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Learning from Cognition: Enhancing RL Efficiency for LLM Reasoning via Hierarchical Metacognitive Decomposition and Refinement

Paper · 2026 · Zexu Sun, Yongcheng Zeng, Erxue Min et al.

Summary

Zero-RL training with verifiable rewards wastes much of its compute on weaker base LLMs. With a fixed prompt template, many problems yield no correct rollout, so accuracy-based filtering discards them. Cog-Rethinker tackles this by adding two cognition-inspired rollout stages on top of a DAPO-style pipeline. Problems that receive zero accuracy in the direct rollout are first re-attempted by having the policy split them into subproblems. This step is guided by demonstrations drawn from a metacognitive buffer, which stores the policy's own successful decompositions and is searched by problem similarity. Problems that still fail are then retried with a reflection template that shows the model its earlier incorrect answers. Correct outputs from both stages are mapped back to the original prompt format and used for SFT. This supports cold-start and keeps training and testing consistent. On Qwen2.5-1.5B-Base and Qwen2.5-7B-Base, the method mostly outperforms GRPO, DAPO and Reinforce++ on math and science benchmarks. It also produces more usable samples early on and converges faster, which makes RL more practical for smaller models.

Key points

  • With Qwen2.5-7B-Base, Cog-Rethinker scores 93.32% on GSM8K, 80.60% on MATH-500, 26.67% on both AIME 24 and AIME 25, 73.50% on AMC 2023, 65.52% on Gaokao 2023en, 36.42% on GPQA-diamond and 44.22% on Olympiad.
  • At 1.5B scale, it is the only method with a nonzero AIME 25 score (all others reach 0.00%), though it slightly trails DAPO on GSM8K.
  • In ablations, removing SFT hurts most (1.5B GSM8K falls to 72.25%). Removing the buffer drops 7B MATH-500 to 78.00%, and removing reflection drops 1.5B Olympiad to 18.67.
  • Compared with DAPO, it yields more valid training batches with less generation overhead, gives shorter stabilized responses, and shows better accuracy per cumulative batch on MATH-500.
  • Limitations: results depend on base-model quality and on the buffer demonstrations, the binary reward gives no credit for intermediate steps, and only math reasoning was tested. Code is released as Cog-Rethinker on GitHub.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢BaiduBeijing · China74 works · 15,263 citations🎓Renmin University of ChinaBeijing · China114 works · 16,118 citations🏛️Chinese Academy of SciencesBeijing · China194 works · 23,906 citations🔬Institute of AutomationBeijing · China70 works · 14,251 citations🎓Shenzhen UniversityShenzhen · China16 works · 95 citations🎓Shenzhen Technology UniversityShenzhen · China3 works · 59 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 works

Cite

Cite as Zexu Sun, Yongcheng Zeng, Erxue Min, Heyang Gao, Bokai Ji, Dugang Liu, Xing Tang, Xiuqiang He and Xu Chen. 2026. Learning from Cognition: Enhancing RL Efficiency for LLM Reasoning via Hierarchical Metacognitive Decomposition and Refinement. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1022

@inproceedings{sun2026learning,
  title = {{Learning from Cognition: Enhancing RL Efficiency for LLM Reasoning via Hierarchical Metacognitive Decomposition and Refinement}},
  author = {Zexu Sun, Yongcheng Zeng, Erxue Min, Heyang Gao, Bokai Ji, Dugang Liu, Xing Tang, Xiuqiang He and Xu Chen},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1022},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1022}
}

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