![[2510.15425] TeamFormer: Shallow Parallel Transformers with Progressive Approximation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2510.15425] TeamFormer: Shallow Parallel Transformers with Progressive Approximation
Summary
The paper introduces TeamFormer, a shallow Transformer architecture that enhances parallelism and reduces training time while maintaining performance, challenging the 'deeper is better' paradigm in machine learning.
Why It Matters
This research is significant as it addresses the limitations of deep Transformer models, such as increased training times and resource demands. By proposing a new architecture that emphasizes parallelism, it opens up possibilities for more efficient machine learning applications, especially in resource-constrained environments.
Key Takeaways
- TeamFormer proposes a shallow architecture that enhances parallelism in Transformers.
- The model achieves up to 15.07x compression and is 3.30x faster than existing solutions.
- Inter-layer collaboration is emphasized over depth for improved performance.
- The architecture supports adaptive continuous learning, making it versatile for various applications.
- Theoretical foundations are based on the Universal Approximation Theorem, providing a new perspective on Transformer design.
Computer Science > Machine Learning arXiv:2510.15425 (cs) [Submitted on 17 Oct 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:TeamFormer: Shallow Parallel Transformers with Progressive Approximation Authors:Wei Wang, Xiao-Yong Wei, Qing Li View a PDF of the paper titled TeamFormer: Shallow Parallel Transformers with Progressive Approximation, by Wei Wang and 2 other authors View PDF HTML (experimental) Abstract:The widespread 'deeper is better' philosophy has driven the creation of architectures like ResNet and Transformer, which achieve high performance by stacking numerous layers. However, increasing model depth comes with challenges such as longer training times, higher inference latency, and impracticality on resource-constrained devices. To address these issues, we propose TeamFormer, a shallow Transformer architecture designed for true parallelism in both structure and computation. By formulating standard Transformers as function approximators in closed-form, our theoretical analysis shows that their performance relies on inter-layer collaboration for progressive approximation, rather than depth itself. While deep Transformers enforce this collaboration through sequential designs, we demonstrate that such collaboration is not inherently tied to sequential structures. TeamFormer removes the sequential constraint by organizing layers into parallel branches, enforcing inter-layer collaboration algorithmically. Specifically, we implement progressive approx...
