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paper_id,Summary,Questions,Limitations,Ethical Concerns,Soundness,Presentation,Contribution,Overall,Confidence,Strengths,Weaknesses,Originality,Quality,Clarity,Significance,Decision
input_transformations,"The paper investigates the impact of various input transformation techniques on the grokking phenomenon in algorithmic datasets. The transformations explored include reversing sequence order, shuffling sequences, and changing encoding to binary representation. The study aims to understand how these transformations affect the speed to grokking, final validation accuracy, and training stability of deep learning models.","['Can the authors provide more in-depth analysis and discussion of the results?', 'What are the broader implications of the findings on the grokking phenomenon?', 'Can the authors explore additional input transformations and model architectures to strengthen the evaluation?', 'Can you provide a more detailed theoretical analysis of why certain transformations impact grokking?', 'How do you situate your research within the broader context of existing literature on data transformations and model generalization?', 'Can you discuss the potential computational cost or efficiency of the proposed transformations?', 'Are there any potential negative societal impacts of your work?', 'Can the authors provide more theoretical grounding for their choice of transformations?', 'Are there additional transformations that could be explored?', 'Can the authors justify their choice of datasets and hyperparameters in more detail?', 'Can you provide more detailed explanations of the modifications made to the `operation_mod_p_data` and `run` functions?', 'What is the rationale behind choosing the specific input transformations explored in the paper?', 'Could you provide a deeper analysis of why certain transformations lead to the observed results?', 'How do the authors ensure that the transformations do not alter the underlying distribution of the dataset?']","['The study is limited to specific input transformations and a single model architecture, reducing the generalizability of the findings.', 'The evaluation metrics used may not capture all aspects of model performance and generalization.', 'Theoretical analysis is lacking.', 'Related work section is underdeveloped.', 'Limited generalizability due to single model architecture and specific hyperparameters.', 'The results are based on a single model architecture and specific hyperparameters. Different architectures and hyperparameters may yield different results.', 'The paper should discuss potential limitations of the study, such as the choice of transformations and datasets, and how these might impact the generalizability of the results.']",False,2,2,2,3,4,"['Addresses a relevant and intriguing phenomenon in deep learning.', 'Proposes straightforward input transformations that offer a fresh perspective on data preprocessing.']","['The contributions, mainly modifying dataset generation functions and logging performance metrics, may not be sufficiently novel or impactful for a top-tier ML venue.', 'The analysis of the results is somewhat superficial and lacks depth and broader implications.', ""The paper is not well organized, with key sections such as 'Related Work' and 'Conclusions' missing or incomplete."", 'The evaluation is limited to a specific set of transformations and a single model architecture, which reduces the generalizability of the findings.', 'Lacks depth in theoretical analysis and does not provide sufficient insight into why certain transformations impact grokking.', 'Underdeveloped related work section that fails to adequately situate the research within the broader context of existing literature.', 'Limited evaluation metrics that may not capture all aspects of model performance and generalization.', 'The clarity of the paper is poor; the organization and presentation of results are confusing.', 'Fails to sufficiently advance the state of the art or provide actionable insights for future research.']",2,2,2,2,Reject
predictive_uncertainty,"The paper investigates the impact of predictive uncertainty on the grokking phenomenon in neural networks, particularly within modular arithmetic and permutation groups. It proposes quantifying predictive uncertainty using Monte Carlo dropout and entropy measures during inference. The contributions include modifying the Transformer architecture to output predicted classes and uncertainty measures, updating training and evaluation protocols, and analyzing the correlation between predictive uncertainty and validation accuracy.","['Can the authors provide more details on how the Monte Carlo dropout is implemented and its computational impact?', 'How does the approach perform on more diverse and complex datasets beyond modular arithmetic and permutation groups?', 'Can the authors include additional ablation studies to demonstrate the effectiveness of the proposed modifications and