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Research questionHow can one deployable LLM learn from multiple specialized teachers when the most reliable teacher varies by sample?A teacher that is strongest in a domain can still answer individual examples incorrectly, while another domain teacher may be right. Consolidating their capabilities therefore requires identifying trustworthy supervision for each sample without serving separate teachers at deployment.
AI
LLM Pretraining & Post-training
Machine Learning
Natural Language Processing
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.Learn from Whoever Is Right: Answer-Verified Multi-Teacher Distillation for Multi-Domain LLMsThe source studies on-policy distillation from several frozen LLM teachers into one student, using answer verification and privileged feedback. It reports results across five students from three model families, including Qwen3-8B.research paper · Sep 2, 2026
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