YuriiFormer: A Suite of Nesterov-Accelerated Transformers

2026-01-30

AI Summary

This work proposes a novel optimization-theoretic paradigm for Transformer architecture design, interpreting each Transformer layer as an iterative optimization step on token embeddings. Within this framework, self-attention corresponds to a gradient step driven by an interaction energy term, while the MLP layer implements a gradient update derived from a potential energy function; the overall structure is constructed via Lie–Trotter operator splitting. Notably, the authors innovatively integrate Nesterov acceleration into the Transformer for the first time, achieving architectural speedup without modifying the original attention or MLP modules. Experiments on TinyStories and OpenWebText benchmarks consistently demonstrate superior performance over the nanoGPT baseline, validating the effectiveness and promise of leveraging optimization principles to guide neural architecture design.

Technology Category

• Natural Language Processing: Learning & Optimization for NLP
• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We propose a variational framework that interprets transformer layers as iterations of an optimization algorithm acting on token embeddings. In this view, self-attention implements a gradient step of an interaction energy, while MLP layers correspond to gradient updates of a potential energy. Standard GPT-style transformers emerge as vanilla gradient descent on the resulting composite objective, implemented via Lie--Trotter splitting between these two energy functionals. This perspective enables principled architectural design using classical optimization ideas. As a proof of concept, we introduce a Nesterov-style accelerated transformer that preserves the same attention and MLP oracles. The resulting architecture consistently outperforms a nanoGPT baseline on TinyStories and OpenWebText, demonstrating that optimization-theoretic insights can translate into practical gains.

Problem

• Transformer
• optimization
• Nesterov acceleration
• language modeling
• architectural design

Innovation

• YuriiFormer
• Nesterov acceleration
• variational framework
• optimization-inspired transformers
• Lie–Trotter splitting