uncertainty measures?', 'What are the specific modifications made to the Transformer architecture to output uncertainty measures?']","['The reliance on Monte Carlo dropout increases computational overhead during inference.', 'The approach may not generalize well to more complex datasets, as evidenced by the permutation dataset results.']",False,2,2,2,3,4,"['Addresses an interesting and practical problem of understanding the grokking phenomenon.', 'Proposes a novel application of Monte Carlo dropout and entropy measures to quantify predictive uncertainty in the context of grokking.', 'Comprehensive experimental setup with detailed analysis of training and validation metrics.']","['Limited novelty as it leverages existing techniques (Monte Carlo dropout, entropy measures) and applies them to an existing problem.', 'Lacks clarity in several sections, making it difficult to follow the methodology and results. Specifically, the implementation details of Monte Carlo dropout and the computational impact are not well-explained.', 'Experimental results are not entirely convincing, especially for more complex datasets like the permutation dataset. The paper does not convincingly demonstrate the generalizability of the approach to other types of neural network architectures and datasets.', 'Significant computational overhead due to Monte Carlo dropout during inference, which may limit practical applicability.']",3,2,2,2,Reject
optimizer_choice,"The paper investigates the impact of different optimizer choices on the grokking phenomenon, where models generalize beyond overfitting on small algorithmic datasets. The study systematically evaluates four popular optimizers: SGD, RMSprop, Adagrad, and AdamW, across multiple datasets to understand their influence on training dynamics and final performance. The results show that AdamW consistently outperforms other optimizers in terms of speed to grokking and final validation accuracy.","['Can the authors provide a more in-depth theoretical explanation for why AdamW outperforms other optimizers?', 'What are the specific implementation details for the experiments, and why were certain hyperparameters chosen?', 'Can the authors explain the rationale behind the choice of datasets and how they are representative of the grokking phenomenon?', 'Have you considered other optimizers beyond SGD, RMSprop, Adagrad, and AdamW?', 'How do you justify the fixed set of hyperparameters used in your experiments?', 'Have you considered the impact of learning rate schedules and batch size variations on the grokking phenomenon?', 'Why were only these four optimizers chosen for the study? Would including more optimizers provide a broader understanding?', 'Can the authors provide more detailed analysis on the variance in training stability across different runs?', 'Can the authors provide more insights into how the findings might generalize to larger or more complex datasets?']","['The study is limited to four optimizers and small algorithmic datasets, which may not generalize to larger or more complex datasets.', 'The paper does not explore the impact of other hyperparameters, such as learning rate schedules and batch sizes.', 'The fixed set of hyperparameters could bias the results.', 'Lacks theoretical analysis to support empirical findings.']",False,2,2,2,3,4,"['Addresses a relevant and practical problem in machine learning related to optimizers and generalization.', 'Provides comprehensive experiments across multiple datasets.', 'Shows that AdamW consistently outperforms other optimizers in terms of speed to grokking and final validation accuracy.']","['Limited originality as the study primarily benchmarks known optimizers.', 'Lacks significant novelty; findings are in line with existing literature.', 'The analysis lacks depth, particularly in explaining why AdamW performs better.', 'The scope of the paper is limited to only four optimizers and small algorithmic datasets, which restricts the breadth of the study.', 'Fixed set of hyperparameters raises concerns about generalizability.', 'Writing is somewhat repetitive and could be more concise.']",2,2,3,2,Reject
latent_space_manipulation,"The paper investigates the impact of latent space manipulation on the grokking phenomenon in neural networks. By introducing Gaussian noise and L2 norm regularization to the latent space, the authors aim to understand how these modifications influence generalization and training dynamics. Experiments are conducted on datasets involving modular arithmetic and permutation groups.","['Why did the authors choose Gaussian noise and L2 regularization specifically? Are there theoretical justifications for these choices?', 'Can the authors provide more detailed explanations for their experimental setup, particularly the modifications to the DecoderBlock class?', 'How do these findings generalize to real-world datasets? Have any preliminary tests been conducted on more complex datasets?', 'Have the authors considered comparing their approach