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Transfer of reasoning behaviors beyond training distributions

Ascertain whether and under what conditions reasoning behaviors learned by large language models transfer beyond their training distributions.

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Background

The authors observe that models often apply narrow strategies that succeed in-distribution but fail on ill-structured or out-of-distribution problems, revealing limited behavioral transfer.

They argue that demonstrating reliable transfer of reasoning behaviors is essential for distinguishing genuine reasoning from brittle, distribution-bound shortcuts.

References

Overall, our analyses expose fundamental gaps: we cannot know which training produces which cognitive capabilities a priori, cannot ensure behaviors transfer beyond training distributions, and cannot validate whether observed patterns reflect genuine cognitive mechanisms or spurious reasoning shortcuts.

— Cognitive Foundations for Reasoning and Their Manifestation in LLMs (2511.16660 - Kargupta et al., 20 Nov 2025) in Section: Opportunities and Challenges (opening paragraph)