ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning in LLMs

Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought (Long
CoT) reasoning have demonstrated remarkable cross-domain generalization capabilities. However,
the underlying mechanisms supporting such transfer remain poorly understood. We hypothesize
that cross-domain generalization arises from shared abstract reasoning prototypes — fundamental
reasoning patterns that capture the essence of problems across domains. These prototypes
minimize the nuances of the representation, revealing that seemingly diverse tasks are grounded in
shared reasoning structures. Based on this hypothesis, we propose ProtoReasoning, a framework
that enhances the reasoning ability of LLMs by leveraging scalable and verifiable prototypical
representations (Prolog for logical reasoning, PDDL for planning). ProtoReasoning features:
(1) an automated prototype construction pipeline that transforms problems into corresponding
prototype representations; (2) a comprehensive verification system providing reliable feedback
through Prolog/PDDL interpreters; (3) the scalability to synthesize problems arbitrarily within
prototype space while ensuring correctness. Extensive experiments show that ProtoReasoning
achieves 4.7% improvement over baseline models on logical reasoning (Enigmata-Eval), 6.3%
improvement on planning tasks, 4.0% improvement on general reasoning (MMLU) and 1.0% on
mathematics (AIME24). Significantly, our ablation studies confirm that learning in prototype space
also demonstrates enhanced generalization to structurally similar problems compared to training
solely on natural language representations, validating our hypothesis that reasoning prototypes
serve as the foundation for generalizable reasoning in large language models.