with other related work or baselines to better assess its effectiveness?', 'Can the authors include additional ablation studies to isolate the effects of Gaussian noise and L2 norm regularization?', 'Can the authors explore other forms of latent space manipulation and their impact on grokking? For example, what about dropout or other regularization techniques?']","['The experiments are conducted on synthetic datasets, which may not fully capture the complexities of real-world data.', 'The paper relies on specific forms of latent space manipulation, limiting the generalizability of the findings.', 'The lack of comparison with related work or baselines makes it challenging to evaluate the relative effectiveness of the proposed methods.']",False,2,2,2,3,4,"['Investigates an intriguing phenomenon (grokking) that is not well understood.', 'Proposes a novel angle by focusing on latent space manipulation.', 'Provides comprehensive experimental results and visualizations using t-SNE and PCA.']","['The techniques (Gaussian noise and L2 norm regularization) are well-known and not particularly innovative.', 'The paper lacks depth in both theoretical and empirical analysis, particularly in explaining why the chosen methods affect grokking.', 'Experimental results are limited to synthetic datasets, making the findings less generalizable to real-world scenarios.', 'Lacks detailed explanations on certain aspects, such as the choice of hyperparameters and the implementation details of the DecoderBlock modifications.', 'Limited exploration of other forms of latent space manipulation beyond Gaussian noise and L2 norm regularization.']",3,2,2,2,Reject
data_distribution,"The paper investigates the impact of different data distributions (uniform, normal, and skewed) on the grokking phenomenon, where models generalize well beyond overfitting on small algorithmic datasets. The authors modify dataset generation classes to include various data distribution techniques and conduct comprehensive experiments to analyze their effects on training and validation performance.","['Can the authors provide more detailed analysis on how different model architectures and hyperparameters might impact the grokking phenomenon?', 'How do the findings on synthetic datasets translate to real-world scenarios?', 'Can the authors improve the clarity of the methodology and experimental setup?', 'Have you considered evaluating the impact of data distribution on grokking using real-world datasets?', 'How might different model architectures or more complex models affect the observed results?', 'What are the potential limitations of using synthetic datasets in this study, and how might they affect the generalizability of the findings?']","['The study is limited to synthetic datasets, which may not fully capture real-world complexities.', 'The impact of different model architectures and hyperparameters on grokking is not explored.', ""There is a lack of discussion on potential negative societal impacts, though the study's nature might mitigate such risks.""]",False,2,2,2,3,4,"['The paper addresses a relevant and interesting topic in the machine learning community.', 'The methodology is thorough, involving modifications to dataset generation and extensive experiments.', 'The results are clearly presented, with detailed analysis of different metrics such as speed to grokking, final validation accuracy, and training stability.']","['The experiments are conducted on synthetic datasets, which may not fully capture the complexities of real-world data.', 'The impact of different model architectures and hyperparameters on grokking is not deeply explored.', 'The clarity and depth of the analysis are lacking, with some sections feeling rushed and not thoroughly explained.', 'The contribution of the paper is somewhat limited and predictable, as it is expected that normal and skewed distributions would lead to faster learning compared to uniform distribution.']",2,2,2,2,Reject
initialization_schemes,"The paper investigates the impact of different initialization schemes and hyperparameters on the performance of Transformer models for modular arithmetic and permutation tasks. The study aims to understand how these factors influence training stability, speed to grokking, and final validation accuracy. The authors propose a comprehensive evaluation framework and validate their approach through extensive experiments.","['Can the authors provide more detailed experimental results and analysis?', 'Why do certain initialization schemes and hyperparameters perform better than others?', 'How do the findings generalize to real-world tasks and datasets?', 'Can the authors provide a more thorough discussion of related work?', 'Can the authors provide more details on the synthetic datasets used, including how they were generated and whether they represent any real-world scenarios?', 'How do the authors plan to validate their findings on more complex and diverse datasets?', 'Can the authors elaborate on