When Do We Need On-Policy Distillation? Distilling on Offline Student Rollouts Is Often Better
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Computer Science > Computation and Language
arXiv:2610.11291 (cs)
[Submitted on 8 Oct 2026]
Title:When Do We Need On-Policy Distillation? Distilling on Offline Student Rollouts Is Often Better
Authors:Siyan Zhao, Yonggan Fu, Jindong Jiang, Shih-Yang Liu, Song Bian, Byung-Kwan Lee, Sharath Turuvekere Sreenivas, Wenliang Dai, Hanrong Ye, Aditya Grover, Pavlo Molchanov
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Abstract:On-policy distillation (OPD) has become increasingly popular for transferring teacher capabilities to student models. In this work, we ask a critical research question: Is on-policy sampling always beneficial for distilling arbitrary teacher-student pairs? We show that a simple alternative, Semi-OPD, which distills from offline rollouts generated by the initial student, can often outperform OPD in both accuracy and training efficiency. Across 17 teacher-student pairs ranging from 1.5B to 235B parameters, Semi-OPD outperforms OPD in 14 cases, with up to +13.6% accuracy and 11.4x training speedup. We further find that the choice between OPD and Semi-OPD depends on the alignment between the initial teacher and student, quantified by an output-token overlap ratio: OPD is beneficial only when the two are highly aligned with high overlap ratios. Our deeper investigation suggests that effective distillation requires on-policyness w.r.t. both the student and the teacher. For misaligned pairs, student rollouts can become increasingly off-policy w.r.t. the teacher as context length grows, weakening the distillation signal. In contrast, Semi-OPD is often more stable, as it distills on shorter contexts while covering full trajectories and exposing the student to more teacher-preferred tokens. Beyond proposing Semi-OPD as an efficient alternative, our work motivates the community to rethink when to use OPD and to study stronger OPD variants with meaningful teacher-student pairs.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.11291 [cs.CL] |
| (or arXiv:2610.11291v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11291
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled When Do We Need On-Policy Distillation? Distilling on Offline Student Rollouts Is Often Better, by Siyan Zhao and 10 other authors
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