> In the extreme case, a language model answering reasoning questions may rely heavily on retrieval from parametric knowledge influenced by a limited set of documents within its pretraining data. In this scenario, specific documents containing the information to be retrieved (i.e. the reasoning traces) contribute significantly to the model’s output, while many other documents play a minimal role.

> Conversely, at the other end of the spectrum, the model may draw from a broad range of documents that are more abstractly related to the question, with each document influencing many different questions similarly, but contributing a relatively small amount to the final output. We propose generalisable reasoning should look like the latter strategy.

Isn't it much more impressive if a model can generalize from a single example?