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Evidence record 13616 · automatically gathered

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization

Open-weight language models from different families exhibit complementary capabilities, motivating their consolidation into a compact student through on-policy distillation (OPD). However, full-vocabulary OPD typically assumes a shared tokenizer, while existing cross-tokenizer methods may discard teacher probability mass or assign it to student tokens with unrelated content. We introduce Byte-Prefix Marginalization (BPM), which re-expresses the teacher's next-token distribution over the student

Record details

Published: 24 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Children & education
Retrieved: 27 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Regulation · Shared topic: Children & education

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How to cite this record

ethics.ai (24 July 2026), “Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization,” evidence record 13616, https://ethics.ai/record/13616 (originally published by arXiv).

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