Asynchronous Delayed Optimization With Time-Varying Minibatches
Why this work is in the frame
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Bibliographic record
Abstract
Large-scale learning and optimization problems are often solved in parallel. In a master-worker distributed setup, worker nodes are most often assigned fixed-sized minibatches of data points to process. However, workers may take different amounts of time to complete their per-batch calculations. To deal with such variability in processing times, an alternative approach has recently been proposed wherein each worker is assigned a fixed duration to complete the calculations associated with each batch. This fixed-time approach results in time-varying minibatch sizes and has been shown to outperform the fixed minibatch approach in synchronous optimization. In this paper we make a number of contributions in the analysis and experimental verification of such systems. First, we formally present a system model of an asynchronous optimization scheme with variable-sized minibatches and derive the expected minibatch size. Second, we show that for our fixed-time asynchronous approach, the expected gradient staleness does not depend on the number of workers contrary to existing schemes. Third, we prove that for convex smooth objective functions the asynchronous variable minibatch method achieves the optimal regret and optimality gap bounds. Finally, we run experiments comparing the performances of the asynchronous fixed-time and fixed-minibatch methods. We present results for CIFAR-10 and ImageNet.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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