weak-driven-learning-logit-mixing

【开发与工程】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.

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