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

==== Delta Activations give, for each trained LoRA/model: ====
* A single vector δ∈Rd\delta \in \mathbb{R}^dδ∈Rd (e.g., 4k dim)
* Computed as average difference in hidden states on a small probe set between base and finetuned.
* That vector summarizes “how this LoRA changes the model’s behavior on generic probes.”

So for each training example (a task / dataset) you have:
* Condition input: prompts / dataset features / descriptions
* Target 1: LoRA weights ΔW\Delta WΔW
* Target 2: Delta activation δ\deltaδ

You can absolutely make “predict δ\deltaδ” an auxiliary task:
* Because δ\deltaδ is low-dimensional and behavior-based,
* It can regularize the representation / conditioning side of the generator in a much nicer way than raw weight MSE.

Key idea:

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