Beam Search as Test-Time Self-Distillation via Counterfactual Contexts
Quick summary
arXiv:2609.37041v1 Announce Type: cross Abstract: Self-Distillation Fine-Tuning (SDFT) enables a language model to act as its own teacher: by conditioning on a demonstration, the model produces an implicit reward via pointwise mutual information, which guides on-policy learning without external supervision. However, SDFT operates at training time: it requires gradient updates and access to expert demonstrations, making it inapplicable at inference. We propose test-time self-distillation, a decoding-time method that extracts a steering signal from the self-distillation framework without any par
Key takeaways
- arXiv:2609.37041v1 Announce Type: cross Abstract: Self-Distillation Fine-Tuning (SDFT) enables a language model to act as its own teacher: by conditioning on a demonstration, the model produces an implicit reward via pointwise mutual information, which guides on-policy learning without external supervision.
- However, SDFT operates at training time: it requires gradient updates and access to expert demonstrations, making it inapplicable at inference.
- We propose test-time self-distillation, a decoding-time method that extracts a steering signal from the self-distillation framework without any par
Why it matters
“Beam Search as Test-Time Self-Distillation via Counterfactual Contexts” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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