Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The authors in this work propose an offline RL based framework to resolve metastability failures in distributed systems. They demonstrate that their proactive strategy outperforms the traditional heuristic based methods.
strengths_and_weaknesses: Strength:
* The authors have clearly presented the problem and proposed a novel method to tackle the problem. The details of the experimental setup are well written and clear for the reader.



Weakness:
* The paper does not seem to have an abstract.
suggested_changes: * In section 2, rewrite "Power is defined as Power = Throughput/Delay" to "Power is defined as Throuhput divided by Delay."
* Correct spelling of throughput in section 2.
* Please expand abbreviation wherever used eg SLO in section 2, IQL in section 3.
* Add abstract to the paper.
review_title: null

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: This paper studies metastable failures in the distributed system. To address the limitation of conventional heuristic-based methods, the authors explore an offline reinforcement learning (RL) based method to predict the rate limit. Extensive experiments demonstrate its superiority and generalization capability.
strengths_and_weaknesses: **Strengths:**

* This paper is indeed well-written. The motivation for adopting offline RL to resolve metastable failures is clear.
* The proposed method seems to be sound. The empirical validation considers in-distribution and OOD settings.

**Weakness:**

* Figure 1 (a) is blurry. Please fix it in the final version.
* The authors mention three key challenges in mitigating metastable failures using RL, without clearly presenting how the proposed method solves each of them.
* Ablation studies are needed. For example, the performance sensitivity on the hyperparameter choices, or the input choices to the RL agent (as described in `State Space`). 

**Minor:**

* Is there any transition in your MDP formulation?

* It would be better to illustrate your method using Algorithm (\begin{algorithmic}).

**Overall:**

* *Clarity:* Good. This paper is well-written. It also includes an appropriate discussion of relevant literature in the Appendix.

* *Correctness:* Good. Most of the content in the main paper seems to be correct.

* *Reproducibility:* Fair. It may be hard to reproduce the results without the open-source code.

* *Follows basic requirements:* Yes, the authors follow the ICLR code of conduct.

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Update: As PC noted, each paper should have an `Abstract` section.
suggested_changes: Please see the weakness and minor sections.
review_title: null

Decision: Invite to present