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Gains and Collapse in On-Policy Distillation:A Reinforcement Learning Perspective

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 05:18:24 UTC.

Briefings

Research reveals that on-policy distillation can cause language models to generate excessively long and repetitive text due to an implicit reward model that amplifies overlong rollouts. This occurs when the teacher's preference misaligns with quality, causing the student to prioritize overgeneration over accuracy. Mitigation strategies include masking unhealthy responses during training and using SFT initialization to prevent collapse.

Key points

• On-policy distillation improves performance without expanding student capabilities by leveraging the teacher's implicit feedback.
• Reward hacking causes overlong, repetitive generation when the teacher's preference misaligns with quality, amplifying overlong rollouts.

Context

On-policy distillation is a post-training approach for language models that improves performance but risks generating excessively long and repetitive text. The underlying mechanism remains poorly understood, leading to divergent outcomes between performance gains and quality degradation.

Limitations

Findings rely on reinforcement learning perspective experiments; specific failure modes depend on teacher-student alignment. Code availability does not guarantee reproducibility or full validation of mitigation claims.

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