Depth-Width tradeoffs in Algorithmic Reasoning of Graph Tasks with Transformers

The paper presents findings on the depth-width tradeoffs in transformer models applied to graph-based algorithmic tasks. It reveals that when the model width is allowed to grow linearly while keeping depth constant, constant depth is sufficient for solving various graph problems, indicating that wider models can achieve similar accuracy to deeper models but with improved training and inference times. This work highlights the potential for optimizing transformer architectures by balancing width and depth, which is crucial for practitioners aiming to enhance efficiency in LLM implementations.