REVIEW 3 major objections 7 minor 40 references
CRISP: Complex Reasoning with Interpretable Step-based Plans
T0 review · 3 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash
Pith's one-line read Fine-tuning a small model on curated step-plans beats few-shot prompting from far larger models.
desk verdict A real dataset and a plausible result, but the missing decontamination check and unvalidated judge keep the headline numbers from being fully trustworthy. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is CRISP (Complex Reasoning with Interpretable Step-based Plans), a dataset of high-level plans, together with its two-stage validation pipeline. Plans are generated by prompting a model to abstract away equations, function names, and computations from a detailed solution, leaving a 3-5 step logical strategy; they are then filtered intrinsically by an LLM judge on clarity, coherence, conciseness, and completeness, and extrinsically by checking that solutions produced with the plan beat solutions produced without it. This filtered data is the training signal for a LoRA fine-tune of an 8B model, and that fine-tuned model is the planner whose outputs are fed to a separate solver. The mechanism that carries the argument is the demonstration that the same underlying task—solving a problem—improves when the solver receives a plan from the trained planner rather than from a vanilla large model or from chain-of-thought reasoning.
What would settle it
A direct test would be to re-run the CRISP filtering pipeline with a different judge, ideally human raters or an independent model family, and then fine-tune on the resulting plans; the central claim would be undercut if the fine-tuned planner no longer outperforms few-shot planning by a large margin, or if plans rejected by the original judge work just as well.
Extended reading notes
Core claim
On its own terms, the paper's discovery is that explicit high-level plan generation is a trainable skill, and that CRISP provides enough validated training signal to teach it. Using Mixtral-8x22B-Instruct to condense detailed solutions from Magpie-Reasoning-V1-150K into 3-5 step plans, then discarding plans judged unclear, incoherent, verbose, or incomplete by Llama-3.1-70B-Instruct, and further discarding plans that did not increase the number of correct solutions in a with-versus-without comparison, the authors retain 65,800 math plans and 56,200 code plans. LoRA fine-tuning of Granite-3.1-8B-Instruct on this data produces a planner whose plans improve a Llama-3.1-70B-Instruct solver to 76.2 pass@1 on MBPP, 85.3 on HumanEval, 95.9 accuracy on GSM8K, and 73.1 on MATH, with error reductions over chain-of-thought up to 28.1%. The same fine-tuned planner also improves the small solver, and plans trained on math transfer to code and vice versa with only small losses relative to in-domain training. The authors take this as evidence that high-level planning generalizes across domains and is worth training explicitly.
Load-bearing premise
Everything rests on the assumption that the LLM judge's ratings of clarity, coherence, conciseness, and completeness are a reliable guide to how useful a plan really is, because the paper reports no human check of those ratings.
Editorial extensions
If this is right
- High-level planning can be improved with a few epochs of parameter-efficient fine-tuning, so planning ability is not fixed by model scale.
- A fine-tuned small planner can serve as a cheap front-end for a large solver, cutting errors on math and code benchmarks by up to roughly a quarter relative to chain-of-thought.
- Planning skills learned on math transfer to code, and coding plans transfer back to math, so a single planning model can span both domains.
- Filtering plans by both intrinsic quality and downstream usefulness produces training data that outperforms unfiltered or less rigorously checked alternatives.
- Concise plans with fewer, well-structured steps can outperform longer plans, suggesting planning quality matters more than verbosity.
Reading between the lines
- A natural next test is whether the same gains hold when the filtering judge is replaced by human raters or an independent LLM; until then, the dataset's quality is tied to one judge's preferences.
- The plan-and-solve setup could be combined with self-consistency or search over plans, which the paper does not explore but which would likely amplify the reported gains.
- Because the transfer is asymmetric, with math-trained plans helping code more than the reverse, the underlying reasoning skills may be domain-general; this could be tested on symbolic or commonsense reasoning benchmarks.
