arXiv:2606.14368v1 Announce Type: new 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-ba
The paper demonstrates an innovative approach to LLM training at a time when multi-model interaction and efficiency in AI development are paramount.
This co-distillation method offers a pathway to more robust and adaptable LLMs, potentially leading to significant improvements in AI agents and specialized applications.
The paradigm shifts from one-way distillation or single-model tuning to a mutual improvement process, allowing models to learn from each other's strengths across domains.
- · AI developers
- · LLM operators
- · Businesses deploying AI agents
- · Legacy AI training methodologies
- · Developers focused solely on single-model optimization
More capable and generalized large language models emerge from this mutual learning process.
Reduced training costs and accelerated development cycles for specialized AI applications may follow from more efficient model improvement.
The widespread adoption of mutually-trained LLMs could accelerate the deployment and capability of autonomous AI agents across various industries.
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Read at arXiv cs.LG