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This paper claims that large reasoning models can be guided at test time to produce much shorter correct reasoning paths, removing roughly 20–50 percent of tokens with minimal accuracy loss.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review

2026-08-04 23:58 UTC pith:6JXT42M4

load-bearing objection EDIT is a plausible test-time trimming method with real token savings, but its consistency check is a proxy that can lock in confidently wrong short answers; worth a rigorous revision, not a desk reject. the 4 major comments →

arxiv 2509.06174 v1 pith:6JXT42M4 submitted 2025-09-07 cs.AI cs.CL

From Long to Short: LLMs Excel at Trimming Own Reasoning Chains

Wei Han , Geng Zhan , Sicheng Yu , Chenyu Wang , Bryan Hooi This is my paper
classification cs.AI cs.CL
keywords reasoning efficiencytest-time scalinglarge reasoning modelschain-of-thought compressionoverthinkinglength constraintsdual-goal searchmathematical reasoning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Large reasoning models solve hard tasks by emitting long chains of thought, but often keep reasoning long after the answer is found. This paper claims that the same model, without any retraining, can be coaxed into finding a much shorter correct path by repeatedly prompting it with a shrinking step limit and watching what happens. The proposed method, EDIT, tracks whether the model's most confident answer stays the same as the allowed steps shrink, and how the length of the agreeing responses shifts, then returns the shortest answer that passes this consistency check. On three math benchmarks across seven models, EDIT cuts reasoning length by roughly 20 to 50 percent while keeping accuracy within about two points of the best baseline, often matching or slightly exceeding it. If that holds, deployments can reduce latency and cost, and users get reasoning traces that are easier to read.

Core claim

The central claim is that reasoning length is a controllable search dimension at inference time, not a fixed property of the model. The paper reports two statistical observations that set up the method: when prompted with a maximum-step constraint, a reasoning model shifts its output length distribution accordingly, and as the constraint tightens, wrong answers tend to occupy the shorter end of the distribution. EDIT exploits the first effect and guards against the second. It performs a binary search over the step limit, sampling several responses at each setting, computing the most confident answer by voting, and recording a length statistic—the average of the minimum, lower quartile, and m

What carries the argument

Dual-Goal Search: a binary search over a step-count constraint that treats the model as a generator of paired answer-and-length statistics. At each iteration it samples responses under the current constraint, identifies the most confident answer by majority vote, and summarizes the length of agreeing responses as the average of the minimum, lower quartile, and median lengths. The consistency check—same answer plus length adapting to the constraint—decides whether to tighten or relax, and an integer patience parameter prevents a single noisy iteration from ending the search early. This mechanism converts the unmeasurable goal 'find the shortest correct chain' into a measurable proxy: keep the

Load-bearing premise

The load-bearing premise is that when the model gives the same answer while its reasoning shrinks under a tighter step limit, that answer is still correct; EDIT never checks the answer against an external source, so a confidently repeated wrong answer can satisfy the search.

What would settle it

On a held-out subset of a math benchmark with known ground truth, run the consistency check on every question where the model's majority answer is wrong and record how often the check still accepts that wrong answer as the constraint tightens. If accepted-wrong cases are common enough that EDIT's accuracy is no better than free generation, the core assumption fails.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • Reasoning cost can be cut by a fifth to a half at inference time without fine-tuning, simply by searching over step constraints.
  • The step limit itself becomes a deployment dial: operators can trade a small accuracy loss for much shorter outputs, or relax it on hard inputs.
  • Length statistics of responses that agree on an answer give a cheap, verifier-free signal of when the model is still confident.
  • The proposed calibrated length and budget-constrained accuracy metrics give a fairer way to compare reasoning methods that differ in both accuracy and length.
  • The method applies across model families—instruction-tuned, reinforcement-learning-trained, and reasoning-distilled—suggesting the effect is a general property of current large reasoning models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same dual-goal search could transfer to non-math tasks such as code generation or planning, wherever a step or token budget can be phrased as a constraint and answers can be voted on.
  • The consistency check is the fragile part: it assumes answer stability under shortening implies correctness. Pairing it with a light external verifier or process reward would likely close most of the residual accuracy gap.
  • The observed length-collapse behavior could double as a calibration probe: questions where the wrong answer resists shortening are exactly the ones a deployment should mark for human review.
  • A natural extension is input-adaptive budgeting: spend only a few search iterations on easy questions and reserve the full search for hard ones, which would compound the savings.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. This paper studies the overthinking problem in large reasoning models and proposes EDIT, a test-time search method that prompts an LRM with an explicit maximum-step constraint, samples multiple completions at each constraint level, tracks the most confident answer and a length statistic, and runs a patience-based binary search over the constraint to find the shortest response consistent with the model's own repeated answer. Experiments on GSM8K, MATH500, and AIMO across seven models compare EDIT with direct prompting, best-of-N, and self-truncation; the authors report accuracy comparable to the best baselines with substantially shorter outputs. They also introduce calibrated metrics (AEU and budget-constrained accuracy) to evaluate the accuracy-length trade-off.

