Recursive Self-Improvement via On-Policy Distillation for Reasoning
On-policy distillation (OPD) trains a student model by having it generate trajectories, then matching its next-token predictions with an external teacher's next-token predictions. This provides dense, token-level supervision to the student. On-policy self-distillation (OPSD) eliminates the need for the external teacher. Specifically, a second frozen copy of the student model, now given the ground truth in its context, serves as the teacher. The student model only receives the problem and learns to mimic the privileged teacher model, while the teacher remains frozen throughout training. Previous work showed that freezing the teacher is useful for training stability, but we argue that this ...
| Authors | Shangjian Yin, Zehao Zhao, Kavosh Asadi et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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