any potential ethical concerns or societal impacts of their work?']","['The study is limited to synthetic datasets for modular arithmetic and permutation tasks, which may not capture the complexities of real-world data.', 'The experiments are limited in scope, and the results are not presented in a manner that allows for easy comparison.', ""The paper's focus on well-known initialization schemes and standard hyperparameters limits its novelty."", 'Further exploration of additional neural network architectures and extending evaluations to real-world datasets would enhance the significance of the results.', 'The paper does not adequately address the limitations and potential negative societal impacts of the work.']",False,2,2,2,3,4,"['The paper addresses an important challenge in neural network training by optimizing initialization schemes and hyperparameters.', 'The comprehensive experimental setup and evaluation framework are well-detailed and robust.', 'The results provide insights into the impact of initialization schemes and hyperparameters on model performance.']","['The novelty of the approach is limited, focusing mainly on well-known initialization schemes and standard hyperparameters.', 'The clarity of some sections, particularly the methodology and evaluation metrics, is lacking and could be improved.', 'The tasks and datasets used are synthetic, which might not fully capture the complexities of real-world data.', 'The significance of the results is questionable, and the paper could benefit from extending evaluations to more complex tasks and real-world datasets.', 'The paper lacks sufficient theoretical justification for the choice of initialization schemes and hyperparameters.', 'The experimental setup and results are not well-contextualized within existing work, making it difficult to assess the significance of the findings.', 'The paper does not address potential ethical concerns or societal impacts, which is an important consideration for any ML study.']",2,2,2,2,Reject
adversarial_training,"The paper investigates the impact of adversarial training on the grokking phenomenon in neural networks, using the Fast Gradient Sign Method (FGSM) to generate adversarial examples. The study evaluates the effect on various datasets and analyzes metrics such as speed to grokking, final validation accuracy, and training stability.","['Can the authors provide more details on the choice of hyperparameters and the specific implementation of the adversarial training process?', 'How do the results vary with different neural network architectures and more complex real-world datasets?', 'Can the authors address the formatting issues in the figures and tables for better clarity?', 'Can the authors provide more detailed explanations in the methodology section?', 'What statistical methods were used to verify the significance of the results?', 'Can the authors include more comprehensive ablation studies to support their claims?', 'Can the authors provide a clearer explanation of how the adversarial examples are integrated into the training process?', 'What specific changes were made to the dataset generation classes to accommodate the adversarial examples?', 'How do the adversarial examples influence the training dynamics and model behavior?', 'Can the authors elaborate on the limitations and potential biases introduced by the choice of hyperparameters and fixed epsilon values?', 'Please provide a more detailed explanation of the autoencoder aggregator and how it is implemented.', 'Can you provide more comprehensive qualitative analysis and better-formatted tables and figures to present the experimental results?', 'Please conduct more thorough ablation studies to evaluate the impact of different components of the proposed method.', 'Why were only synthetic datasets used for the experiments, and how do the authors plan to extend this to real-world tasks?', 'Can the authors provide a more detailed analysis of the results, especially for the permutation dataset?']","['The paper does not adequately discuss the broader applicability of the findings to more complex neural network architectures and real-world datasets.', 'The choice of hyperparameters and the fixed epsilon values may introduce biases that are not thoroughly explored.', 'The paper lacks a thorough discussion of potential ethical concerns or societal impacts of adversarial training.', 'The practical implications of the findings are not discussed in depth.']",False,2,2,2,3,4,"['The paper addresses an interesting and relevant topic in the field of machine learning.', 'The application of adversarial training to the grokking phenomenon is a novel idea.', 'Experiments are conducted on a variety of datasets, providing a broad evaluation.']","['The concept of adversarial training is not new, and while the application to grokking is novel, it is not sufficiently groundbreaking.', 'The paper lacks a thorough theoretical analysis and relies heavily on empirical results.', 'The experimental