Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe

TL;DR

This paper systematically investigates the dynamics of on-policy distillation in large language models, revealing key conditions for success and proposing strategies to improve it, while questioning its scalability for long-horizon tasks.

Contribution

It provides a detailed analysis of OPD mechanisms, identifies critical factors for success, and introduces practical strategies to enhance distillation effectiveness.

Findings

Successful OPD requires compatible thinking patterns between teacher and student.

High-probability token alignment is crucial for effective distillation.

Off-policy cold start and teacher-aligned prompts can recover failing distillation.

Abstract

On-policy distillation (OPD) has become a core technique in the post-training of large language models, yet its training dynamics remain poorly understood. This paper provides a systematic investigation of OPD dynamics and mechanisms. We first identify that two conditions govern whether OPD succeeds or fails: (i) the student and teacher should share compatible thinking patterns; and (ii) even with consistent thinking patterns and higher scores, the teacher must offer genuinely new capabilities beyond what the student has seen during training. We validate these findings through weak-to-strong reverse distillation, showing that same-family 1.5B and 7B teachers are distributionally indistinguishable from the student's perspective. Probing into the token-level mechanism, we show that successful OPD is characterized by progressive alignment on high-probability tokens at student-visited states, a small shared token set that concentrates most of the probability mass (97%-99%). We further propose two practical strategies to recover failing OPD: off-policy cold start and teacher-aligned prompt selection. Finally, we show that OPD's apparent free lunch of dense token-level reward comes at a cost, raising the question of whether OPD can scale to long-horizon distillation.