#dataset

6 articles

01 ·Time Series·★ MEMBER·PAPER·11 min read Paper Explained: Holi-World — Turning First-Person Recordings into a Ledger of the Room, Then Teaching a VLM to Reason over It Holi-World (arXiv:2610.26549) automatically turns first-person recordings into a record of room geometry, furniture, and the trajectories of objects people pick up, then converts that record into about 80K spatial and temporal QA pairs to train a 4B vision-language model. A from-scratch walkthrough of the pipeline, the numbers, and the limits. 02 ·★ MEMBER·PAPER·7 min read Paper Explained: SpatialBlock — Teaching Vision-Language Models Spatial Sense with 15,000 Block-Stacking Puzzles A paper that improves spatial reasoning in vision-language models using nothing but 15,000 synthetic block-stacking questions — no real-scene 3D labels. We walk through how the data is built, the two training recipes, what actually helped, and where it falls short. 03 ·★ MEMBER·PAPER·14 min read SolarWM: Training on 5 Seconds, Walking for an Hour — and Opening the Whole Stack A data engine that folds 10 datasets into 1.43M clips under one contract, plus a three-stage recipe that adapts four different video backbones without erasing them. The causal models roll out for minutes to hours after training on 5-second sequences alone. 04 ·Inference & Serving·★ MEMBER·PAPER·11 min read Paper walkthrough: CyberFactory — turning wild CVEs into runnable training problems An open-source pipeline that converts real CVEs into executable, verifiable tasks and uses a reusable vulnerability-analysis skill to synthesize teacher trajectories the student then internalizes. 58.1% Pass@1 on CyberGym. 05 ·Large Language Models·FREE·PAPER·10 min read Building a Pretraining Corpus — From Web Sludge to Textbook Quality Behind the single line "pretrained on a large corpus of web text" sit four stages: text extraction, quality filtering, deduplication, and mixture weights. This is a from-scratch walkthrough of how a gravel heap called Common Crawl gets sifted into textbook-quality prose — from the MinHash equation to the parameter names you actually touch. 06 ·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.