Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
The Quantization Model of Neural Scaling
Authors: Eric Michaud, Ziming Liu, Uzay Girit, Max Tegmark
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate this prediction on toy datasets, then study how scaling curves decompose for large language models. Using language model gradients, we automatically decompose model behavior into a diverse set of skills (quanta). |
| Researcher Affiliation | Academia | Eric J. Michaud , Ziming Liu, Uzay Girit, and Max Tegmark MIT & IAIFI |
| Pseudocode | No | The paper describes a method named 'Quanta Discovery from Gradients (QDG)' with sequential steps in paragraph format, but it does not present it as a formally structured pseudocode or algorithm block. |
| Open Source Code | Yes | Project code can be found at: https://github.com/ejmichaud/quantization-model. |
| Open Datasets | Yes | For our experiments, we use the Pythia model suite from Eleuther AI [29], a set of decoder-only transformers of varying size trained on approximately 300 billion tokens of The Pile [30]. |
| Dataset Splits | No | The paper mentions training and testing on datasets but does not provide specific percentages or counts for training/validation/test splits, nor does it explicitly detail predefined splits with citations for reproducibility. |
| Hardware Specification | Yes | Availble GPUs include NVIDIA A100, RTXA6000, QUADRORTX6000, GEFORCERTX2080TI, GEFORCERTX2080, GEFORCEGTX1080TI, titan-x, and tesla-v100. |
| Software Dependencies | No | The paper mentions using 'scikit-learn [45]' but does not provide specific version numbers for this or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | We use the Adam optimizer with a learning rate of 10-3. To study scaling with respect to the number of model parameters, we train networks of varying width by sampling batches online... For the results shown, we used ntasks = 500, n = 100, k = 3, α = 0.4, and a batch size of 20000. We vary training dataset size from 1e4 to 5e6 and vary hidden-layer width from 10 to 500 neurons. We train for 2e5 steps. |