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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Scaling Data-Constrained Language Models
Authors: Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, Colin A. Raffel
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 24,916 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,254 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. |
| Researcher Affiliation | Collaboration | 1 Hugging Face 2 Harvard University 3 University of Turku |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations. |
| Open Datasets | Yes | Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations. |
| Dataset Splits | No | The paper mentions training models on 'subsets of C4' and reporting 'validation loss' and 'held-out test set' but does not provide specific train/validation/test dataset splits (e.g., percentages or exact sample counts) for reproducibility. |
| Hardware Specification | No | The paper mentions using 'generous computational resources on the LUMI supercomputer' but does not provide specific hardware details like GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as Python, PyTorch/TensorFlow, or CUDA. |
| Experiment Setup | Yes | We use cosine learning rate schedules that decay 10 over the course of training for each model... we do not use early stopping... Other hyperparameters are based on prior work [89, 42] and detailed in Appendix S. |