World Wire

Dyna Robotics has released Dyna-2 , a world-action model for robot manipulation. It was pre-trained on more than one million hours of egocentric human video. That is roughly 170 years of continuous waking experience. Robot learning has been bottlenecked by action-labelled data, which teleoperation must deliberately produce. Dyna-2 tests whether ordinary human video can substitute. The research team trained a data ladder from 1,000 to 1,000,000 hours and measured what scales. Three results follow: a scaling law on human data, the first transfer of that law to unseen robot data, and evidence that video prediction drives the transfer. Is it deployable? Yes, but as a vendor-operated system, not as downloadable weights. Dyna Robotics has announced no public checkpoint, API, or license for Dyna-2. Deployment today means buying a Dyna robot cell, not self-hosting a model. Which companies : Dyna-1 robots already run in production in hotels, restaurants, and laundromats, per the...

SpaceXAI just released Grok 4.6 . The release is a post-training upgrade over Grok 4.5 rather than a larger base model. SpaceXAI held the foundation constant and spent the improvement on a longer supplemental training run, regenerated supervised fine-tuning trajectories, and reinforcement learning in agentic environments. Agents that stay on a task across many steps without drifting. Grok 4.6 scores 61 on the Artificial Analysis Intelligence Index, up five points from Grok 4.5 and tied with GPT-5.6 Sol Max. The model takes 500,000 context tokens, is live today in Cursor and Grok Build , and adds a new xhigh reasoning-effort level above the ladder Grok 4.5 shipped with. Is it deployable? Yes, in production, with a bounded set of workloads. The model is generally available through the xAI API as grok-4.6 , is the default model in Grok Build , ships in Cursor on all plans, and is routable via OpenRouter, Vercel, and Cloudflare. There is no open-weights release and no self-...

In this tutorial, we build an end-to-end post-training pipeline for a compact instruction-tuned language model using AllenAI’s Open Instruct framework. We move through three major training stages: Supervised Fine-Tuning, Direct Preference Optimization, and Reinforcement Learning with Verifiable Rewards using GRPO, while adapting the original multi-GPU Tulu 3 stack to fit within a 16 GB runtime. We clone the Open Instruct repository, selectively load its native loss and utility functions, configure LoRA adapters, prepare GSM8K data for each training stage, and use deterministic verifiers to evaluate generated mathematical answers. Throughout the workflow, we preserve the core optimization logic of Open Instruct while replacing distributed components such as vLLM, Ray actors, DeepSpeed, and asynchronous rollout queues with lightweight Hugging Face and PyTorch implementations suitable for Colab. Copy Code Copied Use a different Browser import os, sys, subprocess, textwrap, json, ma...