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- <title>Beyond ‘Aha!’: Meta‑Ability Alignment for Large Reasoning Models</title>
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- <h1 class="title is-1 publication-title">Beyond ‘Aha!’: Toward Systematic Meta‑Abilities Alignment in Large Reasoning Models</h1>
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- <span class="author-block"><a href="#" target="_blank">Zhiyuan Hu</a><sup>1*</sup>,</span>
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- <span class="author-block"><a href="#" target="_blank">Yibo Wang</a><sup>2</sup>,</span>
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- <span class="author-block"><a href="#" target="_blank">Hanze Dong</a><sup>3</sup>,</span>
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- <span class="author-block"><a href="#" target="_blank">Yuhui Xu</a><sup>3</sup>,</span>
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- <span class="author-block"><a href="#" target="_blank">Amrita Saha</a><sup>3</sup>,</span>
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- <span class="author-block"><a href="#" target="_blank">Caiming Xiong</a><sup>3</sup>,</span>
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- <span class="author-block"><a href="#" target="_blank">Bryan Hooi</a><sup>1†</sup>,</span>
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- <span class="author-block"><a href="#" target="_blank">Junnan Li</a><sup>3†</sup></span>
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- <span class="author-block"><sup>1</sup>National University of Singapore,</span>
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- <span class="author-block"><sup>2</sup>Tsinghua University,</span>
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- <span class="author-block"><sup>3</sup>Salesforce AI Research</span>
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- <h2 class="subtitle has-text-centered">
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- <span class="dnerf">Meta‑Ability Alignment</span> systematically teaches deduction, induction, and abduction to large language models, turning unpredictable “aha” moments into reliable reasoning capabilities.
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- <p>Large reasoning models (LRMs) possess a latent capacity for long chain‑of‑thought reasoning, but emergent “aha” behaviors are unpredictable and hard to control. We introduce an explicit <em>Meta‑Ability Alignment</em> strategy that separately trains deduction, induction, and abduction specialists on self‑verifiable tasks, then merges them in parameter space and continues domain‑specific RL. This three‑stage recipe boosts performance by &gt;10% over instruction‑tuned baselines and lifts the attainable ceiling after downstream RL, yielding consistent gains across math, coding, and science benchmarks.</p>
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- <pre><code>@article{hu2025metaability,
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- author = {Hu, Zhiyuan and Wang, Yibo and Dong, Hanze and Xu, Yuhui and Saha, Amrita and Xiong, Caiming and Hooi, Bryan and Li, Junnan},
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- title = {Beyond \`Aha!\': Toward Systematic Meta‑Abilities Alignment in Large Reasoning Models},
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- journal = {NeurIPS},
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- year = {2025},
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- url = {https://arxiv.org/abs/2505.00000}
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  }</code></pre>
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+ <meta name="viewport" content="width=device-width, initial-scale=1" />
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+ <meta name="description" content="Beyond ‘Aha!’ — Systematic Meta‑Ability Alignment in Large Reasoning Models presents a three‑stage recipe that explicitly teaches deduction, induction, and abduction, achieving state‑of‑the‑art reasoning performance." />
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+ <meta name="keywords" content="Meta‑Abilities, Deduction, Induction, Abduction, Reinforcement Learning, Large Reasoning Models" />
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+ <title>Beyond “Aha!” — Meta‑Ability Alignment for Reasoning Models</title>
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+ <div class="columns is-centered">
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  <div class="column has-text-centered">
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+ <h1 class="title is-1 publication-title">Beyond “Aha!”: Systematic Meta‑Ability Alignment in Large Reasoning Models</h1>
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+ <div class="is-size-5 publication-authors">
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+ <!-- Update author list as needed -->
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+ <span class="author-block"><a href="#" target="_blank">Zhiyuan Hu</a><sup>1</sup>,</span>
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+ <span class="author-block"><a href="#" target="_blank">Yibo Wang</a><sup>2</sup>,</span>
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+ <span class="author-block"><a href="#" target="_blank">Hanze Dong</a><sup>3</sup>,</span>
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+ <span class="author-block"><a href="#" target="_blank">Yuhui Xu</a><sup>3</sup>,</span>
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+ <span class="author-block"><strong>Amrita Saha</strong><sup>3</sup>,</span>
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+ <span class="author-block"><strong>Caiming Xiong</strong><sup>3</sup>,</span>
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+ <span class="author-block"><strong>Bryan Hooi</strong><sup>1</sup>,</span>
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+ <span class="author-block"><strong>Junnan Li</strong><sup>3</sup></span>
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+ </div>
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+ <div class="is-size-5 publication-authors">
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+ <span class="author-block"><sup>1</sup>National University of Singapore,</span>
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+ <span class="author-block"><sup>2</sup>Tsinghua University,</span>
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+ <span class="author-block"><sup>3</sup>Salesforce AI Research</span>
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+ <a href="https://github.com/zhiyuanhubj/Meta-Ability-Alignment/blob/main/Paper.pdf" target="_blank" class="external-link button is-normal is-rounded is-dark">
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+ <h2 class="title is-3">Abstract</h2>
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+ <p>Large reasoning models (LRMs) possess a latent capacity for long chain‑of‑thought reasoning, but the timing and consistency of emergent “aha” behaviors remain unpredictable. We explicitly align LRMs with three meta‑abilities—<strong>deduction, induction, and abduction</strong>—using automatically generated, self‑verifiable tasks. Our three‑stage pipeline (individual alignment, parameter‑space merging, and domain‑specific reinforcement learning) lifts performance ceilings by&nbsp;≤10 % over instruction‑tuned baselines and delivers state‑of‑the‑art accuracy across math, coding, and science benchmarks.</p>
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+ </section>
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+ <section class="section is-light">
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+ <h2 class="title is-3 has-text-centered">Key Results</h2>
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+ <figure class="image">
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+ <img src="./static/images/results_tables.png" alt="Performance tables showing consistent gains from meta‑ability alignment." />
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+ <figcaption class="has-text-centered">Table&nbsp;1&nbsp;&amp;&nbsp;2: Meta‑ability alignment boosts reasoning performance at both 7B and 32B scales.</figcaption>
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+ </figure>
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+ <br />
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+ <h2 class="title is-3 has-text-centered">Three‑Stage Training Framework</h2>
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+ <figure class="image">
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+ <img src="./static/images/framework.png" alt="Three‑stage meta‑ability alignment framework diagram." />
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+ <figcaption class="has-text-centered">Stage A: Meta‑ability alignment &nbsp;⟶&nbsp; Stage B: Parameter‑space merging &nbsp;⟶&nbsp; Stage C: Domain‑specific RL.</figcaption>
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+ </figure>
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+ </section>
 
 
 
 
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+ <h2 class="title">BibTeX</h2>
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+ <pre><code>@article{hu2025metaability,
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+ author = {Hu, Zhiyuan and Wang, Yibo and Dong, Hanze and Xu, Yuhui and Saha, Amrita and Xiong, Caiming and Hooi, Bryan and Li, Junnan},
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+ title = {Beyond “Aha!”: Systematic Meta‑Ability Alignment in Large Reasoning Models},
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+ journal = {NeurIPS},
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+ year = {2025}
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  }</code></pre>
 
 
 
 
 
 
 
 
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