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MAIGO: Mitigating Lost-in-Conversation with History-Cleaned On-Policy Self-Distillation
Large language models often solve tasks from a fully specified prompt but degrade when the same requirements unfold over multiple turns, known as the lost-in-conversation (LiC) gap. We trace part of this degradation to self-contamination: intermediate assistant replies enter later context and carry
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