SPIN Unprocessed October 26, 2026 ai_technology research
Beyond On-Policy vs. Off-Policy: At What Level Should LLM Agent Distillation Follow the Student?
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arXiv:2610.31918v1 Announce Type: new Abstract: Distilling interactive LLM agents requires choosing between teacher-generated demonstrations and student-generated experience, corresponding broadly to off-policy and on-policy distillation. While both have proven effective, we argue that this distinction is inherently hierarchical: the policy that induces an interaction history need not be the same as the policy that generates the token prefix of the current action. We introduce a hierarchical age
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