Robot Training Breakthrough: 16K Parallel Envs per GPU, 1-Day Training
ChongZzZhang · x · 2026-08-17
Developer ChongZzZhang shared robot training results: using 4x GH200 GPUs, 16,000 parallel environments per GPU with 24 steps per env, the trained policy is deployable. Additionally, via neural mapping and teacher-student distillation, training completed in 1 day (including 1.5h for neural mapping), though the model remains underconfident.
Related event: Robot Policies Trained in a Day via Massive Parallel Sims and Lidar Mapping(2 posts)→
More from Embodied
- AinaInterface team moves to factory to build next-gen HCI — pritisinghhhh · 2026-08-17
- AI Vision: Robots to handle wet lab experiments — dr_alphalyrae · 2026-08-17
- Tesla FSD v14.3.6 Revealed: 10B MoE Model and RL Enhancements — qinzytech · 2026-08-17
- HopTo's Hop-1 Robot Uses Hopping to Cut Energy Use by 75% — MarwaEldiwiny · 2026-08-17
- New robot training method: taga-style attention enables massive parallelization — ChongZzZhang · 2026-08-17
- FCC adds foreign ground robots to Covered List; clarifies it's not a ban and targets production location — the-uncanny-squad · 2026-08-17