Trust-Region Behavior Blending for On-Policy Distillation

Daniil Plyusov; Alexey Gorbatovski; Alexey Malakhov; Nikita Balagansky; Boris Shaposhnikov; Daria Korotyshova; Daniil Gavrilov

arXiv · 2605.31159

Trust-Region Behavior Blending for On-Policy Distillation

Daniil Plyusov·Alexey Gorbatovski·Alexey Malakhov·Nikita Balagansky·Boris Shaposhnikov·Daria Korotyshova·Daniil Gavrilov

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Abstract

On-policy distillation (OPD) trains a student on prefixes sampled from its own policy while matching a stronger teacher. This addresses the prefix mismatch of offline distillation, but early student rollouts can still be poor, placing teacher supervision on weak or low-quality prefixes. We propose Trust-Region behavior Blending (TRB), a warmup method that replaces the early rollout policy with the closest-to-teacher behavior policy inside a student-centered KL trust region, while keeping the per-prefix reverse-KL OPD loss unchanged. The KL budget is annealed to zero, so training returns to pure student rollouts after warmup. Across two math-reasoning distillation settings, TRB attains the strongest average among the compared methods.

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Daniil Plyusov, Alexey Gorbatovski, Alexey Malakhov, Nikita Balagansky, Boris Shaposhnikov, Daria Korotyshova, Daniil Gavrilov. 2026-05-29. Trust-Region Behavior Blending for On-Policy Distillation. https://arxiv.org/abs/2605.31159

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