setup is somewhat limited, and the results are not sufficiently robust across datasets.', 'The paper is not well-organized, with some sections being difficult to follow.', 'There are formatting issues in the figures and tables, which detract from the overall readability.', 'The impact of the findings is limited, with improvements in robustness and generalization not being substantial enough.', 'There are unanswered questions about the choice of hyperparameters, the specific implementation details, and the broader applicability of the findings.', 'The significance of the results is limited, and the broader impact is not well articulated.', 'The paper lacks clarity in describing the modified dataset generation process and the integration of adversarial examples.', 'Evaluation metrics and analysis are not comprehensive enough to draw strong conclusions.']",2,2,2,2,Reject
catastrophic_forgetting,"The paper explores the application of Transformer models to modular arithmetic operations and permutation groups, which are fundamental in fields such as cryptography and combinatorics. The authors claim that their approach leverages the self-attention mechanism of Transformers to achieve high accuracy, outperforming baseline methods. However, the paper is incomplete, with several sections left unfilled, including the Introduction, Related Work, Background, Method, Experimental Setup, Results, and Conclusions. This makes it difficult to evaluate the validity and significance of the work.","['Can the authors provide a more detailed description of the methodology and experimental setup?', 'What specific metrics were used to measure the accuracy of the model?', 'Are there any limitations or potential negative societal impacts of this work that the authors have considered?', 'Please provide detailed descriptions of the Transformer architecture used and any specific modifications made for the tasks.', 'Can you elaborate on the baseline methods used for comparison?', 'Could you provide a more comprehensive analysis of the experimental results?', 'What baseline methods were used for comparison, and how was the improvement in performance measured?', 'Can you provide more details on the experimental setup and the datasets used?', 'What is the theoretical justification for using Transformers in these tasks?', 'What are the specific challenges addressed by using Transformers for these tasks, and how are they mitigated in the proposed approach?']",['The paper does not address any limitations or potential negative societal impacts of the work.'],False,1,1,1,2,4,"['Addresses a challenging and important problem in cryptography and combinatorics.', 'The application of Transformers to discrete and combinatorial tasks is novel and interesting.']","['The paper is incomplete, with several sections left unfilled (Introduction, Related Work, Background, Method, Experimental Setup, Results, and Conclusions).', 'The methodology is not clearly defined; there is a lack of detail on how the model is implemented and trained.', 'The experimental setup is not described, making it difficult to evaluate the validity of the results.', ""Results are mentioned but not presented in a detailed or meaningful way; there's no data or metrics provided."", 'No discussion on the limitations of the approach or potential negative societal impacts.', 'No evidence of ethical considerations being accounted for.']",2,1,1,2,Reject
capacity_task_complexity,"The paper investigates the interplay between model capacity and task complexity within the grokking phenomenon using a Transformer model and algorithmic datasets. It performs a comprehensive grid search over model layers and dimensions, analyzing training and validation performance across configurations. The study aims to identify optimal model configurations for different levels of task complexity.","['Can the authors provide a clearer theoretical foundation for the grokking phenomenon?', 'Why were only algorithmic datasets and Transformer models used in the experiments?', 'Can the authors provide more detailed analysis and explanation of the results and visualizations?', 'Have the potential negative societal impacts or ethical considerations of this work been considered?', 'Can the authors provide a detailed analysis of the impact of different hyperparameters, such as learning rate and batch size?', 'How do the findings generalize to other model architectures and more complex tasks?', 'Can the authors perform more detailed ablation studies to understand the contributions of different components of the proposed approach?', 'Can you provide more theoretical insights into why specific model configurations perform better?', 'What is the rationale behind the chosen hyperparameters? Could different hyperparameter settings affect the results?', 'Can you include more visualizations and detailed descriptions in the qualitative analysis?']","['The study is limited to algorithmic datasets and Transformer models, which restricts the generalizability