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REVIEW 4 major objections 5 minor 1 cited by

Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Mixture of Reasoning (MoR) trains a 7B model on GPT-4o-generated reasoning templates, reaching 0.734 with plain IO prompting (13.5% over baseline) and 0.730 with CoT prompting.

desk verdict A plausible SFT recipe undone by misreported headline numbers, tiny test sets, and a missing overlap check; not ready for peer review. read the letter →

arxiv 2507.00606 v2 pith:TPQ2RXGL submitted 2025-07-01 cs.CL cs.AI

Tao Xiong , Xavier Hu , Wenyan Fan , Shengyu Zhang This is my paper
classification cs.CLcs.AI
keywords MixtureofReasoningsupervisedfine-tuningchaintemplatestask-adaptiveChain-of-ThoughtpromptingGPT-4oQwen2.5-7Bpromptengineering
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces Mixture of Reasoning (MoR), a two-phase training framework that teaches a small language model to reason with many strategies at once. In the first phase, GPT-4o generates hundreds of reasoning-chain templates; in the second, each training question is paired with the template GPT-4o judges most useful, the pair is run through GPT-4o, and only correct answers are kept for supervised fine-tuning. The authors claim improvements to 0.730 with a simple 'let's think step by step' prompt (2.2% over baseline) and to 0.734 with no special prompt (13.5% over baseline), matching or beating task-specific prompt engineering across five reasoning benchmarks. The point of the work is that task-adaptive reasoning can be internalized during training rather than elicited by hand-crafted prompts.

What carries the argument

The central object is the reasoning-chain template: a short instruction that frames how a problem should be approached (e.g., 'devise an experiment to help solve that problem' or 'explore how the solution can be monetized'). The machinery is a training-time selection loop: for each question, GPT-4o chooses the best template among five randomly drawn candidates, produces a reasoning trace under that template, and only correct traces are retained for supervised fine-tuning. This loop converts prompt engineering into dataset construction, so that at inference time the model supplies its own strategy choice.

What would settle it

Evaluate MoR150 on a held-out test set per benchmark that is verifiably disjoint from the SFT training samples; if the margin over Qwen2.5-7B shrinks to noise or disappears, the reported gains are artifacts of the small, possibly overlapping sample pools rather than learned adaptive reasoning.

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Extended reading notes

Core claim

MoR claims that a model can learn to select and execute an appropriate reasoning strategy on its own, provided it is trained on examples where a stronger model has already made that selection. The training data is built by sampling questions from HotpotQA, StrategyQA, MMLU, BigTom, and Trivial Creative Writing; for each question, five reasoning templates are drawn at random, GPT-4o picks the one it considers best, reasons with it, and the resulting correct traces are used as supervised fine-tuning supervision. The authors report their MoR model reaching 0.730 with CoT prompting and 0.734 with direct IO prompting, which they contrast with baseline scores of 0.708 and 0.599; the displayed table records the 0.730 under MoR150-CoT and the 0.734 under MoR500-IO. The authors interpret this as evidence that explicit task-specific reasoning prompts can be replaced by training-time strategy diversity.

Load-bearing premise

The load-bearing premise is that a small model trained on GPT-4o's correct, template-guided answers will recreate that reasoning on new questions on its own, without the oracle that selected the templates, and that the 50-question test pools are fair, disjoint samples of the benchmarks.

Editorial extensions

If this is right

  • Models trained with MoR no longer need task-specific prompts; a generic 'let's think step by step' suffices, and on MoR500 even a direct IO prompt reaches the best score.
  • The number of templates is not monotonic: 150 templates gives the best CoT result, while 500 gives the best IO result, suggesting a trade-off between strategy diversity and trainable signal.
  • MoR improves Theory of Mind reasoning on BigTom, as shown in the case study, where baseline reasoning fails to track a protagonist's changing belief.
  • The method transfers across five heterogeneous benchmarks with a single recipe, pointing to a generalizable alternative to prompt engineering.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The displayed numbers do not fully match the abstract: Table 1 lists the 0.734 IO score under MoR500, not MoR150 as the abstract states, and Section 4.3 refers to an extended 200-sample comparison (Table X) that is not present in the manuscript.
  • The training-time template selector (GPT-4o) is a hidden oracle: the final model must internalize the selection. Ablating the oracle by pairing random templates with the same questions would separate the contribution of adaptive selection from the contribution of template diversity.
  • Because the SFT data keeps only correct GPT-4o traces, the method is a form of selective distillation; testing MoR with a stronger base model or a harder benchmark would show whether the strategy diversity, rather than the teacher's raw correctness, is the active ingredient.
  • The paper does not state whether the 50 test samples per benchmark are disjoint from the training samples; a verified-disjoint held-out evaluation is the minimal check before the reported gains can be read as generalization.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes Mixture of Reasoning (MoR), a two-phase framework that generates diverse reasoning-chain templates with GPT-4o and then constructs a supervised fine-tuning (SFT) dataset by pairing those templates with benchmark samples, filtering to responses judged correct. The resulting models, MoR50 through MoR500, are evaluated on HotpotQA, StrategyQA, MMLU, BigTom, and Trivial Creative Writing using both input-output (IO) and chain-of-thought (CoT) prompts with Qwen2.5-7B as the base model. The abstract claims that MoR150 achieves 0.730 with CoT prompting and 0.734 with IO prompting, corresponding to 2.2% and 13.5% improvements over baselines. The paper argues that MoR internalizes task-adaptive reasoning and removes the need for task-specific prompt engineering.

