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Introduction to OpenTau

At the CES 2026 in Las Vegas, Tensor unveiled OpenTau (τ), an innovative open-source training platform aimed at revolutionizing the development of Physical AI systems. This new toolchain promises to significantly speed up the creation of Vision-Language-Action (VLA) models, which are essential for various applications including autonomous driving, robotics, and human-like interaction with AI.

Vision-Language-Action Models

The essence of the VLA paradigm lies in its integration of vision, language, and action within a single multimodal foundation model. This transformative approach enables smart systems to perceive their surroundings, reason effectively, and respond intelligently. Tensor's commitment to advancing Physical AI technologies positions it as a thought leader in the industry, pushing boundaries and paving the way for more capable AI.

Unveiling OpenTau

OpenTau is designed to make VLA model training not just accessible, but also scalable across different research and development contexts. By opting for an open-source release, Tensor is breaking down barriers, allowing a wider community of researchers and developers to engage with its cutting-edge technology. Jay Xiao, Tensor's Founder and CEO, emphasizes the importance of transparency in AI development, with OpenTau serving as a tool for collaborative innovation.

Key Features of OpenTau

OpenTau encompasses a suite of advanced training capabilities specifically crafted for VLA models, such as:

• - Co-training across diverse datasets: This feature allows the model to learn from a varied array of data, thus enhancing its adaptability and performance.
• - Fast convergence through discrete action modeling: This optimizes the learning process, enabling quicker advancements in training Vision-Language Models (VLMs).
• - Isolation of knowledge between components: This promotes a more effective training environment by preventing interdependency issues and ensuring each part of the model can be optimized independently.
• - VLM dropout methods: These techniques help mitigate overfitting, promoting a generalized performance across unseen data.
• - A dedicated reinforcement learning pipeline: Tailored to support VLA models, it ensures that the intricate dynamics of actions and decision-making are effectively learned.

These features collectively make OpenTau a vital resource for those looking to innovate within the domain of AI, supporting research efforts that were once limited to the confines of large corporations due to resource constraints.

Join the OpenTau Community

Tensor is actively inviting contributions from researchers, developers, and enthusiasts to participate in the evolution of OpenTau. Users can explore its extensive functionalities by visiting its GitHub repository. By forking the codebase, experimenting with the innovations, and providing feedback, the community can help shape the future of Physical AI technologies.

Conclusion

The announcement of OpenTau at CES 2026 marks a pivotal moment in the journey of Physical AI. It represents not just a tool, but a commitment to open collaboration and shared growth in technology. As Tensor continues to pave the way with its groundbreaking advancements, the industry stands on the brink of remarkable change.

Attendees of CES 2026 are encouraged to learn more and experience OpenTau firsthand at Tensor's booth (#5701) in the LVCC West Hall or see the Tensor Robocar on display at Fontainebleau Las Vegas, 4th Floor, The Foundry.