REVIEW 3 major objections 6 minor 31 references
Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving
T0 review · 3 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash
Pith's one-line read Training an LLM to write a high-level abstract plan before producing a step-by-step answer improves self-training accuracy on four reasoning benchmarks.
desk verdict Useful self-training paper with a load-bearing but unverified no-leakage assumption; worth serious review with a required audit. 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 the anticipatory plan: a short, self-generated, abstract blueprint that states the high-level steps for solving a class of problems without containing problem-specific numbers, calculations, or the answer. It carries the argument because it is the only channel through which meta-knowledge enters the training data. LEPA optimizes plans by self-reflection: when a plan leads to an incorrect solution, the model is shown the problem, the plan, the wrong solution, and the correct answer, and is instructed to produce a new plan that avoids the failure while remaining general; the revised plan is then tested by solving the problem again. During supervised fine-tuning, the model is trained to produce the refined plan in one turn and the solution in the next, so planning and answering are learned as one skill.
What would settle it
Inspect the stored plans from LEPA's data-generation phase and count how many contain tokens that also appear in the problem's key numbers or in the correct answer (for instance, the '45' or '9' in the meerkat example). If a non-trivial share of the plans carry such problem-specific content, the claim that the plans are abstract meta-knowledge fails, because the accuracy gain could come from answer leakage rather than transferable planning.
Extended reading notes
Core claim
The paper's central claim is that training an LLM to emit an abstract anticipatory plan before a detailed solution makes self-training more effective than training it on solutions alone. The plan is meant to be general problem-solving meta-knowledge, applicable to similar problems, so the solution must be produced from the plan plus the problem without seeing the answer. LEPA's data-generation loop generates a plan, solves under it, and if the solution is wrong, reflects on the failure and revises the plan; only plan-solution pairs that yield correct answers enter the supervised fine-tuning set. The paper reports that on four reasoning benchmarks LEPA outperforms prior self-training algorithms consistently, including a 30.2% accuracy on MATH versus 28.2% for the best baseline.
Load-bearing premise
Everything depends on the refined plans being genuinely free of problem-specific information: the reflection prompt tells the model not to include the correct answer or detailed calculations, but the paper never checks whether the model obeys.
Editorial extensions
If this is right
- On all four benchmarks LEPA's converged accuracy exceeds the best baseline, so a model trained this way would be expected to answer more test items correctly: 91.2% on HellaSwag, 30.2% on MATH, 88.4% on BoolQ, and 85.9% on PIQA.
- The MATH-to-MMLU-Pro transfer result (38.9% versus 35.8%) implies the learned plans carry over to a different math benchmark, not just to held-out items from the same dataset.
- The ablations imply that the plan, not just the extra inference tokens, drives the gain: removing the plan drops MATH accuracy by 5.9 points, while replacing self-reflection with rejection sampling drops it by 1.4 points.
- LEPA's compatibility with REINFORCE (30.6% versus 30.2% on MATH) implies the planning objective can be plugged into policy-gradient training rather than only supervised fine-tuning.
Reading between the lines
- My inference: if plans are truly abstract, they should be reusable as few-shot exemplars or as a transferable prompt library across benchmarks, a use LEPA does not itself demonstrate.
- My inference: the claimed advantage over answer-augmented self-training would be sharpest to test on a benchmark where answers are easy to memorize, since leakage would show up as large gains on shallow surface similarity.
- My inference: an automatic abstraction score computed on plans could be used to filter low-quality plans during data generation, which LEPA currently leaves to correctness of the final answer.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes LEPA (LEarning to Plan before Answering), a self-training algorithm in which an LLM first generates an abstract anticipatory plan, then generates a solution guided by that plan, and refines the plan through self-reflection when the solution is incorrect. The refined plan and correct solution are then used as supervised fine-tuning data. The authors claim that LEPA captures transferable high-level meta-knowledge and avoids the "information bypassing" pathology of STaR, where models may copy the correct answer without learning valid reasoning. Experiments on Hendrycks MATH, HellaSwag, BoolQ, and PIQA with Llama 3 8B Instruct report consistent improvements over ReST, ReST EM, and STaR, with an average gain of 3.1 points over the best baseline. Additional ablations address the contribution of the plan, self-reflection, and inference-compute allocation, and appendix results include an OOD evaluation on MMLU-Pro, a second base model, additional benchmarks, and a re-evaluation with Simple-Eval.
Significance. If the reported results hold, LEPA is a useful contribution to LLM self-training: it introduces a simple, clearly described mechanism for learning abstract plans, and the empirical picture is unusually complete for a methods paper, including ablations, OOD generalization, an alternative base model, and an independent evaluation harness. The claimed advantages are concrete and falsifiable. However, the central mechanism depends on the assumption that refined plans are genuinely answer-free and problem-agnostic, and the manuscript provides only prompt-level assurances, not empirical verification. Because the improvement over answer-augmented self-training (STaR) and over solution-only self-training rests on this assumption, the missing audit is load-bearing. The absence of variance estimates also makes the reported margins difficult to interpret. These are fixable within the manuscript's scope, so the paper merits major revision rather than rejection.
major comments (3)
- [Section 2.1 and Appendix A] The claim that refined plans are free of problem-specific information is load-bearing but never verified. Section 2.1 states that the reflection prompt receives the correct answer and simply 'stresses' that the reflected plan should not contain problem-specific information, including detailed calculations and the correct answer. Appendix A's new-plan prompt repeats this as an instruction ('You must not contain the correct final answer in the plan'). The paper reports no audit of generated plans, no automatic detection of leaked numeric answers or option text, and no control condition in which the correct answer is withheld during reflection. If a meaningful fraction of refined plans leak the answer, the training set contains exactly the answer-conditioned pattern the paper criticizes in STaR, and the reported margins (e.g., +2.0 on MATH, +3.1 average) could reflect leakage rather than transferable plans. The OOD MMLU-Pro result mitigates generalization concerns but does not verify the stated mechanism. Please add (i) a quantitative compliance analysis over generated plans and (ii) an ablation or control that removes the correct answer from the reflection prompt or otherwise detects leakage.