- The public release of CRISP makes it possible to ablate the filtering stages directly, for example by training on the unfiltered plans to measure how much of the gain comes from validation.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces CRISP, a multi-domain dataset of high-level plans for mathematical reasoning and code generation, built by abstracting detailed solutions from Magpie-Reasoning-V1-150K and validating plans both intrinsically (LLM-as-a-judge on clarity, coherence, conciseness, completeness) and extrinsically (downstream solution accuracy with vs. without the plan). The authors fine-tune Granite-3.1-8B-Instruct (and Llama-3.1-8B-Instruct) with LoRA on CRISP and show that the resulting planner outperforms few-shot planners, including much larger vanilla models, when their plans are fed to a solver, on MBPP, HumanEval, GSM8K, and MATH. They further report that fine-tuning on one domain transfers to the other, with mathematical training transferring to code better than the reverse.
Significance. If the results hold, the paper makes a useful empirical contribution: it demonstrates that high-level planning is a trainable capability rather than an emergent one, offers a public multi-domain planning dataset with dual intrinsic/extrinsic validation, and provides consistent improvements over CoT baselines across four benchmarks and two small-model families. The out-of-domain transfer experiments strengthen the generalization claim. However, the central claim currently rests on point estimates without contamination analysis or statistical significance testing, so the significance is conditional on those gaps being addressed.
major comments (3)
- [Section 3.1, Tables 2 and 3] The paper never reports a contamination analysis between the CRISP training data, derived from Magpie-Reasoning-V1-150K, and the test sets of MBPP, HumanEval, GSM8K, and MATH. Since the fine-tuned planner is trained on a large synthetic corpus that may contain problems resembling or identical to benchmark items, the reported gains (e.g., 85.3 pass@1 on HumanEval and 95.9 on GSM8K with the large solver) could partly reflect memorization rather than learned planning. A decontamination check (n-gram overlap, embedding similarity, or removal of near-duplicate training examples) is needed to support the central claim of generalizable planning ability. This is my main technical concern.
- [Section 4.3, Table 2] All reported results are single point estimates without confidence intervals, standard errors, or significance tests. Several comparisons are close (e.g., 95.9 vs. 94.8 on GSM8K, or 76.2 vs. 74.1 on MBPP with the large solver), and the relative error-reduction percentages amplify small absolute differences. Without variance estimates or repeated runs, the claim that the fine-tuned planner 'vastly outperforms' baselines is not statistically established for these near-adjacent numbers. Please add error bars, significance tests, or at least a variance analysis over multiple seeds or sampling temperatures.
- [Sections 3.2 and 4.4] The LLM-as-a-judge used for intrinsic filtering (Llama-3.1-70B-Instruct) is the same model family as the vanilla-large competitor in the intrinsic evaluation, and there is no human validation of the judge's four criteria. The authors acknowledge the possible self-preference bias in Section 4.4, but the dataset construction itself depends on this judge: plans that fail the binary judgment are discarded, so a systematic judge bias could shape the dataset. Reporting inter-annotator agreement with human ratings on a sample of plans, or at least a human spot-check, would substantially strengthen the validity of both the filtering pipeline and the intrinsic-quality claims.
minor comments (7)
- [Section 4.1 vs. Section 4.2] Section 4.1 states that LoRA fine-tuning was applied to 'Granite-3.1-70B-Instruct', while Section 4.2 and the rest of the paper refer to the small model Granite-3.1-8B-Instruct. This appears to be a typo, but it is confusing for reproducibility; please correct.
- [Table 2 and Table 3] The 'Err↓' header indicates relative error reduction, but some cells use an upward arrow (e.g., 'Vanilla Small Large' shows 1.1%↑), and the meaning of the arrow is never defined. In addition, the GSM8K value '1.2%↑' appears inconsistent with the underlying numbers (error increases by over 12%), so the percentages should be recomputed and the notation clarified.
