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Core information and assessment summary
The paper presents a clear, logical flow from identifying the problem of scaling challenges and HP tuning in LLMs to proposing a solution (CompleteP), providing both theoretical analysis and extensive empirical validation, discussing findings, limitations, and implications.
Strengths: Detailed description of experimental setup and parameterizations tested (Section 3, Appendix I, Table 1, Table 4)., Rigorous empirical comparison of parameterizations across different dimensions (depth, compute-optimal settings, downstream tasks)., Inclusion of control tests (coordinate check) to verify theoretical expectations for stability (Figure 7)., Theoretical derivations provided to support the proposed scaling rules (Appendix D, E).
Weaknesses: Acknowledged limitations in scale of experiments due to compute constraints., Reliance on specific hardware (Cerebras CS-3) which might affect generalizability or reproducibility for others., Scaling law fits for larger models based on only 3 data points.
The claims regarding CompleteP's HP transfer and compute efficiency are well-supported by extensive empirical results presented in figures and tables (Figures 2, 3, 4; Table 2, 3). Theoretical analysis provides plausible explanations for the observed phenomena.
The identification and empirical validation of CompleteP (a=1) as a superior parameterization for joint depth/width scaling, the introduction of the 'complete feature learning' desideratum, and the re-examination of N:L ratios under these controls represent significant novel contributions beyond prior work on µP and depth scaling.
The findings have high potential importance for the field of large language model training by offering methods to reduce computational costs, improve training efficiency, and potentially unlock the use of different model shapes on various hardware, addressing key challenges in scaling LLMs.
Strengths: Precise terminology and formal academic style., Clear problem statement and objective., Well-structured sections., Use of figures and tables to illustrate complex results.
Areas for Improvement: Understanding full experimental details requires consulting multiple appendices., Some theoretical derivations in appendices assume significant prior knowledge.
Theoretical: Formalization of a refined set of desiderata for HP transfer, including the novel concept of 'complete feature learning', and theoretical justification for why CompleteP (a=1) satisfies these desiderata.
Methodological: Extension of depth-aware parameterizations to include principled re-scalings for LayerNorm, bias learning rates, and AdamW epsilon. Rigorous empirical methodology for comparing parameterizations across depth and in compute-optimal settings.
Practical: Identification and validation of CompleteP as a parameterization enabling effective HP transfer and compute efficiency for deep transformers, potentially reducing HP tuning costs and unlocking a wider range of compute-efficient model shapes (including deep-narrow models) for different hardware/operational needs.
Topic Timeliness: High
Literature Review Currency: Good
Disciplinary Norm Compliance: The paper adheres well to the standard norms of empirical and theoretical research in machine learning, including providing theoretical motivation, detailed experimental methods, quantitative results, and discussion of limitations.
Inferred Author Expertise: Deep Learning, Large Language Models, Neural Network Scaling, Hyperparameter Optimization, Theoretical Machine Learning, Computational Hardware (Cerebras Systems)
Evaluator: AI Assistant
Evaluation Date: 2025-05-07
The identification and empirical validation of CompleteP (a=1) as a superior parameterization for joint depth/width scaling, the introduction of the 'complete feature learning' desideratum, and the re-examination of N:L ratios under these controls represent significant novel contributions beyond prior work on µP and depth scaling.