@inproceedings{hu-etal-2026-beyond,
    title = "Beyond `Aha!': Toward Systematic Meta-Abilities Alignment in Large Reasoning Models",
    author = "Hu, Zhiyuan  and
      Wang, Yibo  and
      Dong, Hanze  and
      Xu, Yuhui  and
      Saha, Amrita  and
      Xiong, Caiming  and
      Hooi, Bryan  and
      Li, Junnan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1981/",
    pages = "39747--39764",
    ISBN = "979-8-89176-395-1",
    abstract = "Large reasoning models (LRMs) already possess a latent capacity for long chain-of-thought reasoning. Prior work has shown that outcome-based reinforcement learning (RL) can incidentally elicit advanced reasoning behaviors such as self-correction, backtracking, and verification{--}phenomena often referred to as the model{'}s ``aha moment''. However, the timing and consistency of these emergent behaviors remain unpredictable and uncontrollable, limiting the scalability and reliability of LRMs' reasoning capabilities. To address these limitations, we move beyond reliance on prompts and unpredictable ``aha moments''. Instead, we explicitly align models with three meta-abilities: \textbf{deduction, induction, and abduction}, using automatically generated, self-verifiable tasks. Our three-stage pipeline (individual alignment, parameter-space merging, domain-specific reinforcement learning) boosts performance by over 10{\%} relative to instruction-tuned baselines. Furthermore, domain-specific RL from the aligned checkpoint yields an additional gain in performance ceiling for both 7B and 32B models across math, coding, and science benchmarks, showing that explicit meta-ability alignment offers a scalable and dependable foundation for reasoning. Code and data can be found in Software and Data part in submission page."
}