**Please check the remaining part of our rebuttal to Reviewer oy34 as follows:**
**[Q1.4]** We summarized the errors that have occurred through the evaluation in ALFWorld as follows:
| Type | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Indentation error | Occasionally, the code may contain indentation errors. This error can be addressed by the Code Check. |
| Reference error | The ```start_from``` may be misconfigured and the last breakpoint are not properly loaded. This error can be mitigated by out-of-plan refinement. |
| Incomplete code | Due to the token limit, generated code might sometimes be truncated. We can adopt newer models that support extended context lengths, such as gpt-3.5-turbo-16k. |
**[Q6.1/6.2]** We use each solution as a demonstration in the prompt to solve similar tasks. If including the solution boosts the success rate, we will keep the solution in the skill memory and add it to the prompt for future task-solving.
**[Q6.3]** The skill memory can potentially be large, depending on the task complexity. The automatic adjustment of the skill memory's size is an important feature that we will investigate in the future.
**[Q6.4]** AdaPlanner is purely prompting-based and there is no training process involved. We add the discovered skills to the prompts. The skills are actively discovered by the agent, thus not included in the count of expert demonstrations.
**[Q7]** As illustrated in line 209-213, we assess the effect of using each discovered skill as the demonstration in the prompt to solve similar tasks. If adding a candidate solution boosts the success rate on these tasks, it's added to the memory of discovered skills; if not, it's discarded.
**[Q8]** The successful skills discovered by AdaPlanner will be added to the prompt. It could be understood as a method for prompt tuning. However, we emphasize that this procedure is automatically completed by LLM itself during the planning.
**[Q9]** Yes. The detailed settings for these two figures were mentioned in line 227-229, 442-445. In Figure 4a, we adopt the setting of prompted samples as in Table 4. In Figure 4d, we only provide one sample of the simplest task (pick) and use skill discovery to explore skills for the rest five tasks. The difference in the success rate of “with SD” in Figure 4d originates from this setting difference.
**[Q10]** We use “environment feedback” to indicate any outcome provided by the environment, including the observations.
**[Q11]** The assertion error provides information about the plan rather than the policy. For example, in ALFWorld, the error message reports which action within the plan has been executed and the error occurs (e.g., ```Error in [Step X], …```). This information corresponds to $P_{t-1}$ as in the definition of $\rho(P_t|g, c_t, P_{t-1})$ in line 119.
**[Typos] Q12, 13, 14, 15, 16, 19** Thank you for the comments. We will revise these typos and notations in the updated version of our paper.
**[Q17]** The $h_t$ would be included in $c_{>t}$. For example, the agent identifies the target object ```book 1``` from the environment observation ```On drawer 2, you see a book 2, and a keychain 1.``` Here $h_t$ is the identifier information which is then used for future actions.
**[Q18]** We fix $T$ as the step limit of the environments. For example, in ALFWorld, this number is set to 50.