of the findings.', 'The paper lacks a thorough analysis and explanation of the results.', 'There is no discussion on potential negative societal impacts or ethical considerations.', 'Further research is needed to generalize these findings to other model architectures and more complex tasks.']",False,2,2,2,3,4,"['Addresses a relevant and practical problem in machine learning: the balance between model capacity and task complexity.', 'Employs a systematic approach with a comprehensive grid search over model configurations.', 'Extensive experimentation with varied metrics such as speed to grokking, final validation accuracy, and training stability.']","['Relies heavily on the grokking phenomenon without providing a clear theoretical foundation or novel methodology.', 'Experiments are limited to algorithmic datasets and Transformer models, restricting the generalizability of findings.', 'Lacks thorough analysis and explanation of results, with inadequate visualizations.', 'Misses discussion on potential negative societal impacts or ethical considerations.', 'The novelty of the paper is incremental, building on existing work without significant new insights.', 'Figures and tables need better captions and discussions to enhance clarity.']",2,2,2,2,Reject
data_augmentation,"The paper investigates the impact of data augmentation techniques on the grokking phenomenon in neural networks, focusing on modular arithmetic and permutation group datasets. The study aims to understand how augmentation strategies, such as adding random noise and shuffling operations, affect the speed to grokking, final validation accuracy, and training stability. The authors use a Transformer-based neural network architecture and conduct extensive experiments to evaluate the effects of these techniques.","['Can the authors provide more details on the choice of hyperparameters and architectural configurations?', 'How do the authors ensure the robustness and reproducibility of the results?', 'Can the authors discuss the limitations of using synthetic datasets and the implications of the findings on real-world data?', 'What measures have been taken to ensure the ethical considerations and potential negative societal impacts of the work?', 'Can the authors provide more details on the implementation of the data augmentation techniques?', 'Have the authors considered applying their findings to more complex or real-world datasets?', 'What is the rationale behind choosing the specific noise levels (0.1, 0.2, 0.3, 0.4) for data augmentation?', 'Can the authors include more comprehensive visualizations of the training and validation performance across different noise levels?', 'How do the findings relate to or extend existing work on data augmentation and generalization in neural networks?', 'Could the authors clarify if and how their results could be applied to more complex, real-world datasets?']","['The use of synthetic datasets may not capture the complexities of real-world data, limiting the practical relevance of the findings.', 'The lack of statistical analysis and significance measures for the experimental results raises concerns about the robustness of the findings.', 'The paper does not adequately discuss the limitations and potential negative societal impacts of the work.', 'Potential ethical considerations related to data augmentation and its impact on model fairness should be addressed.']",False,2,2,2,3,4,"['The study addresses an interesting and relatively unexplored area in neural network training dynamics.', 'The paper provides a detailed experimental setup, including the use of modular arithmetic and permutation group datasets, which are well-defined and structured for analyzing the grokking phenomenon.', 'The use of a Transformer-based neural network architecture is appropriate for capturing complex patterns in the data.']","['The clarity of the paper is lacking in several sections, particularly in the explanation of the experimental setup and the results.', 'The paper does not provide comprehensive statistical analysis or significance measures for the experimental results, which makes it difficult to assess the robustness of the findings.', 'The choice of synthetic datasets may limit the generalizability of the results to real-world scenarios, and this limitation is not adequately discussed.', 'The paper lacks a thorough analysis of the limitations and potential negative societal impacts of the work.', 'The results and analyses are not well-organized, and critical details are missing. For instance, the visualization of training and validation loss curves is referenced but not included in the main text.', 'The significance of the findings is not convincingly demonstrated. The paper fails to provide strong evidence that the proposed data augmentation techniques lead to improved generalization in a meaningful way.', 'The clarity of the writing could be improved. The paper is dense with technical jargon and lacks clear explanations for some of the key concepts and results.']",2,2,2,2,Reject
