This academic paper proposes a method to improve the reasoning abilities of Large Reasoning Models (LRMs) by moving beyond inconsistent emergent behaviors. The authors introduce a system to explicitly train models in three key meta-abilities: deduction, induction, and abduction, using automatically generated, verifiable tasks. Their three-stage pipeline involves individual alignment of these abilities, merging them into a single model, and then applying domain-specific reinforcement learning. The results show that this structured approach not only leads to a significant performance boost on diverse benchmarks compared to instruction-tuned models but also establishes a more scalable and dependable foundation for further downstream learning in areas like math, coding, and science.