Significance. If validated, the MoR framework would be a useful contribution to reducing prompt engineering for small instruction-tuned models: the two-phase pipeline is clearly described, Algorithm 1 is explicit, and the idea of distilling multiple reasoning strategies into model weights is plausible and reproducible in principle. The qualitative case study in Figure 2 also illustrates a concrete setting where the fine-tuned model appears to reason more carefully. However, the current evidence does not support the headline numbers: the abstract attributes a 0.734 score to MoR150 while Table 1 assigns that value to MoR500 with the IO prompt, and the promised extended evaluation in 'Table X' is absent. The unresolved question of whether training and test samples are disjoint is load-bearing for the generalization claim, and the correctness filter in Algorithm 1 introduces a selection bias that is not discussed. The contribution is therefore significant only if these issues are resolved with a properly specified held-out evaluation.

major comments (4)
  1. [Abstract and §4.2] The abstract states that 'MoR150 achieving 0.730 (2.2% improvement) using CoT prompting and 0.734 (13.5% improvement) compared to baselines,' but Table 1 reports MoR150 with the IO prompt at 0.700 overall and MoR500 with the IO prompt at 0.734 overall. The 13.5% gap corresponds to the difference between the baseline IO score of 0.599 and the MoR500 IO score of 0.734, not to MoR150. This is an inconsistency in the paper's headline result and must be corrected.
  2. [§3.2 and §4.1] The manuscript never states that the 50 test samples per dataset (80 for BigTom) are disjoint from the samples used to construct DSFT in Algorithm 1. Because DSFT is built by sampling from the same benchmarks that are then used for testing, the reported gains could in principle reflect memorization of GPT-4o-generated correct solutions rather than transferable reasoning. The authors need to state explicitly how the train/test split was performed, or provide a separate held-out evaluation that rules out overlap.
  3. [§4.3] The text says 'As shown in Table X, the extended MoR150 maintains a consistent advantage over the baseline,' but no Table X appears in the manuscript. With only 50 samples per dataset, the per-task accuracies in Table 1 have wide confidence intervals, and no significance testing is reported. The extended evaluation is precisely the evidence needed to assess whether MoR provides a real advantage, so its omission is load-bearing.
  4. [Algorithm 1, line 9] The SFT dataset keeps only responses that are evaluated as correct, so the training distribution is a filtered subset of the benchmark. Even with a disjoint test set, this selection bias could make the fine-tuned model perform well on easy or selectable cases while failing on harder ones. The paper should report evaluation on the full test distribution without filtering, or otherwise demonstrate that the filter does not materially change the difficulty distribution.
minor comments (5)
  1. [§3.2 and Algorithm 1] The text says that '5 reasoning chain templates Tsub' are selected per sample, but Algorithm 1 line 4 uses RandomSelect(T, N) with the comment 'Select N templates'; this inconsistency should be resolved.
  2. [Figure 2] The MoR150 response in Figure 2 ends with 'Answer: b) Isabela believes the bag she took from the pantry contains powdered sugar,' while the ground truth is listed as 'a) Isabela believes the bag she took from the pantry contains flour.' The case study appears to contain an error in the displayed answer, which obscures the claimed qualitative improvement.
  3. [Table 1] The HotpotQA scores show a large drop from the baseline IO accuracy of 1.00 to MoR50's IO accuracy of 0.540; this is not discussed in the analysis and may indicate a training instability or evaluation issue that deserves comment.
  4. [§4.1] The phrase 'models fine-tuned on different numbers of X-chain of thought datasets' is unclear; it should be rewritten to say that the models are fine-tuned on datasets constructed from different numbers of reasoning templates.
  5. [References] The Qwen2.5 technical report reference has a malformed author field ('Qwen, :, An Yang, ...') that needs correction, and the bibliography would benefit from a consistent preprint format.

