title: Beyond 'Aha!': Toward Systematic Meta-Abilities Alignment in Large
    Reasoning Models
summary: 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
paper_authors._id: 6826a569ea77771e3880f793
paper_authors.name: Zhiyuan Hu
paper_authors.status: admin_assigned
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paper_authors._id: 64351475901c5734bcb64248
paper_authors.avatarUrl: /avatars/12346d4301c1bfb00ce0ea128a93cc15.svg
paper_authors.fullname: Zhiyuan Hu
paper_authors.type: user
paper_authors.user: zhiyuanhucs
paper_authors._id: 6826a569ea77771e3880f794
paper_authors.name: Yibo Wang
paper_authors._id: 6826a569ea77771e3880f795
paper_authors.name: Hanze Dong
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/69227c4bce01d33747c1377b6f9672db.svg
paper_authors.fullname: Hanze Dong
paper_authors.type: user
paper_authors.user: hendrydong
paper_authors._id: 6826a569ea77771e3880f796
paper_authors.name: Yuhui Xu
paper_authors.status: admin_assigned
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paper_authors._id: 6602869253a0518b2a98cafd
paper_authors.avatarUrl: /avatars/c14b5953a716f42c83ad28147f8308ae.svg
paper_authors.fullname: Yuhui Xu
paper_authors.type: user
paper_authors.user: yuhuixu
paper_authors._id: 6826a569ea77771e3880f797
paper_authors.name: Amrita Saha
paper_authors.status: admin_assigned
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paper_authors._id: 6461c2905dba83471db3be53
paper_authors.avatarUrl: /avatars/6e36cf86201d590ac729a75d4a439cde.svg
paper_authors.fullname: Amrita Saha
paper_authors.type: user
paper_authors.user: amritasaha87
paper_authors._id: 6826a569ea77771e3880f798
paper_authors.name: Caiming Xiong
paper_authors.status: admin_assigned
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paper_authors._id: 649dbcc4e0fff1ed099dc80a
paper_authors.avatarUrl: /avatars/c87c273ca628dbcddccbf1ee19b2ce33.svg
paper_authors.fullname: Caiming Xiong
paper_authors.type: user
paper_authors.user: cxiong
paper_authors._id: 6826a569ea77771e3880f799
paper_authors.name: Bryan Hooi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-16T08:09:35.640Z
paper_authors._id: 651d8032c50012d33e914f2f
paper_authors.avatarUrl: /avatars/0a44c9f51fc50ce86582e328c361ea00.svg
paper_authors.fullname: Bryan Hooi
paper_authors.type: user
paper_authors.user: bhooi
paper_authors._id: 6826a569ea77771e3880f79a
paper_authors.name: Junnan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-16T08:09:57.841Z
paper_authors._id: 61f9d3b54ac99e8a1bae85f4
paper_authors.avatarUrl: /avatars/ac47d13204dd22452e4bc46e280842d5.svg
paper_authors.fullname: JunnanLi
paper_authors.type: user
paper_authors.user: JunnanLi
paper_title: Beyond 'Aha!': Toward Systematic Meta-Abilities Alignment in Large
    Reasoning Models
paper_summary: 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
paper_ai_summary: Explicit alignment of large reasoning models with deduction, induction, and abduction through a three-stage pipeline improves scalability and reliability in reasoning tasks.
paper_ai_keywords: large reasoning models long chain-of-thought reasoning outcome-based reinforcement learning self-correction backtracking verification meta-abilities automatic task generation parameter-space merging domain-specific reinforcement learning