Unverified50% confidenceCautionExact time
For teams with limited resources, scaling up the teacher model requires allocating a corresponding computational budget to the critic at the same time, otherwise it may backfire.
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11/2/2026
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Related Claims
UnverifiedA joint scaling strategy that scales the critic in tandem with the teacher prevents training collapse and enables the student model to benefit from a larger teacher.72% similarUnverifiedThe paper observes that supervision signals from a fixed teacher become increasingly less worthwhile as the student model's capabilities improve70% similarUnverifiedThe limitation of distillation is that the student model's capability ceiling is constrained by the teacher's performance on that task, and it is difficult to generalize to new tasks outside the training distribution.68% similarUnverifiedOn-policy distillation requires the student to first generate its own prefix trajectory, after which the teacher provides guidance at the states the student actually reaches, so the state distributions are aligned, and it is usually more stable on long-horizon tasks where errors tend to accumulate.68% similarUnverifiedWhen model parameters and data volume reach a certain scale, some capabilities not explicitly taught during training emerge, such as few-shot learning, chain-of-thought reasoning, and cross-lingual transfer.67% similar
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