Searching for the best student architecture in a knowledge distillation framework
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
Knowledge distillation aims to find a smaller model (i.e., student) that can perform at the level of a larger model (i.e., teacher). While the student model is highly beneficial in resource-constrained environments, finding the optimal student remains challenging due to the extensive search required through potential architectures and hyperparameters. To address this, we introduce a novel framework that integrates a caching mechanism and proximity analysis into Reinforcement Learning (RL) for Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO) in knowledge distillation. This approach improves computational efficiency by avoiding redundant evaluations and estimating the performance of similar configurations. Our results, benchmarked against foundational and modern evolutionary search methods, demonstrate that the proposed framework can reduce full training evaluations by over 75% relative to a standard RL search, offering a robust advantage in computationally or operationally constrained environments.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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