- [Section 3.3] The sentence 'The intrinsic filtering stage increases accuracy by 0.72 points on GSM8K and MATH' is ambiguous: it is unclear whether 0.72 is the sum over the two benchmarks or the average, and the same issue occurs for the MBPP/HumanEval pair. Reporting per-benchmark numbers would be clearer.
- [Appendix A.1] The hyperparameter description says 'dropout ratio of 0.05%', which is almost certainly intended to be a dropout probability of 0.05 (5%). Please clarify the value.
- [Section 2] The paragraph on Plan-and-Execute approaches contains a duplicated sentence ('However, these approaches are primarily benchmarked within a single domain...') that should be removed.
- [Section 1] There is a typo in the phrase 'self-generating a high-level plan. by the LLM before executing the task'; the period should be a comma or the sentence should be rephrased.
- [Section 6 (Limitations)] The limitations section is very brief and does not mention the absence of a contamination check, the lack of human validation of the LLM judge, or the absence of statistical significance testing. These points should be acknowledged explicitly.
Circularity Check
Main benchmark claims are independent; only the intrinsic plan-quality evaluation is mildly self-referential via judge reuse.
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fitted input called prediction
[Sections 3.2 and 4.4 (filtering/validation and intrinsic evaluation)]
"First, we apply 'LLM as a Judge' with Llama-3.1-70B-Instruct to determine whether the generated plans are concise, clear, coherent, and complete. ... Surprisingly, although we used Llama-3.1-70B-Instruct as both a judge and a competitor, which should create a bias toward its own generations ... it preferred the plans generated by the small fine-tuned model across all datasets in 73.3% of the cases on average."
The same judge model is used to filter CRISP, keeping only plans it labels clear, coherent, concise, and complete, and then to evaluate plan quality in Section 4.4. The fine-tuned planner is trained on the judge-approved plans, so a later preference by the same judge partly measures how well the model reproduces the judge's own filtering criterion, not an independent quality signal. The paper notes the self-preference bias from the judge also being a competitor, but that only makes the observed preference harder; it does not remove the filter-evaluation loop. This loop does not affect the central Tables 2 and 3 results, which are computed on separate benchmarks.
full rationale
The paper's central claim is empirical: LoRA fine-tuning of a small model on CRISP improves plan quality enough to raise downstream scores on MBPP, HumanEval, GSM8K, and MATH relative to CoT and vanilla plan-and-solve. Those benchmark evaluations are independent of the CRISP construction process: the training plans are generated from Magpie-Reasoning-V1-150K, while the downstream accuracy numbers come from standard benchmark test sets, so they do not reduce to the filter by construction. There are no parameter fits that are later renamed as predictions, and no load-bearing self-citation or imported uniqueness theorem. The only self-referential element is that Llama-3.1-70B-Instruct is reused as both the dataset-quality filter and the intrinsic comparative judge; this makes the Section 4.4 quality claim partially circular, but the paper explicitly acknowledges the judge-as-competitor bias and the main results do not depend on that intrinsic evaluation. Overall circularity is therefore minor and confined to the intrinsic evaluation.
Assumptions & free parameters
free parameters (3)
- LoRA hyperparameters =
R=32, alpha=16, dropout=0.05, lr=1e-5, 5 epochs
- Number of sampled solutions for extrinsic filtering =
10
- Plan length constraint =
3-5 steps
assumptions (3)
- domain assumption The LLM-as-judge (Llama-3.1-70B-Instruct) provides valid evaluations of plan clarity, coherence, conciseness, and completeness.
- domain assumption The detailed solutions in Magpie-Reasoning-V1-150K are correct and complete enough to serve as ground truth for plan extraction.
- domain assumption Benchmark performance with a plan prepended to the prompt is a valid extrinsic measure of plan quality.
