q-tuning-joint-pruning-efficient-training

Dramatically reduce training data requirements (to 12.5% of original) while improving model performance using joint sample and token pruning guided by Error-Uncertainty plane diagnostics. Asymmetric pruning preserves calibration signals while removing redundant tokens from misconception examples.

Independent GitHub snapshot · Automated classification · Original documentation governs