ALRA: Adaptive Local Relational Alignment for Logit-Based Pre-training Distillation of Autoregressive Language Models
Logit-based knowledge distillation for autoregressive language models usually aligns teacher and student next-token distributions over the entire…
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| Factor | Weight | Score | Contribution | Where it came from |
|---|---|---|---|---|
| Corroboration | 0.35 | 0.39 | +0.135 34% | 1 independent org on the story. Tier-3 aggregators never corroborate — they can show something is circulating, never that it is true. |
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| Freshness | 0.20 | 0.25 | +0.050 13% | Halves every 10 hours from the newest item on the story. This is the only factor that rewards a story for nothing more than being recent. |
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ALMA (ESO/NAOJ/NRAO) · CC BY 4.0 · Wikimedia Commons · illustrativeWhat happened
Logit-based knowledge distillation for autoregressive language models usually aligns teacher and student next-token distributions over the entire vocabulary. However, this global objective overlooks relative preferences among likely token alternatives. Existing local approaches often select candidate tokens from either the teacher or the student alone. Teacher-only selection can miss tokens that the student considers likely, while student-only selection can rely on an inaccurate ranking early in training. We propose Adaptive Local Relational Alignment (ALRA), a position-specific framework combining student proposals with teacher guidance.
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- 01 Arxivfirst-party first seen ALRA: Adaptive Local Relational Alignment for Logit-Based Pre-training Distillation of Autor
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