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REVIEW 4 major objections 6 minor 16 references

Learning Together to Perform Better: Teaching Small-Scale LLMs to Collaborate via Preferential Rationale Tuning

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that a small LLM can improve its own reasoning by training several clones to write competing rationales, then using the ground-truth answer's likelihood to pick winners for preference tuning, without any larger external…

desk verdict A well-constructed training recipe with a clean ablation story, but the unexamined CoT-Collection overlap with test sets undermines every headline number; needs decontamination analysis and variance reporting. read the letter →

arxiv 2506.02519 v1 pith:IUDFXKHQ submitted 2025-06-03 cs.CL

Sohan Patnaik , Milan Aggarwal , Sumit Bhatia , Balaji Krishnamurthy This is my paper
classification cs.CL
keywords smalllanguagemodelsrationaletuningdirectpreferenceoptimizationchain-of-thoughtreasoningdiversitynoexternaldistillationtask-guidedimprovement
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 tries to establish that a small language model can improve its own reasoning without any help from a larger or closed model. It proposes COLLATE, which trains several clones of the same instruction-tuned model on different data splits so they generate distinct rationales, then uses preference optimization to teach them to produce the rationale that best helps the model arrive at the ground-truth answer. The rationale's usefulness is measured by how much it raises the frozen instruction-tuned model's probability of generating the correct answer. Across math word problems, natural language inference, and commonsense reasoning, the resulting rationales beat baselines such as SPIN by up to about seven percentage points and improve models from 1B to 8B parameters. A sympathetic reader would care because this suggests the reasoning bottleneck in small models is not missing knowledge but misaligned rationale selection, and that alignment can be fixed without distilling from proprietary LLMs.

What carries the argument

The load-bearing object is the usefulness score: the probability that the frozen instruction-tuned model $M_{IFT}$ assigns to the ground-truth answer when the candidate rationale is inserted in the prompt, $l_s = \pi_{\theta_{IFT}}(A^T \mid [P_{[I;R]\to A}; I^T; \hat{R}^T_s])$. This single scalar ranks rationales into a winner $\hat{R}^T_w$ and an eliminated $\hat{R}^T_e$, decides which samples survive filtration (Eq. 9), and supplies the preference signal for DPO (Eq. 8). Around it, the framework's machinery consists of (a) three-mode instruction tuning so one model can answer, produce rationales, and score rationale-conditioned answers; (b) $S$ rationale providers trained on disjoint splits to be distinct; and (c) iterative DPO rounds where each provider's previous iteration serves as the reference model.

What would settle it

On a held-out task, rank generated rationales by the paper's usefulness score and then have independent annotators label which rationales are correct; if the score's ranking does not predict which rationale leads to the correct answer better than chance, the selection mechanism at the heart of COLLATE is not doing the claimed work.

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Extended reading notes

Core claim

COLLATE's central claim is that task-guided preferential tuning of multiple rationale providers can turn a single small LLM into a better reasoner. Starting from a multi-mode instruction-tuned model $M_{IFT}$, the framework clones it into $S$ rationale providers, each fine-tuned on a different random split of a rationale corpus so they behave differently. Given a task instruction, each provider writes a candidate rationale; the frozen $M_{IFT}$ scores each candidate by $\pi_{\theta_{IFT}}(A^T \mid [P_{[I;R]\to A}; I^T; \hat{R}^T_s])$, the likelihood of generating the ground-truth answer conditioned on the rationale. The best and worst rationales form the DPO preference pair, and DPO is applied only to samples where the best rationale beats using no rationale at all. The paper reports that this procedure outperforms SPIN, Self-Rewarding LMs, Distilling Step-by-Step, and prompting baselines on GSM8K, WinoGrande, PIQA, HellaSwag, and CSQA, and that gains hold across five model families from 1B to 8B parameters.

Load-bearing premise

The load-bearing premise is that the frozen instruction-tuned model's probability of generating the correct final answer, given a rationale, is a reliable measure of rationale quality; if that probability is noisy, biased, or gameable, the winner/eliminated selection and sample filtration will reinforce the wrong rationales.

