reviews: 
Comments: This paper proposed a meta-learning objective to train a meta model with operates well across a sequence of tasks it is finetuned on (though unfortunately, exact details of the proposed approach are quite unclear and obfuscated). When deployed across a large range of dataset-adaptation-sequences, the proposed subnetwork projection showcases promising performance, particular for task sequences large than two.
Strengths: * The experimental results are quite convincing once the third task is reached. Before that, performance closely resembles that of PAINT-style adaptation.
* The suite of adaptation experiments is quite expansive and covers a dozen of common evaluation benchmarks.
Weaknesses: The primary issue with this paper comes from its presentation and writing, which is incredibly difficult to parse, lacks detail and often motivation and descriptions for experiments and components. In particular:

* First, and most importantly, the proposed SNP/SNP++ approach is never really formalized, with the base-text just references large, ad-hoc algorithm chunks without providing any actionable information on the method details, and what actually happens. Similarly, subnetworks, as used and target in this work, are never actively defined.
As a result, it is very difficult (near impossible) to understand what the method is exactly supposed to do. It would be important for the authors to provide some better context here.

* The paper effectively has two separate experiment sections. While it's clear that the first one serves to motivate the method, it lacks a lot of context that makes it difficult to understand some of the experimental specifics without constantly jumpting back and forth. In particular:
	* For 3.1, it would be great to first lead with a quick summary of what this section aims to investigate. Generally, it remains somewhat unclear what the exact point of this section is. Is it just to highlight that models finetuned on different datasets exhibit differences with respect to each other? And if so, a more detailed investigation for example relating this to semantic relations between datasets would be much more insightful, instead of just highlight some arbitrary numerical grouping.

	* Section 3.2 starts to talk about subnetworks, without those being introduced beforehand. In general, it is again difficult to understand the purpose of this section and the experiments conducted in it.


* The discussion of related works is also quite lackluster, and misses a relevant discussion of both recent works looking at interpolation-based adaptation of foundation models for CL (e.g. [1]), as well as the large body of research investigating continual learning and adaptation through architectural restructuring (e.g. [2-4]). In general, the overall discussion of continual learning works could certainly be improved.

* This work often leverages metalearning as a driving motivator. But how crucial is a Meta-Learning approach in light of works such as [7,8] which highlight limited importance of a full meta-learning objective for few-shot transfer?


__Smaller issues regarding the general writing:__

* The abstract is very short, and could benefit from some additional notes on the subnetwork selection and its motivation, alongside some further details about the experiments.

* The exact values for Tab. 1 aren't crucial for the readers understanding, and the table would be much better replaced with a corresponding heatmap. Same goes for Table 2.

* Tables are often placed awkwardly far from the first point of reference (e.g. Tab.7 & 8). In general, they are so dense that they become difficult to parse.

* It would be great to elaborate on the distance function dist(.,.) as soon as it is introduced (p.3)

* Algorithm 1 is never even explicitly referenced (and Algorithm 2 not in order), only the correspondingly defined functions are used in subsequent algorithms. It would be great to provide crossreferences here.

* In general, standard deviations would be great for all reported numbers.



__Related Works__
* [1] "Momentum-based Weight Interpolation of Strong Zero-Shot Models for Continual Learning", Stojanovski et al. 2022
* [2] "PackNet: Adding multiple tasks to a single network by iterative pruning", Mallya et al. 2018
* [3] "Progress & Compress: A scalable framework for continual learning", Schwarz et al. 2018
* [4] "Compacting, Picking and Growing for Unforgetting Continual Learning", Hung et al. 2019
* [5] "An empirical investigation of the role of pre-training in lifelong learning", Mehta et al. 2022
* [6] "Effect of scale on catastrophic forgetting in neural networks", Ramasesh et al. 2022
* [7] "A Baseline for Few-Shot Image Classification", Dhillon et al. 2019
* [8] "Exploring Simple Meta-Learning for Few-Shot Learning", Chen et al. 2020
Recommendation: 3: reject, not good enough
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 1 poor