Anytime Minibatch with Delayed Gradients: System Performance and Convergence Analysis
Why this work is in the frame
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Bibliographic record
Abstract
We present convergence analysis of Anytime Minibatch with Delayed Gradients (AMB-DG) algorithm. In AMB-DG, workers compute gradients in epochs of fixed duration while the master uses stale gradients to update the optimization parameters. We analyze AMB-DG in terms of its regret bound and convergence rate. We present results for convex smooth objective functions which show that AMB-DG achieves the optimal regret bound and convergence rate. To complement our theoretical contribution, we deploy AMB-DG on SciNet, an academic high performance cloud computing platform, and compare its performance with that of the K-batch async scheme. K-batch async provides a baseline for schemes that exploit works completed by all workers while using stale gradients. In our experiments, for MNIST AMB-DG converges 2.45 times faster than K-batch async.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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