S1's ICL advantage grows exponentially in long-horizon, out-of-distribution tasks
ZeYanjie · x · 2026-08-31
Deepak Pathak of SkildAI highlights that S1's in-context learning (ICL) excels most in super long-horizon tasks (10+ mins) and out-of-distribution scenarios. While simple tasks are solved by zero-shot prompts, ICL becomes critical for unseen scenarios, with S1's advantage over regular VLAs growing exponentially as tasks move further from the pretraining distribution.
More from Embodied
- Berkeley researchers sample humanoid robot policies directly from weight space with diffusion — berkeley_ai · 2026-10-16
- OneWM: unified causal training shows world and action modeling improve each other — IAAI233 · 2026-10-16
- Tesla's hover car isn't a flying car — compressed air can't sustain flight — tinyfool · 2026-10-16
- Scobleizer relays claim that Optimus demo will be even more mind-blowing than the road-flipping Roadster — Scobleizer · 2026-10-16
- Indie dev's AI PASSPORT wearable firmware hits 1,200+ downloads in days — ezshine · 2026-10-16
- 1X Opens California Factory for Mass Production of NEO Humanoid Robots — emmanuelvivier · 2026-10-16