AGENTIC-SYSTEMSJuly 17, 20261 min read

The Steering Budget: Examples beat Knobs

Generative AI models are traditionally steered using 'knobs' like prompts, but they hit a hard ceiling. A new study reveals that providing concrete examples instead of adjusting prompts unlocks the model's full 'steering budget,' offering far greater reach and expressiveness.

Computer Science > Artificial Intelligence

Title:The Steering Budget: Examples beat Knobs

Generative models are steered with knobs -- prompts, guidance scales, property tags. Turn one as hard as you like and, past a point, it stops moving the property you care about. We find that ceiling is not a shortcoming of the model but a budget, set by the training data before the model is trained: a property's movable range splits in two -- the part a knob can reach, and a second, significant part that only examples -- concrete instances of what you want more of -- can reach. That second part is usually much larger, but not always, and the same budget says so in advance.

Reaching that second part takes a different move: instead of turning a knob, you show the model examples, composed from what it already learned rather than added to its training. A cheap audit of the training data measures the budget; we give a recipe for building the example set that reaches all of it.

This buys two things a knob can't. Reach: it moves a property across the whole budget, not just the part a knob reaches. Expressiveness: it steers toward targets you can only specify by example -- including ones you can't put into words. We turn these into a handful of falsifiable claims and verify them in two unrelated domains, image and crystal-structure generation -- marking where a knob is enough, and where only examples will do.

Source: arXiv cs.AI Recent

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