Circularity Check

0 steps flagged · score 2.0 of 10

No construction-level circularity; evaluation is on external benchmark splits, with only a minor background self-citation.

full rationale

MoR's claimed gains are evaluated on standard external benchmarks (HotpotQA, StrategyQA, MMLU, BigTom, Trivial Creative Writing) using 50 held-out samples per dataset. The training pipeline (GPT-4o template generation, template selection, correctness filtering, SFT) determines the model weights, but the reported accuracies are measurements on those benchmark test samples, not quantities derived by definition from the training construction. No equation or table in the paper defines a reported number as a function of the fitted data. The only self-citation is the background reference to the authors' instruction-tuning survey (Zhang et al., 2024) for the definition of SFT; this is not load-bearing. The missing 'Table X' for the 200-sample extension and the absence of an explicit statement that training and test samples are disjoint are external-validity or contamination risks, not circularity, because the paper never asserts that the test split was produced by the training algorithm. Accordingly, the derivation is self-contained with respect to circularity, and the score reflects only the minor background self-citation.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim depends on a handful of unstated or loosely specified domain assumptions rather than on fitted mathematical parameters. The key uncertainty is whether the filtered GPT-4o-derived training distribution transfers to the held-out test sets; none of these assumptions is tested in an ablation.

free parameters (2)
  • Number of reasoning templates M = 50, 150, 300, 500
    The paper sweeps this value by hand to define MoR50 through MoR500; the central claim picks MoR150 and MoR500 as best, so M is a hand-chosen hyperparameter.
  • Number of training samples per dataset (N) = not specified
    The method selects N samples per dataset for the SFT set, but N is never given in the paper, so the training set size is an unspecified design choice that affects all reported numbers.
assumptions (3)
  • domain assumption GPT-4o's generated reasoning templates and best-template selections are useful for training a smaller model
    The entire pipeline in Section 3 relies on GPT-4o as a teacher; the paper does not validate this by ablating template quality.
  • domain assumption SFT on filtered correct answers transfers to held-out test sets
    Section 3.2 keeps only examples GPT-4o answered correctly; the paper assumes this filtered distribution is representative enough to improve test accuracy, but provides no transfer analysis.
  • domain assumption The test set is disjoint from the SFT training samples
    Section 4.1 says 50 samples per dataset are chosen for testing and Section 3.2 samples training data from the same five datasets, but disjointness is never stated.

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Cite this review

Pith. "Pith review of Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies." pith.science (2026). https://pith.science/paper/TPQ2RXGL

@misc{pith2026250700606,
  author       = {Pith},
  title        = {Pith review of: Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TPQ2RXGL}},
  note         = {Machine review of arXiv:2507.00606}
}
read the original abstract

Large language models (LLMs) excel in complex tasks through advanced prompting techniques like Chain-of-Thought (CoT) and Tree-of-Thought (ToT), but their reliance on manually crafted, task-specific prompts limits adaptability and efficiency. We introduce Mixture of Reasoning (MoR), a training framework that embeds diverse reasoning strategies into LLMs for autonomous, task-adaptive reasoning without external prompt engineering. MoR has two phases: Thought Generation, creating reasoning chain templates with models like GPT-4o, and SFT Dataset Construction, pairing templates with benchmark datasets for supervised fine-tuning. Our experiments show that MoR significantly enhances performance, with MoR150 achieving 0.730 (2.2% improvement) using CoT prompting and 0.734 (13.5% improvement) compared to baselines. MoR eliminates the need for task-specific prompts, offering a generalizable solution for robust reasoning across diverse tasks.

Figures

Figures reproduced from arXiv: 2507.00606 by the authors.

Figure 1
Figure 1. Overview of our proposed MoR framework. The MoR framework can be divided into two stages: (1) [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Case study comparing the baseline model and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey

    cs.AI 2025-07 conditional novelty 3.0 of 10

    A comprehensive review that categorizes methods for shortening and adaptively triggering chain-of-thought reasoning in large language models.

Reference graph

Works this paper leans on

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Reviewed August 6, 2026 · model on record in the stance chip above.