Compiling Learning Problems into Adaptation Programs for Language Models
Quick summary
arXiv:2609.37371v1 Announce Type: cross Abstract: Model adaptation is typically governed by a fixed recipe, even though different update programs can produce substantially different behavioral outcomes. We introduce adaptation compilation, which reframes where, how, and to what extent a model should adapt as a joint prediction and decision problem. Rather than searching over candidate programs anew for each learning episode, a compiler learns from prior adaptations to predict a vector-valued counterfactual response surface over candidate programs---their expected effects on acquisition, transf
Key takeaways
- arXiv:2609.37371v1 Announce Type: cross Abstract: Model adaptation is typically governed by a fixed recipe, even though different update programs can produce substantially different behavioral outcomes.
- We introduce adaptation compilation, which reframes where, how, and to what extent a model should adapt as a joint prediction and decision problem.
- Rather than searching over candidate programs anew for each learning episode, a compiler learns from prior adaptations to predict a vector-valued counterfactual response surface over candidate programs---their expected effects on acquisition, transf
Why it matters
“Compiling Learning Problems into Adaptation Programs for Language Models” signals where capital and distribution power are moving in the AI market. Product continuity, pricing, workforce skills and the competitive options available to startups may all be affected.
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