXiv Machine Learning
Jul 14
AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating
arXiv:2607. 10593v1 Announce Type: new Abstract: Normalization is a critical component for stabilizing Transformer training, yet the choice between static strategies such as Layer Normalization (LN) and adaptive alternatives remains largely task-dependent.
arXiv Machine Learning
Jun 30
Generalization Analysis of Transformers in Distribution Regression
arXiv:2606. 29256v1 Announce Type: cross Abstract: In recent years, models based on the Transformer architecture have seen widespread applications and have become one of the core tools in the field of deep learning.
arXiv AI
Jun 11
Unifying Learning Dynamics and Generalization in Transformers Scaling Law
arXiv:2512. 22088v3 Announce Type: replace-cross Abstract: The scaling law, a cornerstone of Large Language Model (LLM) development, predicts improvements in model performance with increasing computational resources.
arXiv AI
22h ago
Gradient Heterogeneity Complements Hessian Heterogeneity in Transformer Optimization
arXiv:2502. 00213v5 Announce Type: replace-cross Abstract: Transformers are difficult to optimize with stochastic gradient descent (SGD) and largely rely on adaptive optimizers such as Adam.
arXiv Machine Learning
Jun 30
A Mechanistic Study of Transformers Training Dynamics
arXiv:2410. 24050v3 Announce Type: replace Abstract: Large-scale pretraining of transformers has been central to the success of foundation models.
arXiv AI
Jul 7
Individual Parameters in Weight-Sparse Transformers Appear Interpretable
arXiv:2607. 02964v1 Announce Type: cross Abstract: A central goal of mechanistic interpretability is to understand how neural networks work and what each individual component does.
arXiv Machine Learning
Jun 16
LiFT: Local Search via Linear Programming for Overfitting-Controlled Transformers
arXiv:2606. 16243v1 Announce Type: new Abstract: This paper proposes a Linear Programming (LP)-based local search framework for fine-tuning pretrained transformer models with explicit control against overfitting.
arXiv AI
Jul 14
Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks
arXiv:2607. 11875v1 Announce Type: cross Abstract: We present a theoretical framework to explain the emergence of inductive reasoning abilities in Transformer language models.
arXiv Machine Learning
Jul 2
Ghost in the Kernel: In-Context Learning with Efficient Transformers via Domain Generalization
arXiv:2607. 00479v1 Announce Type: new Abstract: Transformer-based large models have demonstrated remarkable generalization abilities across different tasks by leveraging a context-aware attention module for in-context learning.