The Role of Feedback Alignment in Self-Distillation
Conditioning a language model on additional context, such as feedback on a previous attempt, typically improves its response. Self-distillation trains the model to retain this improvement when the context is not present. The method works by matching the model's output distribution under two settings: a student that sees only the question, and a self-teacher that also sees the context. What the model learns therefore depends on what context the self-teacher receives, yet the design of this context remains largely unexplored. We study context design for self-distillation by training a solver on feedback from a frozen critic. We compare three conditions: (i) a binary reward (GRPO), (ii) the ...
| Authors | Semih Kara, Oğuzhan Ersoy |
|---|---|
| Primary category | cs.AI |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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