Editorial extensions

If this is right

  • A commercial user can improve a small model's reasoning without training on outputs from closed models, avoiding the legal restrictions that such outputs carry.
  • The pipeline applies to any new task that has instruction-answer pairs, because the target task needs no rationale annotations: rationales are generated by the providers and scored by the frozen instruction-tuned model.
  • Inference cost stays comparable to baselines: one provider writes one rationale, since the diversity and selection happen during training.
  • Adding a third rationale provider improves GSM8K from 53.58 to 55.24 and PIQA from 59.57 to 61.73, indicating the method is not saturated at two providers.
  • On LLaMA3-8B, COLLATE cuts the GSM8K performance gap to GPT-4o roughly in half, from 17.16 to 8.75 percentage points.

Reading between the lines

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

  • If the usefulness score is the real bottleneck, swapping in a stronger verifier, such as a reward model trained on the same task, should push the same framework further; this is an extension the paper does not test.
  • The random split of training data across providers could be replaced by routing samples by problem type or difficulty, potentially creating specialized experts and greater diversity; the paper lists this as open future work.
  • Because the scorer comes from the same model family, the framework may inherit the base model's blind spots; on tasks where the model cannot recognize a good rationale even when the right answer is supplied, the selection signal could weaken.
  • The method requires a single ground-truth answer to compute usefulness, so extending it to open-ended generation would need a learned answer verifier or a rubric-based scorer.
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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 / 6 minor

Summary. The paper proposes COLLATE, a framework for improving rationale generation in small language models without a larger teacher LLM. It trains an instruction-tuned model M_IFT on CoT-Collection, creates multiple rationale providers as clones of M_IFT tuned on different data splits, generates a pool of rationales per task instance, scores each rationale by the likelihood M_IFT assigns to the ground-truth answer when conditioned on that rationale, and uses these scores to form DPO preference pairs and to filter training samples. Evaluation is performed by using the generated rationales as additional inputs during task-specific SFT of the base model, with accuracy reported on GSM8K, WinoGrande, PIQA, HellaSwag, and CSQA across model families from 1B to 8B parameters.

Significance. If the reported results are artifact-free, the paper offers a useful alternative to LLM-as-a-judge for small-model rationale improvement: the preference signal is intrinsic to the model being trained, the training loop is self-contained, and the design is tested by the ablations in Table 4 and by a human study in Appendix K. The cross-family and cross-scale results in Table 3 and the released code are also valuable. However, the central quantitative claims require guarding against training/test contamination and random-seed variation before they can be accepted.

