Editing Openai/693c0f4f-255c-8008-92e9-0cd44c6d6226 (section)

===== - In the compositional reasoning paper, generator side = decoder f^(z)\hat f(z)f^(z), encoder side = representation g^(x)\hat g(x)g^(x). =====
* They show that structural assumptions are much easier to impose on the generator side FintF_{\text{int}}Fint than on the inverse side GintG_{\text{int}}Gint, which is why generative modeling wins for OOD compositionality. 2512.08854v1

DnD is philosophically similar:
* Instead of “training the adapter by gradient descent for each task” (analog of repeatedly learning a discriminative model), they build a generator over LoRA spaces: condition (prompts)  →  weights\text{condition (prompts)} \;\rightarrow\; \text{weights}condition (prompts)→weights
* That’s a generator living in weight space rather than input space.

You could say:
* The compositional paper: “be generative about '''images'''”.
* DnD: “be generative about '''adapters'''”.

Both are pushing the work into a single, amortized generative system instead of re-doing task-specific optimization.