Overtaught, Underlearned: Learner-Centered Distillation from Multi-Agent Discussion

arXiv:2610.16543v1 Announce Type: new Abstract: Multi-agent discussion produces reasoning collaboratively, while deployment still relies on a smaller student that must answer alone. Full-graph distillation transfers such discussions by treating each correct solution as a separate training target, but what teachers provide need not match what the student learns. This mismatch appears in two ways: several correct solutions may amount to the same lesson in the student's representation (over-teaching), while some training questions remain missed after learning (under-learning). We quantify both wi

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