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 Fine-Grained Complexity of Gradient Computation for Training Large Language Models
Authors: Josh Alman, Zhao Song
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is a purely theoretical paper, and it doesn t include any experiments. |
| Researcher Affiliation | Academia | Josh Alman Department of Computer Science Columbia University EMAIL Zhao Song Simons Institute for the Theory of Computing University of California, Berkeley EMAIL |
| Pseudocode | No | The paper describes algorithmic ideas and steps but does not include any explicit pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | This paper is a purely theoretical paper, and it doesn t include any experiments. |
| Open Datasets | No | This paper is a purely theoretical paper, and it doesn t include any experiments. |
| Dataset Splits | No | This paper is a purely theoretical paper, and it doesn t include any experiments. |
| Hardware Specification | No | This paper is a purely theoretical paper, and it doesn t include any experiments. |
| Software Dependencies | No | This paper is a purely theoretical paper, and it doesn t include any experiments. |
| Experiment Setup | No | This paper is a purely theoretical paper, and it doesn t include any experiments. |