Cite this review
Pith. "Pith review of CRISP: Complex Reasoning with Interpretable Step-based Plans." pith.science (2026). https://pith.science/paper/AFNTOTDE
@misc{pith2026250708037,
author = {Pith},
title = {Pith review of: CRISP: Complex Reasoning with Interpretable Step-based Plans},
year = {2026},
howpublished = {\url{https://pith.science/paper/AFNTOTDE}},
note = {Machine review of arXiv:2507.08037}
}
read the original abstract
Recent advancements in large language models (LLMs) underscore the need for stronger reasoning capabilities to solve complex problems effectively. While Chain-of-Thought (CoT) reasoning has been a step forward, it remains insufficient for many domains. A promising alternative is explicit high-level plan generation, but existing approaches largely assume that LLMs can produce effective plans through few-shot prompting alone, without additional training. In this work, we challenge this assumption and introduce CRISP (Complex Reasoning with Interpretable Step-based Plans), a multi-domain dataset of high-level plans for mathematical reasoning and code generation. The plans in CRISP are automatically generated and rigorously validated--both intrinsically, using an LLM as a judge, and extrinsically, by evaluating their impact on downstream task performance. We demonstrate that fine-tuning a small model on CRISP enables it to generate higher-quality plans than much larger models using few-shot prompting, while significantly outperforming Chain-of-Thought reasoning. Furthermore, our out-of-domain evaluation reveals that fine-tuning on one domain improves plan generation in the other, highlighting the generalizability of learned planning capabilities.
Figures
Reference graph
Works this paper leans on
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[1]
A **Problem Description** that explains the problem at hand
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Your goal is to produce a short, high-level plan describing how to solve the problem logically
A **Detailed Solution** that fully works out how to solve the problem step-by-step. Your goal is to produce a short, high-level plan describing how to solve the problem logically. This plan must not include any specific equations, function names, or detailed numerical computations. It should be purely indicative and helpful, outlining the logical strategy...
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[3]
Reasoning with large language models, a survey. Preprint, arXiv:2407.11511. Archiki Prasad, Alexander Koller, Mareike Hart- mann, Peter Clark, Ashish Sabharwal, Mohit Bansal, and Tushar Khot. 2024. ADaPT: As- needed decomposition and planning with lan- guage models. InFindings of the Association for Computational Linguistics: NAACL 2024, pages 4226–4252, ...
arXiv 2024
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[4]
Language agent tree search unifies reason- ing acting and planning in language models. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuur- mans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi. 2023. Least-to-most prompting enables complex reasoning in large language mod- els. In The Eleventh International Con...
work page 2023
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Focus on the reasoning steps rather than low-level implementation. **Problem Description** \{problem\_description\} **Detailed Solution** {detailed_solution} **Formatting Requirements**
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Read and understand the **Problem Description} below
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Review the **Detailed Solution** below (do not copy it)
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From these, generate a concise, 3-5 step high-level plan that explains the logical approach needed to solve the problem
Show all 40 references
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[10]
The plan should be abstract and conceptual-avoid quoting or revealing detailed equations, formulas, function names, or code
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[12]
Your final answer should be 3-5 bullet points (or numbered steps)
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Each bullet/step should be brief, logical, and to the point
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Do not include specific equations or code references
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[15]
Do not include extraneous commentary or repeat large sections from the solution
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[16]
**Example Output Structure**
Focus on a clear, conceptual strategy that someone could follow to solve the problem at a high level. **Example Output Structure**
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[17]
Identify the main elements, quantities, or variables in the problem
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Determine the key relationships or principles that connect these elements
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Outline a general strategy for combining or manipulating these elements to get closer to a solution
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Check or validate the approach by ensuring it aligns with the key requirements
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**Few-Shot Examples** **Example 1** **Problem Description** Consider a regular octagon
Summarize the final reasoning step or expected result in broad terms. **Few-Shot Examples** **Example 1** **Problem Description** Consider a regular octagon. How many different triangles can be formed if the octagon is placed inside a circle and we can also use the center of t...
