Fundamental Reasoning Paradigms Induce Out-of-Domain Generalization in Language Models
Deduction, induction, and abduction are fundamental reasoning paradigms, core for human logical thinking. Although improving Large Language Model (LLM) reasoning has attracted significant research efforts, the extent to which the fundamental paradigms induce generalization has yet to be systematically explored. In this study, we shed light on how the interplay between these core paradigms influences LLMs' reasoning behavior. To this end, we first collect a new dataset of reasoning trajectories from symbolic tasks, each targeting one of the three fundamental paradigms, to abstract from concrete world knowledge. Then, we investigate effective ways for inducing these skills into LLMs. We experiment with a battery of methods including simple fine-tuning, and more complex approaches to increase model depth, or transform a dense model to a mixture-of-experts. We comprehensively evaluate induced models on realistic out-of-domain tasks, that are entirely formulated in natural language and contain real-world knowledge. Our results reveal that our approach yields strong generalizability with substantial performance gains (up to 14.60) across realistic tasks.
698b0ddf1b2dc6b37d61b3bd
Mingzi Cao
698b0ddf1b2dc6b37d61b3be
Xingwei Tan
claimed_verified
2026-02-11T11:17:33.490Z
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/avatars/9534aaf81cbf12f015c6826b682fdb84.svg
Xingwei Tan
XingweiT
user
XingweiT
698b0ddf1b2dc6b37d61b3bf
Mahmud Akhter
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Marco Valentino
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Maria Liakata
698b0ddf1b2dc6b37d61b3c2
Xi Wang
698b0ddf1b2dc6b37d61b3c3
Nikolaos Aletras
Fundamental Reasoning Paradigms Induce Out-of-Domain Generalization in Language Models
Deduction, induction, and abduction are fundamental reasoning paradigms, core for human logical thinking. Although improving Large Language Model (LLM) reasoning has attracted significant research efforts, the extent to which the fundamental paradigms induce generalization has yet to be systematically explored. In this study, we shed light on how the interplay between these core paradigms influences LLMs' reasoning behavior. To this end, we first collect a new dataset of reasoning trajectories from symbolic tasks, each targeting one of the three fundamental paradigms, to abstract from concrete world knowledge. Then, we investigate effective ways for inducing these skills into LLMs. We experiment with a battery of methods including simple fine-tuning, and more complex approaches to increase model depth, or transform a dense model to a mixture-of-experts. We comprehensively evaluate induced models on realistic out-of-domain tasks, that are entirely formulated in natural language and contain real-world knowledge. Our results reveal that our approach yields strong generalizability with substantial performance gains (up to 14.60) across realistic tasks.
Research investigates how fundamental reasoning paradigms influence large language model generalization through targeted training approaches and evaluation on real-world tasks.
Large Language Model reasoning paradigms fine-tuning mixture-of-experts out-of-domain tasks generalizability