Experiments across code LLMs show no-review collapses fastest, human-gated filters slow collapse, and AI self-gates lose effect over time, degenerating to ungated self-training under self-confirming acceptance as proven via gated distributional reweighting and spectral analysis.
Comparing rewinding and fine-tuning in neural network pruning.arXiv preprint arXiv:2003.02389, 2020
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STARFISH recovers accuracy in pruned neural networks by optimizing internal state alignment to the original model with a minimal unlabeled calibration set, outperforming prior recovery methods especially at high pruning ratios.
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STARFISH: faST Accuracy Recovery in pruned networks From Internal State Healing
STARFISH recovers accuracy in pruned neural networks by optimizing internal state alignment to the original model with a minimal unlabeled calibration set, outperforming prior recovery methods especially at high pruning ratios.