Significance. If the claims hold, this is a useful training-free method for reducing reasoning tokens by roughly 20-50% while retaining accuracy, and the AEU/BCA evaluation framework is a practical contribution. The paper is generally honest in reporting cases where EDIT trades accuracy for length, and the ablations explore sensitivity. However, the central claim that EDIT identifies 'the shortest correct reasoning paths' at test time is not established: the search's correctness signal is the model's own consistency rather than an external verifier, and the paper's own Figure 1b documents a failure regime where incorrect answers concentrate at short lengths. The aggregate results are suggestive but require significance testing and a more precise algorithm specification.

major comments (4)
  1. [§3.3, Algorithm 1] Check_Consistency is never defined. The prose gives qualitative conditions ('answers from the two iterations are same' and 'length metric adapts') but no formal predicate; line 9 queries H at t=1 when H is empty, and line 24 returns most_confident(H). Since every tighten/relax decision depends on this predicate, specify it exactly, including how 'length adapts' is computed and what tolerance is used. This is load-bearing because without an external verifier, a model that repeats the same wrong answer under a tighter constraint passes the check; Figure 1b shows that incorrect answers concentrate at short lengths in exactly the regime EDIT searches. Report how often this failure occurs, e.g., by comparing EDIT's returned answers with ground truth in an error analysis, or by adding a verifier-based ablation.
  2. [§4.3, Table 2] Table 2 is said to be averaged over five runs, but no variance or significance is reported. Many accuracy differences are within ±1 point, e.g., Llama-3.1-8B on MATH: 52.4 vs 52.6 for BoN; Qwen3-8B on MATH: 73.2 vs 74.2 for ST. Without confidence intervals or paired tests, the claims that EDIT 'preserves accuracy to the maximum extent' and 'nearly achieves or surpasses the best baseline' are not supported. Report per-run standard deviations or paired bootstrap intervals for at least the main table.
  3. [§4.5, Table 4] The ablation shows the default configuration is not the best on Llama-3.1-8B: max_iter=4 gives 61.8 accuracy vs 52.4 for the default max_iter=6, with length 162 vs 108. The text explains this as premature termination, but with no validation-based selection the default hyperparameters appear arbitrary, and the headline numbers may depend on that choice. Justify max_iter=6 and beta0=1 (e.g., via a validation split), or update the default and report all configurations.
  4. [§4.4, Eq. (5)] The calibrated length lpen and AEU depend on gamma, but gamma is never specified. Since Table 3 is the main evidence for efficiency under the accuracy/length trade-off, report the value of gamma and its sensitivity. Also clarify the max{l_wrong} term: maximum over which set, and what happens for a question where no wrong sample exists.
minor comments (6)
  1. [§3.1, Eq. (2)] The notation \vec{r_i}=a_i appears to be a typo for \phi(r_i)=a_i; please fix.
  2. [§3.3, Algorithm 1] The pseudocode mixes notations: line 12 uses \hat{a}_i while the loop variable is \hat{a}_t. Also line 9 consults an empty H on the first iteration; define the behavior of Check_Consistency for an empty history.
  3. [§4.3] The claim that 'the largest performance gap is below relatively 2%' is not consistent with Table 2, which shows a 2.3-point drop and a 3.3-point gain. Specify whether these are absolute or relative percentage differences.
  4. [§4.3, Figure 3] Figure 3 averages over six models but shows no error bars or per-model spread; consider adding confidence bands.
  5. [Throughout] There are several typos and inconsistent spellings: 'delibrate' (Section 2), 'straingent' (Section 3.2), 'Regards' (Section 2), and inconsistent 'LLaMA'/'Llama'. Please copyedit.
  6. [Limitations] The Limitations section only discusses system-level efficiency and does not mention the absence of an external verifier or the lack of statistical significance testing. Both are relevant to interpreting the method's guarantees and should be acknowledged.

Circularity Check

0 steps flagged

No significant circularity: core results are externally benchmarked; the unverified consistency proxy is a correctness risk, not a circular derivation.

full rationale

The paper's central accuracy and length results (Table 2) are measured against external ground-truth labels on GSM8K, MATH500, and AIMO, with matching sampling budgets and decoding settings across baselines. Algorithm 1's Check_Consistency uses the model's own answer repetition and the lstat length statistic as a test-time proxy for correctness, but this is an unsupported heuristic and a robustness limitation, not a circular step: the reported accuracy is not defined as that proxy, and the paper's claim is not inferred from a metric that already encodes the conclusion. The AEU and BCA metrics in Section 4.4 are aligned with EDIT's own objective (short and correct), but they are auxiliary evaluation devices; the raw Acc/Length columns stand independently and do not rely on those calibrated metrics. No load-bearing self-citations, imported uniqueness theorems, or ansatz-smuggling citations appear. The absence of an external verifier and the imprecise length-adaptation criterion are legitimate correctness concerns, but they do not make the derivation equivalent to its inputs by construction.

