Gio Huh @giohuh_ · 13h
What can Astra do when given a humanoid embodiment?
We built HomeBody to find out. Controlled by GPT Astra, it carries out long-horizon tasks in a previously unseen kitchen—from tidying up across the room to retrieving remembered objects from ambiguous requests—without environment-specific training data or additional policy learning.
Here's how we did it [eyes emoji]: tml.stanford.edu/homebody/
[video frame, 0:16: a humanoid robot in a kitchen; overlay labels "STAGE 3 OF 3 / Deploy it" and "Astra 24x"]
Note from Claude Sonnet 5
Tweet by Gio Huh announcing HomeBody, a humanoid robot controlled by GPT Astra performing long-horizon kitchen tasks in an unseen environment without environment-specific training. Embedded video frame shows a humanoid robot in a kitchen with a 'Stage 3 of 3: Deploy it' label.
gpt astrahumanoid robotsembodimenthomebodyroboticsstanford
Percy Liang @percyliang
In our last episode, careful tuning, scaling, and ensembles led to a 5x gain in data efficiency (requires 5x less data to get the same loss). Now, with a rephraser model, we can get an additional 1.8x gain in data efficiency. I know, everyone's compute constrained, but we're preparing for a data-constrained future.
> QUOTED: Konwoo Kim @konwookim · 7h
> for data-constrained pre-training, synth data isn't just benchmaxxing, it lowers loss on the real data distribution as we generate more tokens
> for even better scaling, treat synth gens as forming on...
> [Embedded paper image: "Data-efficient pre-training by scaling synthetic megadocs" — Konwoo Kim, Suhas Kotha, Yejin Choi, Tatsunori Hashimoto, Nick Haber, Percy Liang, Stanford University. Diagrams of "Simple Rephrasing," "Stitched Rephrasing," and "Latent Thoughts" methods for generating synthetic training data from real docs; a scaling chart "Scaling Generations" showing IID Loss decreasing from 3.55 (real data only) down to ~3.34-3.41 as generations per pretraining doc increase from 0 to 32, with Latent Thoughts performing best.]
2:52 PM · Mar 20, 2026 · 5,748 Views
Note from Claude Sonnet 5
A Stanford paper (Percy Liang's group) on data-efficient pretraining via synthetic "megadocs" generated by rephrasing/expanding real documents, addressing anticipated data-constrained scaling. Relevant to AI capabilities/scaling research Nathan tracks, and tangentially to his own training-efficiency work on brain_graph_1.
twittermachine learning researchdata efficiencysynthetic datapretrainingscaling lawsstanfordpercy liang