major comments (4)
  1. [§3.1, §3.2, Tables 1 and 3] The manuscript trains M_IFT on 140k random samples from CoT-Collection and the rationale providers on 195k random samples from the same collection, but it reports no decontamination check with respect to the five evaluation test sets. Because CoT-Collection is constructed from public instruction and reasoning benchmarks, and GSM8K, WinoGrande, PIQA, HellaSwag, and CSQA are standard members of that pool, exact or near-duplicate test questions may be present in the training subsets. If so, the accuracy gains in Tables 1 and 3 could reflect memorization of answer-specific phrasing rather than improved rationale generation. This is load-bearing because every main result depends on those numbers, and the ablations in Table 4 vary internal components but never remove the potential overlap. Please add a contamination analysis that reports exact and near-duplicate overlap between the CoT-Collection subsets used in §3.1–§3.2 and the evaluation test sets, and retrain the affected components on deduplicated data if overlap is found.
  2. [§4, Tables 1, 3, and 4] All reported accuracies are single-run numbers with no variance or significance testing. Several comparisons are small in absolute terms, for example HellaSwag 99.21 vs. 98.14 and CSQA 80.13 vs. 78.76 in Table 1, and several gaps in Table 3 are around 1.5–2.5%. Without multiple seeds, confidence intervals, or a significance test, the claim that COLLATE is uniformly better than the baselines is not yet fully supported. Please report means and standard deviations over at least three random seeds for the main table, or provide another statistically grounded comparison for the key claims.
  3. [§3.3, Eqs. (7) and (9), Appendix K] The usefulness score in Eqs. (7) and (9) is the sole quality signal for selecting winner and eliminated rationales and for sample filtration, yet it is not validated per dataset beyond the 75-sample human study in Appendix K. The human study reports 84% agreement with likelihood-based selection, and Table 4 row 2 shows that an LLM-as-a-judge alternative is worse, which are useful checks. However, the scorer M_IFT is trained on a subset of the same CoT-Collection used to initialize the rationale providers, and DPO iteratively amplifies preferences, so a noisy or biased usefulness score could systematically select rationales that are answer-conducive under M_IFT but not generally helpful. Please provide per-dataset evidence that the usefulness score correlates with downstream SFT accuracy, or substantially expand the human validation.
  4. [Abstract and §1 vs. §3.1] The abstract and introduction state that COLLATE improves small LMs 'without distilling information from larger LLMs' and 'without relying on any other external LLM.' Sections 3.1 and 3.2, however, use CoT-Collection for both the multi-mode IFT of M_IFT and the DPO initialization of the rationale providers, and CoT-Collection contains rationale triples whose provenance the paper does not disclose. If those rationales were produced by a larger external model, as is publicly documented for CoT-Collection, then the 'no external LLM' claim is stronger than what the experiments demonstrate. Please either document the source of the rationales and qualify the claim accordingly, or show that the method's benefits do not depend on the origin of the pre-existing rationales.
minor comments (6)
  1. [Table 1 and Table 3 headings] The heading 'Comonsense' is misspelled and should read 'Commonsense.'
  2. [Introduction, §4.4] The phrase 'Appendix 4.4' in the introduction should be 'Section 4.4' or 'Table 4,' since the ablation study appears in the main body.
  3. [§3.3, Eq. (8)] The symbol j is used in the reference-model term of Eq. (8) before the defining sentence 'where j=(i-1)' appears; please move the definition before the equation or state it in the equation's notation.
  4. [Appendix G, Table 10 vs. Table 1] Appendix G reports 53.58 for COLLATE with 2 RPs on GSM8K, while Table 1 reports 53.48 for the same configuration; please reconcile the two numbers.
  5. [Appendix J, Figure 2] Figure 2 is described as a 'normalized plot' of task test accuracy versus likelihood, but the plot is not self-explanatory in text; please add axis labels, the correlation coefficient, and a description of what is being normalized.
  6. [Appendix K] The human study would be easier to interpret if the per-annotator agreement for each of the three metrics were reported alongside the Fleiss kappa value, and if the 75 samples were stratified explicitly by dataset in the results table.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: COLLATE's preference signal is grounded in ground-truth answer likelihood and is evaluated by independent downstream SFT accuracy; no fitted parameter is presented as a prediction.

full rationale

COLLATE's derivation chain is self-contained with respect to circularity. The usefulness score (Eq. 7 and Eq. 9) is the likelihood of the frozen instruction-tuned model M_IFT generating the ground-truth answer conditioned on a rationale; this score is used only to construct DPO preference pairs (Eq. 8) and to filter samples (Eq. 9). The reported gains are measured by an independent evaluation protocol: task-specific SFT of the base model on held-out test sets using the generated rationale as additional input (Section 4, Evaluation Procedure). No accuracy number is used as a training signal, no test set is used for model selection, and no fitted parameter is renamed as a prediction. The best rationale provider is selected on a CoT validation split by the same likelihood proxy, not on the task test sets, so the final accuracy is not forced by construction. The loop in which a model scores rationales for another instance of the same model family is self-referential in spirit, but the reward is anchored to external ground-truth answers and is independently validated by the human study (84% alignment, Appendix K) and by the downstream accuracy experiments. The reviewer's decontamination concern regarding CoT-Collection subsets overlapping the evaluation test sets is a data-validity risk, not a circularity of the derivation; likewise, the use of CoT-Collection rationales (originally produced by larger models) weakens the 'no external LLM' framing but does not make the method's equations reduce to their inputs. No load-bearing self-citations or imported uniqueness theorems appear. Therefore no circular step can be quoted, and the score is 0.

