We posit that questions requiring multiple iterations of bootstrapping to solve present a greater
challenge to models. In Complex CoT (Fu et al., 2022), the difﬁculty level is gauged through the
number of hops in reasoning chains. Consequently, our assessment of difﬁculty is relative to the
model’s capacity, which is more straightforward and precise. Despite some differences, the results of
the two difﬁculty measurement methods are generally consistent. Therefore, for the convenience of
further validating our approach in selecting examples with appropriate level of difﬁculty, We sort the
test set of GSM8K according to the hops of the annotated reasoning chains, and conduct experiment
using Iter-CoT and other baselines, as shown in Figure 7. Our results indicate that Iter-CoT performs
comparably to other methods for questions with few hops, whereas its performance is signiﬁcantly
better than other methods for questions with multiple hops like 7-hop (with an average improvement
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models