Training Dyna-2 at million-hour scale, repeatably — DYNA
dyna.co · 4,152 words · saved by 2 readers
How we rebuilt every step of our robotics data stack for million-hour scale training.
[ Research ] Training Dyna-2 at million-hour scale, repeatably Category: Research Author: Dyna Robotics Date: August 2026 Read: 22 min Robot or vendorcollectionLanding bucketobject storageTraining-ready dataMCAP episodesTraining manifestcolumnar + mmapGPU clustertraininguploadsingestioncurationloading Figure 1: Flowchart of data lifecycle From collection to training on the GPU cluster, robot data moves through four stages: collection into a landing bucket, ingestion into training-ready episodes, curation into the dataset for a given experiment, and loading into training batches.…
saved by
related reading
- Does Scaling Web-Video Pre-training Help Real Robots Do Real Work? | Rhoda AIrhoda.ai
- How to Rack 30 Petabytes of Storage | blogsi.inc
- Causal Video Models Are Data-Efficient Robot Policy Learners | Rhoda AIrhoda.ai
- Multi-Datacenter Training: OpenAI's Ambitious Plan To Beat Google's Infrastructuresemianalysis.com
- Keep the Tokens Flowing: Lessons from 16 Open-Source RL Librarieshuggingface.co
- Training great LLMs entirely from ground up in the wilderness as a startup - Yi Tayyitay.net
- How to Rack 30 Petabytes of Storage | blogsi.inc
- Nikolaus West (@NikolausWest) on Xx.com
- Network and Storage Benchmarks for LLM Training on the Cloud | Henry Zhumaknee.github.io
- how we accidentally solved robotics by watching 1 million hours of YouTube – atharva's blogksagar.bearblog.dev
- From bare metal to a 70B model: infrastructure set-up and scripts - Imbueimbue.com
- IsoCompute Playbook: Optimally Scaling Sampling Compute for RL Training of LLMscompute-optimal-rl-llm-scaling.github.io