OPSRD: On-Policy Self-Role Distillation
Role prompting elicits specialized behavior from large language models through an expert identity, offering a lightweight way to guide reasoning on demanding tasks. However, evaluating or distilling complete role-prompted answers can miss useful next-token preferences when the sampled solution remains incorrect. Transferring these preferences also requires an objective that reaches alternatives the student rarely predicts. We introduce OPSRD, which uses a fixed expert role as privileged teaching context for on-policy self-distillation without reference solutions. A role-free student generates a trajectory, and a frozen instance of the same base model supplies role-conditioned distribution...
| Authors | Weijie Ren, Yanwen Zhang, Hao Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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