Beyond 'Aha!': Toward Systematic Meta-Abilities Alignment in Large
  Reasoning Models
Authors: name: Zhiyuan Hu, name: Yibo Wang, name: Hanze Dong, name: Yuhui Xu, name: Amrita Saha, name: Caiming Xiong, name: Bryan Hooi, name: Junnan Li

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 coincidental "aha moments". Instead, we explicitly align models with three
meta-abilities: deduction, induction, and abduction, using automatically
generated, self-verifiable tasks. Our three stage-pipeline individual
alignment, parameter-space merging, and domain-specific reinforcement learning,
boosting performance by over 10\% relative to instruction-tuned baselines.
Furthermore, domain-specific RL from the aligned checkpoint yields an
additional 2\% average gain in the performance ceiling across math, coding, and
science benchmarks, demonstrating that explicit meta-ability alignment offers a
scalable and dependable foundation for reasoning. Code is available at:
https://github.com/zhiyuanhubj/Meta-Ability-Alignment