includes more than 20 different sub-goals and
therefore demand significantly longer reasoning steps.
Meanwhile, many of
these sub-goals have to be
achieved precisely with the exact object name, quanti-
ties, and preconditions, e.g., mine 3 obsidian with
diamond pickaxe, craft 1 diamond pickaxe from
3 diamonds and 2 sticks on crafting table; oth-
erwise, the subsequent sub-goals won’t be executed due
to unfulfilled preconditions. To tackle this, we may
refer to some approaches in LLM reasoning, e.g. self-
debugging [Chen et al., 2023] and turning the planning into
an interactive fashion. In Figure 2 (Middle), we’ve shown
that as the complexity of the task increases, our JARVIS-
1, which uses interactive planning [Wang et al., 2023a] to
mitigate the aforementioned issues (details can be found in
Section 3.2), elicits more significant advantages over the
baseline (GPT) planner.

2.3. Challenge III: Life-long Learning
JARVIS-1