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

The method introduces no new physical or conceptual entities. It relies on standard DPO and IFT machinery, but makes strong assumptions about the reliability of the frozen reference model as a scorer and about the diversity obtained from random splits.

free parameters (3)
  • Number of rationale providers (S) = 2 in main experiments, 3 in Appendix G
    Set by hand; more providers increase performance (Table 10). Affects diversity of rationales.
  • DPO coefficient beta = 0.1
    Standard DPO hyperparameter, chosen by hand.
  • Number of DPO iterations and epochs = 2 iterations, 10 epochs per iteration
    Chosen on validation; performance increases with iterations (Table 9).
assumptions (3)
  • domain assumption The likelihood of the ground-truth answer under M_IFT conditioned on a rationale is a reliable proxy for rationale usefulness.
    Central to winner/eliminated selection (Eq. 7) and sample filtration (Eq. 9). Paper provides a human study with 84% alignment on 75 samples, but this is a strong assumption about the scorer.
  • domain assumption Randomly splitting the training data into S splits yields sufficiently distinct rationale providers.
    The method relies on this diversity for DPO preference pairs. The paper validates different random splits through ablations, but there is no theoretical guarantee of diversity.
  • domain assumption The CoT-Collection dataset provides high-quality rationales for bootstrapping the IFT and rationale providers.
    The entire method depends on this external dataset, which was likely generated by large LLMs. This means the claim of no reliance on other LLMs is not absolute for the bootstrapping phase.

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Cite this review

Pith. "Pith review of Learning Together to Perform Better: Teaching Small-Scale LLMs to Collaborate via Preferential Rationale Tuning." pith.science (2026). https://pith.science/paper/IUDFXKHQ

@misc{pith2026250602519,
  author       = {Pith},
  title        = {Pith review of: Learning Together to Perform Better: Teaching Small-Scale LLMs to Collaborate via Preferential Rationale Tuning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IUDFXKHQ}},
  note         = {Machine review of arXiv:2506.02519}
}
read the original abstract

LLMssuch as GPT-4 have shown a remarkable ability to solve complex questions by generating step-by-step rationales. Prior works have utilized this capability to improve smaller and cheaper LMs (say, with 7B parameters). However, various practical constraints, such as copyright and legal issues, owing to lack of transparency in the pre-training data of large (often closed) models, prevent their use in commercial settings. Little focus has been given to improving the innate reasoning ability of smaller models without distilling information from larger LLMs. To address this, we propose COLLATE, a trainable framework that tunes a (small) LLM to generate those outputs from a pool of diverse rationales that selectively improves the downstream task. COLLATE enforces multiple instances of the same LLM to exhibit distinct behavior and employs them to generate rationales to obtain diverse outputs. The LLM is then tuned via preference optimization to choose the candidate rationale which maximizes the likelihood of ground-truth answer. COLLATE outperforms several trainable and prompting baselines on 5 datasets across 3 domains: maths problem solving, natural language inference, and commonsense reasoning. We show the eff icacy of COLLATE on LLMs from different model families across varying parameter scales (1B to 8B) and demonstrate the benefit of multiple rationale providers guided by the end task through ablations. Code is released here (https://github.com/Sohanpatnaik106/collate).

Figures

Figures reproduced from arXiv: 2506.02519 by the authors.

Figure 1
Figure 1. Overview of architecture of COLLATE. The task-instruction ( [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Normalized plot between task test accuracy [PITH_FULL_IMAGE:figures/full_fig_p018_2.png] view at source ↗

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Reference graph

Works this paper leans on

16 extracted references · 14 canonical work pages

  1. [1]

    15 samples ran- domly from the test sets of each of the 5 task datasets

    We collected a total of 75 samples based on random selection by taking an equal number of samples for each task i.e. 15 samples ran- domly from the test sets of each of the 5 task datasets

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    InAdvances in Neural Information Processing Systems, volume 36, pages 11809–11822

    Tree of thoughts: Deliberate problem solving with large language models. InAdvances in Neural Information Processing Systems, volume 36, pages 11809–11822. Curran Associates, Inc. Zhangyue Yin, Qiushi Sun, Cheng Chang, Qipeng Guo, Junqi Dai, Xuanjing Huang, and Xipeng Qiu

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    toaster".✗ Rationale Provider 1:The issue described is that the eggplant didn’t fit in the toaster. Therefore, the correct answer is “eggplant

    Exchange-of-thought: Enhancing large lan- guage model capabilities through cross-model com- munication. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Process- ing, pages 15135–15153, Singapore. Association for Computational Linguistics. Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, and ...

