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#representation-learning

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01 ·★ MEMBER·PAPER·10 min read Paper Explained: Beyond Data Scaling — Why the Backbone, Not the Trajectory Count, Decides Your VLA (VLAct) Robot trajectories can't be scraped off the web, so the paper argues you should squeeze reusable representations out of the trajectories you do have. Three modest interventions — preserving the VLM prior, co-supervising with multiple action heads, and partially unifying the action space across embodiments — beat industrial VLA systems on fully open data and 16 GPUs. 02 ·Machine Learning Basics·★ MEMBER·PAPER·11 min read Self-Supervised Learning — The Day Unlabeled Data Became an Asset Nobody has to label the data — the data can write its own exam. A ground-up tour of the two families (masked prediction and contrastive learning), from the intuition through the InfoNCE loss, an interactive figure, PyTorch code, and finally why LLM pretraining is the largest self-supervised system ever run.