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Same Evidence, Different Answers: Canonical-Context On-Policy Distillation for Multi-Turn Language Models
Large language models (LLMs) often solve a task when all instructions are given in a single prompt, but fail when the same information is revealed gradually across turns. When a clean FULL prompt and a RAW-SHARDED conversation contain the same complete user evidence, the model should still arrive at
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