Researchers introduce new on-policy distillation methods for LLMs
Recent papers propose seven distinct techniques to improve on-policy distillation (OPD), addressing challenges like prefix failure, cross-tokenizer alignment, and knowledge drift. These methods aim to optimize model training by grounding supervision in a student's own rollouts rather than relying solely on static datasets.
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