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
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs
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.
-
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.