Anytime Minibatch with Stale Gradients : (Invited Paper)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Large-scale machine learning problems are often solved using distributed optimization techniques by dividing tasks across multiple compute nodes. Due to variability in compute time amongst nodes, distributed systems suffer from slow nodes known as stragglers that can have a big impact on convergence speed. Recently, Anytime Minibatch (AMB) technique has been proposed to speed up convergence by exploiting work done by stragglers rather than avoiding them. In AMB, workers are given a fixed time to calculate gradients, followed by a fixed communication time to average gradients. We observe that nodes stay idle during communication time. In our work, we allow workers to compute gradients during communication time resulting in what is known as stale gradients due to gradients calculated on out-of-date parameters. Our simulation results show that such approach achieves up to 60% faster convergence in wall time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it