Learning a Fact Is Not Learning How to Retrieve It
Quick summary
arXiv:2610.03251v1 Announce Type: new Abstract: A model trained on "The capital of X is Y" may produce "Y" after "The capital of X is" but fail after "The capital of X:". We call these different ways of eliciting the same fact request forms. To separate learning a fact from retrieving it, we train two models in two stages. In the first stage (request-form training), one model sees each fact in five forms and the other sees the same facts only as statements. In the second stage (target-fact training), both receive identical training on new facts, all as statements. Both then retrieve the new fa
Key takeaways
- arXiv:2610.03251v1 Announce Type: new Abstract: A model trained on "The capital of X is Y" may produce "Y" after "The capital of X is" but fail after "The capital of X:".
- We call these different ways of eliciting the same fact request forms.
- To separate learning a fact from retrieving it, we train two models in two stages.
Why it matters
“Learning a Fact Is Not Learning How to Retrieve It” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

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