Beyond 'Aha!': Toward Systematic Meta-Abilities Alignment in Large
  Reasoning Models
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
6826a569ea77771e3880f793
Zhiyuan Hu
admin_assigned
2025-05-16T08:02:17.850Z
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/avatars/12346d4301c1bfb00ce0ea128a93cc15.svg
Zhiyuan Hu
user
zhiyuanhucs
6826a569ea77771e3880f794
Yibo Wang
6826a569ea77771e3880f795
Hanze Dong
admin_assigned
2025-05-16T08:08:50.665Z
63a3ff69f91ad3ea5703841d
/avatars/69227c4bce01d33747c1377b6f9672db.svg
Hanze Dong
user
hendrydong
6826a569ea77771e3880f796
Yuhui Xu
admin_assigned
2025-05-16T08:09:07.114Z
6602869253a0518b2a98cafd
/avatars/c14b5953a716f42c83ad28147f8308ae.svg
Yuhui Xu
user
yuhuixu
6826a569ea77771e3880f797
Amrita Saha
admin_assigned
2025-05-16T08:09:22.879Z
6461c2905dba83471db3be53
/avatars/6e36cf86201d590ac729a75d4a439cde.svg
Amrita Saha
user
amritasaha87
6826a569ea77771e3880f798
Caiming Xiong
admin_assigned
2025-05-16T08:09:29.160Z
649dbcc4e0fff1ed099dc80a
/avatars/c87c273ca628dbcddccbf1ee19b2ce33.svg
Caiming Xiong
user
cxiong
6826a569ea77771e3880f799
Bryan Hooi
admin_assigned
2025-05-16T08:09:35.640Z
651d8032c50012d33e914f2f
/avatars/0a44c9f51fc50ce86582e328c361ea00.svg
Bryan Hooi
user
bhooi
6826a569ea77771e3880f79a
Junnan Li
admin_assigned
2025-05-16T08:09:57.841Z
61f9d3b54ac99e8a1bae85f4
/avatars/ac47d13204dd22452e4bc46e280842d5.svg
JunnanLi
user
JunnanLi
Beyond 'Aha!': Toward Systematic Meta-Abilities Alignment in Large
  Reasoning Models
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
Explicit alignment of large reasoning models with deduction, induction, and abduction through a three-stage pipeline improves scalability and reliability in reasoning tasks.
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