AsyncOPD shows asynchronous OPD training reaches 1.6-3.8x higher throughput than synchronous baselines with comparable accuracy by using forward-KL estimators and multi-sample Monte Carlo correction for finite teacher caches.
KDFlow: A user-friendly and efficient knowledge distillation framework for large language models.arXiv preprint
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SimCT enlarges the supervision space in cross-tokenizer on-policy distillation using short jointly tokenizable multi-token continuations, producing consistent gains over shared-token baselines on math and code benchmarks.
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AsyncOPD: How Stale Can On-Policy Distillation Be?
AsyncOPD shows asynchronous OPD training reaches 1.6-3.8x higher throughput than synchronous baselines with comparable accuracy by using forward-KL estimators and multi-sample Monte Carlo correction for finite teacher caches.
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SimCT: Recovering Lost Supervision for Cross-Tokenizer On-Policy Distillation
SimCT enlarges the supervision space in cross-tokenizer on-policy distillation using short jointly tokenizable multi-token continuations, producing consistent gains over shared-token baselines on math and code benchmarks.