Fine-Tuning Cosmos Predict 2.5 with LoRA/DoRA for Robot Video Generation

Fine-tuning large world models like NVIDIA Cosmos Predict 2.

5 for domain-specific tasks—such as robot manipulation video generation—is hindered by the cost of full fine-tuning, risk of catastrophic forgetting, and the scarcity of real-robot demonstration data.

The article addresses this tension by presenting a parameter-efficient fine-tuning approach using LoRA and DoRA adapters, which inject small trainable modules into the frozen 2B-parameter model, enabling adaptation on a single GPU while keeping adapter files small and portable for flexible swapping at inference.