#training

With SAM 3 you can use text and visual prompts to precisely identify, segment, and follow any object in images or videos—coming soon to Instagram Edits and Vibes on the Meta AI app.

Segment Anything Model 2 (SAM 2) is a foundation model towards solving promptable visual segmentation in images and videos. We extend SAM to video by considering images as a video with a single frame. The model design is a simple transformer architecture with streaming memory for real-time video processing. We build a model-in-the-loop data engine, which improves model and data via user interaction, to collect our SA-V dataset, the largest video segmentation dataset to date. SAM 2 trained on our data provides strong performance across a wide range of tasks and visual domains.

Turn raw visual data into model-ready datasets with CVAT annotation platform. Run it on your own infrastructure, CVAT cloud, or let our labeling team do the work.

Robostral Navigate is an 8B model that enables robots to autonomously navigate complex environments using only a single RGB camera, achieving 76.6% success on unseen R2R-CE benchmarks—outperforming multi-sensor approaches while being more efficient. Built entirely in-house with simulated data and token-efficient techniques, it generalizes across robot types and adapts to real-world obstacles unseen during training. The model combines pointing-based navigation with reinforcement learning for continuous improvement, paving the way for unified embodied AI in robotics.

State-of-the-art performance on R2R-CE

79.4% Success Rate on validation seen

76.6% Success Rate on validation unseen 

Operates from a single RGB camera, with no LiDAR or depth sensors

8B model, built in-house and trained entirely in simulation

Runs on wheeled, legged, and flying robots, and generalizes across robot sizes

Robust to differences in camera intrinsics

Token-efficient training via prefix-caching

A key ingredient of Robostral Navigate is an efficient training algorithm based on prefix-caching. Using a tree-based attention-masking strategy, our method compresses an entire episode into a single sequence, enabling training on all time steps in a single forward pass while preventing information leakage between time steps.

Compared to training with one sample per time step, our approach reduces the number of training tokens by 22× while preserving all of the learning signals. In practice, this method transforms training runs that would take months into runs that complete in days.

We will train new models using data from Free, Pro, and Max accounts when this setting is on (including when you use Claude Code from these accounts).

If you’re a current user, you can select your preference now and your selection will immediately go into effect. This setting will only apply to new or resumed chats and coding sessions on Claude. Previous chats with no additional activity will not be used for model training. You have until October 8, 2025 to make your selection.
If you’re a new user, you can pick your setting for model training during the signup process.
You can change your selection at any time in your Privacy Settings.

From April 24 onward, interaction data—specifically inputs, outputs, code snippets, and associated context—from Copilot Free, Pro, and Pro+ users will be used to train and improve our AI models unless they opt out.

Should you decide to participate in this program, the interaction data we may collect and leverage includes:

Outputs accepted or modified by you
Inputs sent to GitHub Copilot, including code snippets shown to the model
Code context surrounding your cursor position
Comments and documentation you write
File names, repository structure, and navigation patterns
Interactions with Copilot features (chat, inline suggestions, etc.)
Your feedback on suggestions (thumbs up/down ratings)

This program does not use:

Interaction data from Copilot Business, Copilot Enterprise, or enterprise-owned repositories
Interaction data from users who opt out of model training in their Copilot settings
Content from your issues, discussions, or private repositories at rest. We use the phrase “at rest” deliberately because Copilot does process code from private repositories when you are actively using Copilot. This interaction data is required to run the service and could be used for model training unless you opt out.