reviews: 
Comments: The paper introduces a method called MaestroMotif for designing and using AI-assisted skills that enhance agent performance and adaptability, particularly for complex tasks in environments like NetHack. MaestroMotif leverages Large Language Models (LLMs) to translate natural language skill descriptions into reward functions, which are then used to generate code for skill initiation and termination and to create a policy over these skills. This allows the AI to perform specified behaviors through a combination of reinforcement learning (RL) and LLM-based code generation. 
The paper evaluates MaestroMotif on the NetHack Learning Environment (NLE) and demonstrates its effectiveness in handling complex navigation, interaction, and composite tasks. MaestroMotif’s zero-shot approach, using skills without additional training, outperformed both LLM-only and reward-based RL baselines, highlighting the value of hierarchical skill composition and the LLM’s role in skill design.
Strengths: Overall, I believe MaestroMotif presents an effective approach to incorporating human prior knowledge to solve specific tasks. Given that NetHack tasks involve a variety of challenges, including understanding natural language descriptions, planning over high-level abstractions, and exploration, it is notable that the authors demonstrated improved performance in the NetHack environment using their method.
Additionally, I find it interesting that the authors showed that learning skills simultaneously is essential for acquiring skills more efficiently, thanks to the emergence of curriculum-based learning.
Weaknesses: - **Source of Performance Gains**

  I wonder whether the performance gain primarily comes from the human domain knowledge or the method itself. It appears that the method relies heavily on human effort and domain knowledge to solve individual tasks. For instance, humans select a set of skills such as *Discoverer, Descender, Ascender, Merchant,* and *Worshipper,* and they even required to modify prompts for acquiring each of these skills. Therefore, when comparing this work with baselines, it’s difficult to determine whether the performance gain is due to the method itself or the expertise of human contributors. While the core of this work aims to incorporate human priors, the baseline algorithms should ideally leverage human priors at a comparable level.

- **Concerns about Generalizability**

  I'm not entirely convinced that the proposed five skills are general enough to tackle any downstream task in the NetHack environment, or if they are instead a highly targeted set of skills specifically suited for the benchmark tasks. To better demonstrate the method’s generalizability, one approach could be to train reusable skills across a broader range of environments, such as robotics tasks. For instance, training foundational skills on a robotic platform, like a humanoid or robotic arm, and performing zero-shot evaluations on new tasks that require combinations of these learned skills could provide stronger evidence of generalizability.

- **Need for Pseudocode**
        
  It is challenging to understand the entire concept concretely without pseudocode for the full process, and some aspects remain unclear. For example:
        
    - If certain agent states satisfy instantiation conditions for more than one skill, how does the LLM prioritize among different skills?
    - What happens if none of the skills satisfy the instantiation conditions?
    - Does the training-time policy over skills adapt/evolve during training? I wonder because, as each individual skill is being changed along the training process, it might be helpful for a policy-over-skills adapts to the current set of learned skills.

  I understand that the specific prompts in the Appendix may partially address these questions; however, I believe it would be beneficial to clearly state them in the main sections.

- **Relationship to Related Work**
     - It would be helpful if the relationship between this work and Motif (Kilssarov, 2024) were more explicitly stated.

- **Minor Corrections**
     - *Line 196:* "crafts aploicy" → "crafts a policy"
     - *Line 239:* *Llama 3.1 70b* → *Llama 3.1 70B*
     - *Figure 8(c):* It is difficult to compare the performance of the two different approaches, as they are visualized in separate plots.
     - *Appendix A.2 and A.3* : title of these subsections are same
     - *Line 269:* What negative impact? Additional clarifications would be helpful for better understanding
Recommendation: 5
Reviewer confidence: 2
Soundness/correctness: 2
Clarity: 2