When Teachers Mislead: Spurious-Signal-Aware On-Policy Distillation
On-Policy distillation (OPD) transfers teacher capabilities by supervising student-sampled trajectories with dense token-level teacher signals. Recent selective OPD methods improve this process by prioritizing signals that are confident, informative, or learnable. However, the assumptions overlook a fundamental failure mode of language models: their token-level judgments can be driven by input-agnostic language priors, formatting conventions, or stereotyped reasoning templates rather than task-specific evidence. We refer to such optimization-relevant but weakly input-grounded supervision as spurious signals in OPD, which may produce large gradients while contributing little task-improving...
| Authors | Yinuo Jiang, Yongjie Ye, Zhou Tao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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