previous execution results. This allows the four components described in § 3.1 to be well integrated and
to cooperate effectively to achieve complex tasks. We refer to such an incremental reasoning strategy as
extrospective reasoning. Compared to introspective reasoning, extrospective reasoning additionally considers
feedback from the user and environment (Figure 6), and is thus better suited to complex tasks, such as
multi-step QA and embodied learning, where decision-making at each step is dependent on the preceding
context.
Recent works such as Self-Ask (Press et al., 2022), ReAct (Yao et al., 2022b), and ToolFormer (Schick et al.,
2023) have demonstrated that by providing access to search engine APIs, models are able to achieve improved
accuracy on multi-step QA. Through CoT prompting (Self-Ask and ReAct) or ﬁne-tuning (ToolFormer),
models can learn to decompose complex questions and utilize the search API to ﬁnd the answer to the ﬁrst
Tool Learning with Foundation Models