Enhance Reasoning of Large Language Models via Macro-Micro Self-Training
Authors: Mingchen Zhang, Yiran Zhao


Reviewer paper summaries: This paper studies the improvement of reasoning abilities of LLMs for solving math problems, primarily through solutions in code format. The authors propose iteratively refining the generated code from both whole and part outputs. Using self-evaluations and self-modifications of LLMs, the experimental results demonstrate the effectiveness of the proposed methods.
Summary of strengths: - The focus on evaluating both whole and part outputs is significant, as it could lead to more comprehensive improvements in reasoning abilities.
Summary of weaknesses: - The proposed method does not involve training a model but merely prompting LLMs. Therefore, the term “self-training” is unsuitable and misleading.

- The method for solving reasoning tasks through global and local perspectives is not novel. Previous works such as [1] and [2] have already adopted this approach and fine-tuned models accordingly.

- The relationship between cost and performance should be considered. Refining outputs will increase inference time. Similar methods like self-consistency [4] and self-refine [5] should be compared. Recent work [3] shows the ineffectiveness of various prompting methods, where the effect diminishes relative to the unit cost. The authors should address this issue.


[1] Uesato, Jonathan, et al. "Solving math word problems with process-and outcome-based feedback." arXiv preprint arXiv:2211.14275 (2022).

[2] Havrilla, Alex, et al. "Glore: When, where, and how to improve llm reasoning via global and local refinements." arXiv preprint arXiv:2402.10963 (2024).

[3] Wang, Junlin, et al. "Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies." arXiv preprint arXiv:2406.06461 (2024).

[4] Madaan, Aman, et al. "Self-refine: Iterative refinement with self-feedback." Advances in Neural Information Processing Systems 36 (2024).

[5] Wang, Xuezhi, et al. "Self-consistency improves chain of thought reasoning in language models." arXiv preprint arXiv:2203.11171 (2022).
Comments, suggestions and typos: - The Python code in Section 2.2 lacks descriptions, such as captions, which mislead readers.

- In lines 69-75, citations should use `\citet`, as in “xxx et al (20xx) propose …”.
Overall assessment scores: 2