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

==== This is where it gets interesting for research ideas: ====
# Compositional hyper-networks You could try to design a structurally constrained hyper-network class over LoRA space that plays the same role as FintF_{\text{int}}Fint: - Decompose LoRA updates into a sum of slot-specific contributions: ΔW=∑khk(sk)\Delta W = \sum_k h_k(s_k)ΔW=∑khk(sk), where sks_ksk are “task factors” extracted from prompts. - Constrain interactions between these factors in a polynomial / low-order fashion (like the image generator). - Aim for formal results like: if the hyper-network fits many tasks, it must be close to the “true” factorized mapping.
# Two-level generative story Combine them: - Bottom level: a compositional generator over data (like the first paper), giving strong OOD reasoning inside a task. - Top level: a parameter generator (DnD-style) that quickly moves that generator into a task-specific region of parameter space given prompts from a new domain. Then you’re using generative structure both in input space and in weight space.
# Replay in ''weight'' space vs replay in ''data'' space The compositional paper uses generative replay of images to train a better encoder. DnD can be read as a form of “replay in parameter space”: you train on many past LoRAs and learn a distribution over them. There might be a neat meta-learning angle where you use a structured data generator to create synthetic tasks, fine-tune them into synthetic LoRAs, and then train a DnD-style hypernetwork on that synthetic parameter corpus.