Sufficient is better than optimal for training neural networks
Bibliographic record
Abstract
The array of neural network training techniques that invoke optimization but rely on ad hoc modification for validity suggests that optimization-based training is misguided. Shortcomings of optimization-based training are brought to strong relief by overfitting, where naive optimization produces spurious outcomes. Here, we introduce simmering, a physics-based method that trains neural networks to generate “good enough” weights and biases, paradoxically outperforming leading optimization-based approaches. Instead of optimizing, simmering systematically samples non-optimal weights and biases to generate an ensemble that provides sufficient representations of the underlying phenomenon. Simmering corrects neural networks that are overfit by optimization, and produces more generalizable predictions if deployed from the outset compared to other overfitting mitigation methods. Our results question optimization as a paradigm for training transformers, and feedforward and convolutional neural networks. We leverage information-geometric arguments to point to the existence of classes of sufficient-training algorithms that do not take optimization as their starting point. The authors propose simmering, a physics-inspired alternative to optimization-based neural network training that generates weights through systematic sampling rather than optimization, to mitigate overfitting and achieve better generalization compared to conventional methods.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".