#data-curation

3 articles

01 ·Large Language Models·★ MEMBER·PAPER·7 min read Paper Explained: OmniEdu — Building an Open Education LLM Around Four Skills: Solve, Place, Diagnose, Guide A from-scratch walkthrough of OmniEdu, an open K-12 LLM family: how ~1.34M candidate examples were curated down to 60,951, how per-example teaching instructions let one model either answer or hold back, and what the results and limitations actually show. 02 ·Distillation & Compression·FREE·PAPER·8 min read Designing Distillation Data — Deciding What to Ask the Teacher What decides a distilled student's quality is less how smart the teacher is than what you asked the teacher to answer. Synthetic data generation, coverage design, why you should skew toward hard problems, correctness filtering, and why DeepSeek-R1's distillation worked — from first principles. 03 ·Training & Alignment·★ MEMBER·PAPER·11 min read Building a Dataset in Practice — Collect, Clean, Blend Most of the work in building a model is really the work of building its dataset. From designing the population you sample from, to calibrating filter thresholds, measuring leakage into your test set, converting mixture ratios into epochs, using synthetic data where it belongs, and writing annotation guidelines that actually hold — explained from zero, with the formulas and the procedures.