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    D Dataset Samples Details of datasets were discussed in the ‘Experi- ments and Evaluation’ section in the main paper

    for an elaborate discussion of the details. D Dataset Samples Details of datasets were discussed in the ‘Experi- ments and Evaluation’ section in the main paper. Table 7 in the appendix shows samples of instruc- tions for each dataset from all task domains - (i) Maths Problem Solving (GSM8K), (ii) Natural Lan- guage Inference (WinoGrande and PIQA), and (i...

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    For each sample, we obtain the rationalesR1g, R2g from the two LLM rationale providers. Once the above rationales are obtained, we em- ployed three paid human annotators and presented them with the instruction in each sample along with different rationales obtained above. The human evaluators are asked to judge the quality of differ- ent rationales based ...

    work page

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    Provide a label out of 0 or 1 such that 0 means that the final rationale is totally wrong; and 1 means that the final rationale is totally correct

    Question 1: Is the final rationale obtained from COLLATE useful for answering the question correctly? The rationale is useful if it is correct and provides the correct expla- nation on how the answer for the instruction in the sample should be derived. Provide a label out of 0 or 1 such that 0 means that the final rationale is totally wrong; and 1 means t...

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    Provide a label of 0 or 1 where 0 means that none of the rationales is better than the other and 1 means that one rationale is better than the other

    Question 2: Compare the two rationales ob- tained using the two rationale providers - R1g and R2g. Provide a label of 0 or 1 where 0 means that none of the rationales is better than the other and 1 means that one rationale is better than the other

    work page

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    Definition of Metrics Estimated from Human Labels Different rationales were presented to human evaluators in jumbled order to avoid biases while comparing rationales

    Question 3: In Question 2, in case one ra- tionale is better than the other (between the rationales obtained from the two rationale providers), select the better rationale. Definition of Metrics Estimated from Human Labels Different rationales were presented to human evaluators in jumbled order to avoid biases while comparing rationales. Based on the judg...

    work page

Show all 16 references
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    totally correct

    Final Rationale Alignment – % proportion of samples which were assigned label 1 i.e. totally correct

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    cases where one of the two ratio- nales is better than the other (label 1)

    Diversity b/w two Rationales - % proportion of samples where the two rationales R1g and R2g obtained from two rationale providers are different i.e. cases where one of the two ratio- nales is better than the other (label 1). This metric is estimated to verify if the variants t...

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    one of the generated rationales is judged better than the other generated rationale (comparing R1g and R2g)

    Better Rationale Alignment with Likelihood based Selection: We consider samples where label 1 is provided to Question 2 i.e. one of the generated rationales is judged better than the other generated rationale (comparing R1g and R2g). We estimate the metric as % proportion case...

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    From Table 13, we can observe that the final rationale alignment is 72.0% which means that final rationale obtained from COLLATE is reliable and aligns with human preferences. Metric Name Value (in %) Final Rationale Alignment 72.0 Diversity b/w two Rationales 78.67 Better Rat...

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    This means that employing two variants of same LLM is useful to obtain distinct and diverse rationales which are useful to improve quality of preference data for DPO

    Rationales from the two rationale providers are diverse: It is observed that for 78.67% cases, one rationale obtained was judged to be better than the other generated rationale. This means that employing two variants of same LLM is useful to obtain distinct and diverse rationa...

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    This shows that our choice of using likelihood of final GT answer for selecting winner ra- tionale aligns with human preferences and is suitable to obtain the preference data

    Likelihood based rationale selection aligns with human preferences: For 84.0% cases, better generated rationale determined based on human preferences matches the better ra- tionale based on likelihood-based utility score. This shows that our choice of using likelihood of final...

    2024

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    The results are summarized in Table 14, where Table 14: Performance comparison of COLLATE with SPIN on additional benchmarks

    Mathematical Reasoning:AIME focuses on complex math word problems.2) Commonsense Reasoning:CROW requires applying background knowledge to infer correct outcomes.3) Multi-hop QA:HotpotQA evaluates long chain reasoning and contextual grounding. The results are summarized in Tabl...

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    InAdvances in Neural Information Processing Systems, volume 36, pages 53728–53741

    Direct preference optimization: Your language model is secretly a reward model. InAdvances in Neural Information Processing Systems, volume 36, pages 53728–53741. Curran Associates, Inc. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Ya...

    2023 arXiv

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