Distillation is Fundamentally a Data Problem
willdepue · x · 2026-07-19
The author corrects their phrasing on distillation, arguing that distillation is primarily a data problem, not a logits problem. They give an example: training on content like Shakespeare or fable is essentially "distilling from that data," where the key lies in the quality and distribution of the data itself.
Their core judgment is that many people lack an understanding of the data ↔ performance relationship; even with infinite html website data or even some logits clues, it may not bring the imagined improvements in low-entropy scenarios.
Related event: Distillation is Fundamentally a Data Problem, Not Logits(2 posts)→
More from Research
- Style-similarity analysis puts Kimi K3 closer to Claude Fable 5 than to K2.6 — soumitrashukla9 · 2026-07-21
- A GLP1R variant may explain stronger Ozempic weight loss, and the team built an agent workflow — julia_kiseleva · 2026-07-21
- Proceedings for the second geometry-grounded representation learning workshop are now online — erikjbekkers · 2026-07-21
- New survey maps how agentic systems are learning to improve themselves — SchmidhuberAI · 2026-07-21
- A curated TTS list for voice agents tracks latency, cancellation, and evals — mahimairaja · 2026-07-21
- Jacob Tsimerman interview frames LLMs as a turning point for mathematical discovery — stevenstrogatz · 2026-07-21