weak-driven-learning-logit-mixing

Break through supervised fine-tuning saturation by mixing logits from weaker model checkpoints into strong model training targets. Amplifies hard negatives that strong models have already suppressed, enabling continued learning after standard training plateaus.

Independent GitHub snapshot · Automated classification · Original documentation governs