Be My Tutor: On-Policy Co-Distillation for Mutual LLM Improvement via Peer Feedback
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Computer Science > Machine Learning
Title:Be My Tutor: On-Policy Co-Distillation for Mutual LLM Improvement via Peer Feedback
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.
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.14368 [cs.LG] |
| (or arXiv:2606.14368v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.14368
arXiv-issued DOI via DataCite (pending registration)
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