Describes end-to-end learning of a foundation model for adaptive whole-body humanoid control via massive simulation variation. Combines proprioceptive perception and policy adaptation across embodiments.

Releases an open-source MuJoCo-based benchmark with 11 dexterous tasks, low-cost teleoperation hardware, and 1.1K human demonstrations. Designed to evaluate and train modern VLA/robotic policies.

Skill library, demonstrations, and dataset for multi-modal robotic skill learning and manipulation tasks.

Introduces dual-brain fusion Vision-Language-Action model with LangForce physics-grounded training methodology.

Vision-Language-Action (VLA) model fine-tuned on the merged LIBERO robotics dataset (1,693 episodes, 273k+ frames) achieving 98.25% success rate on manipulation tasks. Released with both checkpoint and dataset for VLA finetuning.

A 2.6B-parameter open-source world model that generates coherent 720p, minute-long videos with precise 6-DoF camera control on a single GPU using a Hybrid Linear Diffusion Transformer + Gated DeltaNet for long-context efficiency. Targets controllable physics simulation.

New benchmark and dataset for robotic manipulation in unconstrained 'wild' environments. Includes standardized containers, leaderboards, and evaluation protocols for cross-embodiment policies.

Introduces a latent-space world model framework that lets agents simulate state transitions and iteratively refine plans before real-world execution. Evaluated on 20+ MCP-Bench tasks with measurable gains in tool-use success.

Details OpenAI's reasoning-focused o1 model and its 'long thought' approach using test-time compute scaling. Explores how extended reasoning during inference can improve model performance on complex tasks.

Comprehensive technical report on the Llama 3 family, covering architecture, training at scale, multimodal extensions, and real-world impact. Details the development of Meta's flagship open-source language model series.