Efficiently Representing Algorithms With Chain-of-Thought Transformers

The paper presents a theoretical framework demonstrating that chain-of-thought (CoT) transformers can efficiently simulate Word RAM algorithms, achieving performance comparable to traditional computation models with only a poly-logarithmic overhead. The authors establish this for finite-precision transformers, continuous CoT representations, and a hybrid architecture combining transformers with recurrent layers, indicating that CoT can sort in \(\mathcal{O}(n \log n)\) and execute Dijkstra's algorithm in \(\mathcal{O}(E + V \log V)\) steps. This advancement is significant for AI practitioners as it suggests that CoT transformers can be utilized for more efficient algorithmic implementations, enhancing their applicability in real-world computational tasks.