Design axis
Training signal
What besides the base objective shapes what the model learns — data, auxiliary losses, reward models?
Online RL / policy gradient
Fine-tune a generator against a reward model with online policy-gradient methods such as GRPO, rather than only maximizing likelihood on a fixed dataset.
Representation alignment (REPA-style)
An auxiliary loss during generator training that pulls its intermediate hidden states toward a frozen vision foundation encoder's features, without changing the generator's own latent space.