- [Section 3.1, Table 1] All accuracy numbers are single-run point estimates with no error bars, confidence intervals, or significance tests. The improvements over the best baseline are 1.7 to 4.8 points per benchmark, and without variance estimates it is unclear whether these differences are stable across seeds and data-generation randomness. Please report results over multiple seeds (or bootstrap intervals) and state explicitly whether the same test split is used across all methods.
- [Section 3.1, footnote 1] The evaluation protocol is asymmetric: STaR is reported at its highest test accuracy because its accuracy drops on MATH, while all other methods are reported at convergence. This makes the magnitude of LEPA's advantage over STaR difficult to interpret. Please report all methods under the same checkpoint-selection rule, or present both convergence and best-checkpoint numbers for every method.
minor comments (6)
- [Section 2.3] The heading 'Learning generalizable high-level meta-knowledge' appears twice; the second occurrence should be titled something like 'Avoiding information bypassing in self-reflection' to match its content.
- [Section 2.2 and Section 3.2] There are typos: 'LPEA' instead of 'LEPA' in Section 2.2, and 'hiders' instead of 'hinders' in Section 3.2. Also, the method name is inconsistently written as 'ReSTEM' and 'ReST EM'; please choose one form.
- [Appendix A] The self-reflection prompt asks 'What contents should the new plan obtain?' which appears to be a typo for 'contain'; please correct it and check the prompt text for similar errors.
- [Figure 3] The learning curves are presented without error bars or a description of how many seeds they average over; please add this information or explicitly state that the curves are single runs.
- [Table 3] The table is split across two rows because of page width; please reformat it so that the method names and their token counts/accuracies are aligned in a single row, or use a more compact layout.
- [Appendix C] The additional-base-model result on Llama 3.1 8B is reported only on Hendrycks MATH; please clarify whether the full main benchmark suite was run on that model, or state explicitly that only MATH was used.
Circularity Check
No significant circularity: LEPA's central claims are evaluated on external benchmarks, and the plan-leakage concern is an empirical validity issue rather than a derivation-level circularity.
full rationale
LEPA's derivation chain is a standard self-training pipeline: generate plan-solution pairs, filter or refine with self-reflection, and fine-tune with the SFT loss in Eq. (1) over the generated pairs. There are no fitted constants whose value is renamed as a prediction, and no equation in which the claimed output is identical to an input by construction. The main claim, that training on anticipatory plans improves test accuracy, is checked against held-out external benchmarks (Hendrycks MATH, HellaSwag, BoolQ, PIQA, plus MMLU-Pro, CSQA, and Simple-Eval in the appendix), so the central result is not equivalent to its inputs. The one notable concern is the answer-leakage assumption: the self-reflection prompt is given the correct answer and Appendix A's new-plan prompt instructs the model that it 'must not contain the correct final answer in the plan,' while Section 2.3 asserts that LEPA thereby 'isolates the correct answer from solution generation.' Whether the generated plans actually comply is never audited. This is a load-bearing empirical assumption about leakage control, and it could affect the interpretation of the reported gains, but it is not circular: the plan is not defined in terms of the answer, the training loss does not force the plan to equal the answer, and the benchmark results are external. The paper's only self-citations are prior meta-RL works by overlapping authors used as background in Related Work, not as the load-bearing justification for LEPA's mechanism. The ethics statement notes that LEPA 'cannot guarantee that every solution will strictly match the plans it creates,' which is a limitation but not a circular step. Overall, no circular step can be exhibited with the required quote-and-reduction evidence, so the circularity score is low.
Assumptions & free parameters
assumptions (3)
- domain assumption The base LLM can generate and follow abstract plans that omit problem-specific details.
- domain assumption Prompt wording alone prevents plan leakage of the correct answer or calculations.
- domain assumption The binary scoring function f_cor correctly determines solution correctness on each benchmark.
Cite this review
Pith. "Pith review of Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving." pith.science (2026). https://pith.science/paper/ETKEL4SO
@misc{pith2026250500031,
author = {Pith},
title = {Pith review of: Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving},
year = {2026},
howpublished = {\url{https://pith.science/paper/ETKEL4SO}},
note = {Machine review of arXiv:2505.00031}
}
read the original abstract
In the field of large language model (LLM) post-training, the effectiveness of utilizing synthetic data generated by the LLM itself has been well-presented. However, a key question remains unaddressed: what essential information should such self-generated data encapsulate? Existing approaches only produce step-by-step problem solutions, and fail to capture the abstract meta-knowledge necessary for generalization across similar problems. Drawing insights from cognitive science, where humans employ high-level abstraction to simplify complex problems before delving into specifics, we introduce a novel self-training algorithm: LEarning to Plan before Answering (LEPA). LEPA trains the LLM to formulate anticipatory plans, which serve as abstract meta-knowledge for problem-solving, before engaging with the intricacies of problems. This approach not only outlines the solution generation path but also shields the LLM from the distraction of irrelevant details. During data generation, LEPA first crafts an anticipatory plan based on the problem, and then generates a solution that aligns with both the plan and the problem. LEPA refines the plan through self-reflection, aiming to acquire plans that are instrumental in yielding correct solutions. During model optimization, the LLM is trained to predict both the refined plans and the corresponding solutions. By efficiently extracting and utilizing the anticipatory plans, LEPA demonstrates remarkable superiority over conventional algorithms on various challenging natural language reasoning benchmarks.
Figures
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Reference graph
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