Axiom & Free-Parameter Ledger

5 free parameters · 4 axioms · 0 invented entities

The method leans on the model's own confidence and length statistics rather than an external verifier. The main free parameters are the patience, iteration count, constraint range, and the penalty factor in the efficiency metric, several of which are not fully specified in the paper.

free parameters (5)
  • patience beta0 = 1 (default)
    Initial patience in Dual-Goal Search; ablated in Table 4, with beta0=0 and beta0=2 both changing results.
  • max iterations T = 6 (default)
    Number of search iterations; total sampling budget N=64 split across T. Ablation shows max_iter=4 yields higher accuracy on Llama-3.1-8B (61.8 vs 52.4) than default.
  • constraint range for num_step = not specified
    The values of tau_max and tau_min for the step-count constraint are never given in the paper, only examples in Figure 1 (2, 8, 12).
  • penalty factor gamma = not specified
    In Eq. 5, gamma > 1 weights wrong-answer length cost, but no value is reported, making Table 3 irreproducible.
  • length statistic lstat weights = average of min, Q1, median
    Eq. 4; ablated in Table 4, changing to median-only or min-only degrades results.
axioms (4)
  • domain assumption Majority-vote confidence of LRM outputs correlates with correctness
    Algorithm 1 uses answer_conf to select the most confident answer and trusts it as the search target.
  • domain assumption Step-count constraints monotonically shift the length distribution
    Section 3.2, Figure 1a; the search assumes tighter constraints reliably produce shorter outputs.
  • ad hoc to paper Consistency of same answer plus shrinking length implies correctness
    Algorithm 1 Check_Consistency; no external verifier is used, and the paper does not define the length-adaptation criterion precisely.
  • domain assumption Ground truth used only for final evaluation, not for search
    Eq. 1 measures accuracy against labels, but the search itself never consults labels, so correctness of the selected answer is assumed from self-consistency.

reviewed 2026-08-04 · how reviews work

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Cite this review

Pith. "Pith review of From Long to Short: LLMs Excel at Trimming Own Reasoning Chains." pith.science (2026). https://pith.science/paper/6JXT42M4

@misc{pith2026250906174,
  author       = {Pith},
  title        = {Pith review of: From Long to Short: LLMs Excel at Trimming Own Reasoning Chains},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6JXT42M4}},
  note         = {Machine review of arXiv:2509.06174}
}
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read the original abstract

O1/R1 style large reasoning models (LRMs) signal a substantial leap forward over conventional instruction-following LLMs. By applying test-time scaling to generate extended reasoning paths, they establish many SOTAs across a wide range of complex reasoning tasks. However, recent studies show that LRMs are prone to suffer from overthinking -- the tendency to overcomplicate simple problems, leading to excessive strategy switching and long, convoluted reasoning traces that hinder their interpretability. To mitigate this issue, we conduct a systematic investigation into the reasoning efficiency of a broad set of LRMs and uncover a common dilemma: the difficulty in balancing multiple generation objectives such as correctness and brevity. Based on this discovery, we propose a test-time scaling method, EDIT (Efficient Dynamic Inference Trimming), which efficiently guides LRMs to identify the shortest correct reasoning paths at test time. EDIT employs constraint-guided generation while jointly tracking length and answer distributions under varying constraints, allowing it to select responses that strike an optimal balance between conciseness and correctness. Extensive experiments across diverse models and datasets show that EDIT substantially enhance the reasoning efficiency, producing compact yet informative outputs that improve readability and user experience.

Figures

Figures reproduced from arXiv: 2509.06174 by Bryan Hooi, Chenyu Wang, Geng Zhan, Sicheng Yu, Wei Han.

Figure 1
Figure 1. Figure 1: Histogram of length distributions on MATH500 dataset with LLaMA-3.1-8B: the generation [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Two running examples of the EDIT framework, with and without patience respectively. Dark red and green arrows between search steps represent the constraint is tighter or relaxed. In figure (a), the consistency checking fails due to running-out of patience. The next step continues with a looser constraint and a sub-optimal response is returned. A higher value of initial patience in figure (b) and a better r… view at source ↗
Figure 3
Figure 3. Figure 3: Accuracy variation under different token budget for three methods on three datasets. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Length distribution for correct and incorrect responses from three methods on GSM8K. [PITH_FULL_IMAGE:figures/full_fig_p017_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Length distribution for correct and incorrect responses from three methods on MATH. [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗

discussion (0)

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This paper was first reviewed by deepseek-v4-flash on August 4, 2026.