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Recognize the two types of triangles possible: those with only octagon vertices and those that use the center as one vertex
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Conceptually determine how to count each type of triangle without going into specific combinations
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**Example 2** **Problem Description** Write a function that merges two sorted lists into a single sorted list, without using any built-in sorting functions
Combine the counts logically to get the total number of different triangles. **Example 2** **Problem Description** Write a function that merges two sorted lists into a single sorted list, without using any built-in sorting functions. The time complexity should be \(O(n)\), whe...
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Recognize the need to keep track of where we are in each list as we form the new list
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Conceptually compare the front elements from both lists to decide which goes next
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Continue until one list is exhausted, then add any remaining elements from the other
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Now, please provide your high-level plan in 3-5 steps
Return the combined list as the final merged sequence. Now, please provide your high-level plan in 3-5 steps. A.4 LLM-based Judgement Prompt We used the following prompt for judging the four attributes of generated plans with LLM- based judgement. You are an intelligent, knowl...
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**Problem Description:** {problem_description}
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**High-Level Plan:** {high_level_plan}
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**Detailed Solution:** {solution} --- ### **Evaluation Criteria** Assess whether the High-Level Plan sufficiently and logically bridges the Problem Description and the Detailed Solution based on the following four criteria: #### **1. Clarity** - Are the steps written in a way ...
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Reference specific strengths and weaknesses
** `explanation`**: A detailed assessment, addressing how well the plan meets each of the four criteria above. Reference specific strengths and weaknesses
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true"` if the plan **fully satisfies all four criteria**, or `
** `judgement`**: A string set to `"true"` if the plan **fully satisfies all four criteria**, or `"false"` otherwise. --- ### **Strict Output Requirements:** - **Do not** include any extra keys or fields. - **Do not** output any additional text outside the JSON structure. - Th...
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**Clarity** - Are the steps written in a way that is easy to understand? 13 - Does the plan avoid ambiguity and vague language? - Are complex ideas broken down into comprehensible components?
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**Conciseness** - Does the plan avoid unnecessary repetition or overly verbose explanations? - Are the steps described efficiently, without omitting crucial details? - Is there any redundant or overly wordy content that could be simplified?
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**Coherence (Logical Flow & Structure)** - Do the steps follow a clear and logical progression from problem to solution? - Are there any gaps, abrupt transitions, or missing links in the reasoning? - Is the structure intuitive and easy to follow?
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### What to Do
**Completeness** - Are all the essential steps included to fully address the problem and derive the solution? - Does the plan omit any critical information or assume unstated knowledge? - Are there any logical leaps or missing transitions between key points? ### Inputs **Probl...
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Examine each plan in relation to the problem and the solution
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Assess Plan A and Plan B based on the four criteria: Clarity, Conciseness, Coherence, and Completeness
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explanation
Decide whether Plan A is superior, Plan B is superior, or they are equally good overall. ### How to Report Provide your final output as a single JSON object in the exact format below: {{ "explanation": "Explain your comparison referencing each of the four criteria as needed. D...
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[2023]
Boyi Li, Philipp Wu, Pieter Abbeel, and Jitendra Malik
Benchmarking cognitive biases in large language models as evaluators.arXiv preprint arXiv:2309.17012. Boyi Li, Philipp Wu, Pieter Abbeel, and Jitendra Malik. 2023. Interactive task planning with lan- guage models. In 2nd Workshop on Language and Robot Learning: Language as Gro...
2023 arXiv
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[2024]
In The Thirty-eighth Annual Conference on Neural Information Processing Systems
Semcoder: Training code language mod- els with comprehensive semantics reasoning. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. Dheeru Dua, Shivanshu Gupta, Sameer Singh, and Matt Gardner. 2022. Successive prompting for decomposing complex qu...
2022 arXiv
Reviewed August 6, 2026 · model on record in the stance chip above.
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