Zeroth Order Optimization for Pretraining Language Models
Bibliographic record
Abstract
ABSTRACT: The physical memory for training Large Language Models (LLMs) grow with the model size, and are limited to the GPU memory. In particular, back-propagation that requires the computation of the first-order derivatives adds to this memory overhead. Training extremely large language models with memory-efficient algorithms is still a challenge with theoretical and practical implications. Back-propagation-free training algorithms, also known as zeroth-order methods, are recently examined to address this challenge. Their usefulness has been proven in fine-tuning of language models. However, so far, there has been no study for language model pretraining using zeroth-order optimization, where the memory constraint is manifested more severely. We build the connection between the second order, the first order, and the zeroth order theoretically. Then, we apply the zeroth order optimization to pre-training light-weight language models, and discuss why they cannot be readily applied. We show in p articular that the curse of dimensionality is the main obstacle, and pave the way towards modifications of zeroth order methods for pre-training such models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".