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# Alternating Chain of Thought

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  1. arXiv

    Allspark lets a weak teacher pass reasoning to frozen strong models via alternating chains of thought, lifting ARC-AGI-2 accuracy from 78.1% to 81.6%

    The work introduces Allspark, a training and inference framework in which a weak teacher and a frozen copy of the same model alternate reasoning segments during training while only the teacher is updated from weak-model rollouts, and at inference a stronger frozen student replaces the frozen partner; across Qwen3-1.7B/4B math and reasoning tasks and Inkling-Small teacher evaluations on ARC-AGI-2 with Inkling, Kimi-K2.6, and Nemotron-3-Ultra students, accuracy gains are observed along with an explicit accuracy-token tradeoff.

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