Hedra Model Factory puts physical AI models into a continuous improvement loop

What is changing?

Teams can bring their own simulations and data to run supervised fine-tuning, LoRA or reinforcement learning in GPU sandboxes. Hedra researchers define goals and guardrails while autonomous agents run experiments. Vision-language models can support data captioning, scoring and online reinforcement-learning feedback. Existing models can also be served on Hedra’s inference engine.

Deployment can run on premises or in a nearby partner data center, with models optimized for target hardware and latency budgets. Production performance feeds the next training cycle, and Hedra says it releases only versions that pass each customer’s evaluations. This does not mean the process is fully unsupervised: evaluation sets, acceptance thresholds and researcher-defined guardrails remain core parts of the loop.