MAIGO: Mitigating Lost-in-Conversation with History-Cleaned On-Policy Self-Distillation
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Computer Science > Computation and Language
Title:MAIGO: Mitigating Lost-in-Conversation with History-Cleaned On-Policy Self-Distillation
Abstract: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 early deviations forward. Motivated by this mechanism, we propose MAIGO, an on-policy self-distillation method that reduces this contamination using history-cleaned references from the model's own policy. For middle turns, MAIGO removes prior assistant replies while preserving the user-visible sharded prefix; for answer turns, it distills from paired full-view references conditioned on the completed user-side dialogue. A reliability weight downweights middle-turn samples that disagree with the clean reference. MAIGO requires no verifier rewards, state labels, or inference-time scaffolding. Under the LiC paired-view protocol with deterministic verifiers, MAIGO improves Qwen2.5-7B-Instruct SHARDED accuracy from 52.8 to 66.1 and the SHARDED/FULL ratio from 66.5% to 84.1%, while keeping FULL accuracy within 2.3 points. These results show that self-contamination is a trainable component of the LiC gap.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.27186 [cs.CL] |
| (or arXiv:2605.27186v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.27186
arXiv-issued DOI via DataCite (pending registration)
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