arXiv Machine Learning
4d ago

Bug or Feature$^2$: Weight Drift and Activation Sparsity

The paper investigates a negative weight drift that arises from the interaction between standard loss functions (MSE or cross‑entropy) and positively biased activation functions. This drift, which is inherent to the optimization process rather than the data, pushes downstream weights toward negative values during early training and leads to high activation sparsity—up to nearly 90% in GPT‑nano when using ReLU. The authors also examine the impact of this sparsity, noting that model performance degrades sharply when activation sparsity exceeds about 70%, and study sparsity‑inducing activation functions that produce spikes in intermediate transformer layers.

By Egor Shvetsov, Aleksandr Serkov, Shokorov Viacheslav, Redko Dmitry, Vladislav Goloshchapov, Evgeny Burnaev
arXiv Machine Learning
Sep 11

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations

SG-Blend introduces a per‑layer adaptive activation that interpolates between a bias‑corrected, parametric Swish variant (SSwish) and GELU, using a learnable blend coefficient, sharpness, and zero‑centering bias. The method adds only three scalars per feed‑forward block and, on BERT‑style IMDB classification, matches peak accuracy while reducing seed‑to‑seed variance by 42 %. It also achieves the lowest validation perplexity on WikiText103 and generalizes to computer vision and other domains.

By Gaurav Sarkar, Syed Affan Daimi, Jay Gala, Subarna Tripathi
arXiv AI
Sep 21

Decoupling Internal Representational Changes and Causal Importance in Fine-Tuned Large Language Models

Fine‑tuning reshapes internal representations of large language models, affecting attention patterns and layer‑wise activations. The study shows that components identified by EAP as important for task performance cluster in specific layers, yet these layers do not align with those undergoing the largest representational changes. Additionally, overlapping EAP components across different tasks do not guarantee cross‑task transfer and can even degrade performance when tasks differ in nature.

By Lingfang Li, Procheta Sen, Shubham Das, Danushka Bollegala