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arXiv · 2606.14368

Be My Tutor: On-Policy Co-Distillation for Mutual LLM Improvement via Peer Feedback

Woohyeon Byeon·Jiwon Jeon·Jeonghye Kim·Youngchul Sung
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Abstract

We study multi-domain LLM training in which two models, each stronger in a different domain, co-evolve by tutoring each other through on-policy feedback. Unlike one-way distillation or single-model fine-tuning, our goal is mutual Pareto improvement: each model improves across domains without losing its original strength. To this end, we propose On-Policy Co-Distillation (OPCoD), where each student's self-distillation is conditioned on its own correct rollout and feedback from its peer. To make feedback exchange effective, OPCoD uses cognizance-based gating to decide when to give feedback and feedback anchoring to ground feedback in the problem. On Science Q\&A tasks, OPCoD consistently outperforms baselines and achieves Pareto improvement across all evaluated domain pairs and students.

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Woohyeon Byeon, Jiwon Jeon, Jeonghye Kim, Youngchul Sung. 2026-06-12. Be My Tutor: On-Policy Co-Distillation for Mutual LLM Improvement via Peer Feedback. https://arxiv.org/abs/2606.14368

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