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• Log-likelihood based evaluation can significantly underestimate the performance gap between a teacher model and a distilled student model, potentially leading to misleading conclusions about design choices.limitation 95%
• A 7B parameter distilled model that appears to perform nearly identically to its teacher (0.2 pp difference) under log-likelihood scoring can exhibit a substantial performance deficit (20.8 pp difference) when evaluated using autoregressive generation.result 95%
• The proposed GenDistill pipeline, combined with the Hybrid Kimi Delta Attention (Hybrid-KDA) architecture, is designed to guide distillation decisions using generation-based evaluation.method 90%
• Dataset selection, completion-only loss masking, and freezing attention layers during post-training are identified as the most impactful factors for improving generation quality in distilled models.result 90%
• The best Hybrid-KDA model achieves 86-90% of the teacher's accuracy on knowledge benchmarks while reducing KV cache memory by up to 75% and improving time-to-first-token by 2-4x at 128K-token contexts.result 95%
• The Kimi Delta Attention (KDA) architecture allows for distillation with a smaller total token budget (approximately 800M tokens) compared to prior multi-stage distillation pipelines.technical 90%
• The GenDistill pipeline consists of three stages: attention mechanism initialization and alignment, block-wise hidden state alignment, and end-to-end knowledge distillation for error correction and task adaptation.method 90%
• Log-likelihood based evaluation can significantly underestimate the performance gap between a distilled model and its teacher, potentially leading to misleading conclusions about design choices.limitation 95%
• A Hybrid Kimi Delta Attention (Hybrid-KDA) architecture combined with a multi-stage distillation pipeline (GenDistill) can achieve high-quality generation in distilled models.method 90%
• Dataset selection, completion-only loss masking, and freezing attention layers during post-training are the most impactful factors for improving generation quality in distilled models.result 90%
• The proposed Hybrid-KDA model retains 86-90% of teacher accuracy on knowledge benchmarks while significantly reducing KV cache memory and improving time-to-first-token.result 95%
• The GenDistill pipeline uses a three-stage process: attention mechanism initialization and alignment, block-wise hidden state alignment, and end-to-end knowledge distillation.method 90%

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