The deployment of large language models (LLMs) is witnessing remarkable growth across both personal and professional domains [Nakano et al., 2021, Touvron et al., 2023].

The authors deal with a new problem that extends existing problems in a natural way, and present new solutions that extend existing solutions in a natural way; thus in some ways the paper is not very original, but it must be noted that the problem #opt has some surprising properties that make the whole endeavor quite interesting. As for the contribution, it is a valuable piece with new algorithms that have good performance. I really take issue with the emphasis on semirings; I cannot see how this helps the whole effort. Are semirings bringing new insights here? Or are they just more general so that the algorithm applies to other problems?

My two biggest complaints center on 1) the illustrative single-step matrix game of section 4.1 and figure 3 and 2) the practical applications of MACKRL. 1) Since the primary role of the single-step matrix game in section 4.1 is illustrative, it should be much clearer what is going on. How are all 3 policies parameterized? What information does each have access to? What is the training data? First, let's focus on the JAL policy. As presented up until this point in the paper, JAL means centralized training *and* execution.

This paper is methodological (and experimental) in nature, providing a suite of approaches to differentially-private Bayesian linear regression. The key significance is to revisit DP linear regression in the Bayesian setting, where it is natural to consider 1) how privacy-preserving noise affects posterior estimates; 2) leverage Bayesian inference through directly modelling the noise process, to improve utility (broadly construed including in terms of calibration). The paper does a quality job of exploring how such modelling and inference could be performed based on sufficient statistic perturbation. The paper has high clarity, further adding to the potential practical impact. The main technical ideas are largely inspired by prior work such as Bernstein and Sheldon (2018)'s work on exponential families.

All reviewers consider the paper to make a solid contribution and recommend acceptance.

While in recent years a number of extreme low-precision quantization techniques were developed for DNNs, they were not directly applicable to recurrent architectures. In recent work [1] an extreme low-precision quantization method was proposed that utilizes batch normalization and compresses recurrent neural networks without a large drop in accuracy achieving state-of-the-art performance. In this paper, the authors proposed a theoretical explanation of the difficulties of training LSTMs with low-precision weights and practically explored a combination of different normalization techniques with different quantization schemes. The authors experimentally showed that simple introduction of weight or layer normalization allows applying many standard quantization techniques without modifications. Comments, suggestions, and questions: Figure 1 is quite difficult to read.

This paper proposes the use of memory in life-long learning to prevent catastrophic forgetting by means of experience replay and local adaptation. The idea is simple yet it is an interesting new step in this line of work. The paper would be a good addition to the conference, and has support from reviewers.