architecture_variation,"The paper investigates the impact of varying Transformer architecture parameters on the grokking phenomenon, where models initially overfit and then suddenly generalize well. The authors conduct a comprehensive grid search over different configurations, varying the number of layers and model dimensions. They analyze training and validation performance, speed to grokking, and stability across architectures. The study finds that models with fewer layers and smaller dimensions tend to grok faster, while deeper and larger models exhibit more stable training dynamics.","['Can you explore other potentially influential factors such as different attention mechanisms and normalization techniques?', 'Can you provide more comprehensive and detailed ablation studies?', 'Can you offer deeper insights into the underlying reasons for the observed phenomena?', 'Can you improve the clarity and organization of the paper?', 'How do the findings generalize to other types of tasks beyond the algorithmic datasets used?', 'Would a more targeted experimental design offer better insights than a grid search?', 'Are there specific reasons why the chosen tasks (e.g., modular arithmetic) are particularly suited for studying the grokking phenomenon?', 'Can you provide more insights into the computational cost of the grid search approach and possible alternatives?']","['The study is limited to a specific set of algorithmic tasks, which may not generalize to other domains.', 'The grid search approach is computationally expensive and may not be the most efficient way to explore the impact of architectural parameters.', 'The paper does not address potential negative societal impacts or ethical considerations.']",False,2,2,2,3,4,"['Addresses a relevant and intriguing phenomenon in machine learning.', 'Systematically explores the impact of Transformer architecture parameters.', 'Provides practical guidelines for designing efficient Transformer models.']","['The grid search approach is computationally expensive and may not offer the most efficient insights.', 'The analysis lacks depth and does not provide significant new theoretical insights.', 'Figures and qualitative analysis are somewhat generic and not very informative.', 'The scope of tasks is limited to algorithmic datasets, which may not generalize to other types of tasks.', 'The paper does not adequately address the limitations and potential negative societal impacts of its findings.', 'The clarity of the paper is lacking, with some sections being difficult to follow or understand.']",2,3,3,2,Reject
batch_size_grokking,The paper proposes a novel training strategy to dynamically adjust batch sizes to tackle the grokking phenomenon in small algorithmic datasets. The method starts with a small batch size and gradually increases it during training to balance training speed and generalization performance. Experimental results show that the proposed method can lead to faster generalization and improved performance compared to baseline approaches.,"['How does the proposed dynamic batch size adjustment strategy compare with other existing dynamic batch size methods in terms of grokking?', 'Can the authors provide more detailed statistical analysis and comparison with strong baselines?', 'Can the authors provide a more detailed description of the model architecture and the dynamic batch size adjustment strategy?', 'What are the potential limitations and negative impacts of the proposed method in large-scale datasets and real-world scenarios?']","['The paper does not adequately address the limitations of the proposed method, such as feasibility in large-scale datasets and real-world scenarios.', 'Potential negative impacts and ethical considerations are not mentioned.']",False,2,2,2,3,4,"['The paper addresses an interesting and challenging problem (grokking phenomenon) in the context of small algorithmic datasets.', 'The proposed dynamic batch size adjustment strategy is a novel approach that leverages the benefits of both small and large batch sizes during different stages of training.', 'The paper provides empirical evidence that the proposed method can achieve faster generalization and improved performance compared to baseline approaches.']","['The concept of dynamically adjusting batch sizes is not entirely new, and the paper lacks a thorough comparison with related works in dynamic batch size adjustments.', 'The results are not very convincing due to the lack of detailed statistical analysis and insufficient comparison with strong baselines.', 'The description of the model architecture and the dynamic batch size adjustment strategy are not detailed enough, making it difficult for others to reproduce the results.', 'The paper does not adequately address the limitations of the proposed method, such as feasibility in large-scale datasets and real-world scenarios.', 'Potential negative impacts and ethical considerations are not mentioned.']",3,2,2,3,Reject