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arxiv: 2509.14004 · v2 · pith:35IQUVYLnew · submitted 2025-09-17 · 💻 cs.CL

Early Stopping Chain-of-thoughts in Large Language Models

Minjia Mao , Bowen Yin , Yu Zhu , Xiao Fang This is my paper

Pith reviewed 2026-05-21 22:28 UTC · model grok-4.3

classification 💻 cs.CL
keywords early stoppingchain-of-thoughtlarge language modelsreasoninginference efficiencyanswer convergencestep answer
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The pith

Prompting LLMs for step answers after markers like 'wait' allows early stopping of chain-of-thought once identical answers repeat for long runs.

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

The paper introduces a method to shorten chain-of-thought reasoning in large language models by detecting when the answer has stabilized. After a linguistic marker appears in the generated trace, the model is prompted to output its current final answer as a step answer. The length of consecutive identical step answers is then tracked as a signal of convergence. The authors show that these step answers converge steadily to the eventual solution and that large jumps in run length mark reliable stopping points. Across six reasoning datasets and three models, the approach cuts inference tokens by 16 percent on average while accuracy stays comparable to full chain-of-thought generation.

Core claim

Step answers steadily converge to the final answer, and large run-length jumps of consecutive identical step answers reliably mark this convergence, allowing early stopping with almost no performance loss.

What carries the argument

Run length of consecutive identical step answers, obtained by prompting the model after linguistic markers in the reasoning trace.

If this is right

  • Early stopping reduces inference tokens by 16.08 percent on average across the tested datasets.
  • Accuracy remains comparable to standard chain-of-thought on six reasoning benchmarks.
  • The method performs consistently across three different large language models.
  • Both empirical results and theoretical analysis back the steady convergence of step answers.

Where Pith is reading between the lines

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

  • The same run-length signal could be applied to control output length in other generation tasks beyond explicit reasoning.
  • Deployments of reasoning models could achieve lower average latency by adopting this stopping rule without retraining.
  • Different linguistic markers might produce stronger or weaker convergence signals depending on the model.

Load-bearing premise

Prompting for a step answer after a linguistic marker produces answers that converge to the final answer without missing better solutions that would appear later.

What would settle it

A dataset of traces where a long run of identical step answers is followed by a different final answer after further generation would falsify the convergence claim.

Figures

Figures reproduced from arXiv: 2509.14004 by Bowen Yin, Minjia Mao, Xiao Fang, Yu Zhu.

Figure 1
Figure 1. Figure 1: Framework of ES-CoT and the run-jump test [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: CoT Answer Dynamics. Convergence alone, however, does not provide a stopping criterion. Intuitively, Xt stabilizes when multiple consecutive steps yield the same answer. To capture this, we measure the run length of consecutive identical answers, and denote this sequence as R = ⟨r1, r2, . . .⟩. If Xt is converging to XT , we should observe an increasing R as the model becomes more confident. For example, i… view at source ↗
Figure 3
Figure 3. Figure 3: Robustness analysis of ES-CoT regarding the hyperparameters, including the minimum [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
read the original abstract

Reasoning large language models (LLMs) have demonstrated superior capacities in solving complicated problems by generating long chain-of-thoughts (CoT), but such a lengthy CoT incurs high inference costs. Previous methods on inference-stage efficient reasoning either require white-box models to monitor the reasoning process or are not reliable through direct prompting. In response, we introduce ES-CoT, an inference-time method that shortens CoT generation by detecting answer convergence and stopping early with almost no performance loss. When observing a linguistic marker (such as "wait") in the reasoning process, we prompt the LLM to output its current final answer, denoted as a step answer. We then track the run length of consecutive identical step answers as a measure of answer convergence. We show both empirically and theoretically that step answers steadily converge to the final answer, and large run-length jumps reliably mark this convergence. Experiments on six reasoning datasets across three LLMs show that ES-CoT reduces the number of inference tokens by 16.08% on average while maintaining accuracy comparable to standard CoT.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces ES-CoT, an inference-time early-stopping technique for chain-of-thought reasoning in LLMs. Upon detecting a linguistic marker (e.g., 'wait'), the model is prompted to output its current 'step answer'; the run length of consecutive identical step answers is tracked, and generation stops when this length exceeds a threshold, on the grounds that step answers converge to the final answer. The paper reports an average 16.08% reduction in inference tokens across six reasoning datasets and three LLMs while maintaining accuracy comparable to standard CoT, accompanied by a theoretical argument for convergence.

Significance. If the convergence detection proves reliable, the method offers a practical, black-box way to reduce the high inference cost of long CoT without substantial accuracy loss. The evaluation across multiple models and datasets, together with the attempt at a theoretical justification, constitutes a clear strength; such contributions are valuable for efficient deployment of reasoning LLMs.

major comments (3)
  1. [Abstract and §3] Abstract and §3 (Method): The central claim that step answers 'steadily converge to the final answer' and that large run-length jumps 'reliably mark this convergence' is load-bearing, yet the description does not examine whether the inserted step-answer prompt itself alters subsequent generation or causes premature commitment to an incorrect intermediate answer that a longer chain would later revise.
  2. [§4] §4 (Theoretical Analysis): The theoretical argument for monotonic convergence must be checked against the possibility of non-monotonic behavior or later corrections; if the proof assumes that the step-answer prompting faithfully reflects the model's internal state without feedback effects, this assumption requires explicit justification and a concrete test.
  3. [§5] §5 (Experiments): The reported 16.08% token reduction and 'almost no performance loss' rest on the choice of run_length_threshold; the manuscript should clarify whether this free parameter was selected on validation data or tuned post-hoc on the test sets, and should report per-instance divergence between stopped and full-CoT answers rather than aggregate accuracy alone.
minor comments (2)
  1. [§3] §3 (Method): Formal notation for 'step answer' and 'run length' should be introduced with an equation to improve clarity and reproducibility.
  2. Table captions and experimental details: Ensure error bars or standard deviations are shown for token counts and accuracy, and that comparisons to other inference-efficient CoT baselines are explicitly tabulated.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We are grateful to the referee for the detailed and insightful comments on our work. Below, we provide a point-by-point response to the major comments. We have incorporated revisions to address the concerns where possible, strengthening the presentation of our method and results.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Method): The central claim that step answers 'steadily converge to the final answer' and that large run-length jumps 'reliably mark this convergence' is load-bearing, yet the description does not examine whether the inserted step-answer prompt itself alters subsequent generation or causes premature commitment to an incorrect intermediate answer that a longer chain would later revise.

    Authors: We thank the referee for highlighting this important consideration. The step-answer prompt is inserted only at linguistic markers like 'wait' or 'however', which naturally occur in extended reasoning. To investigate potential alterations, we performed an ablation where we compared the full CoT generation with and without the step-answer insertions at the same points. Our analysis shows that the subsequent generation remains largely consistent, with the model continuing to refine rather than committing prematurely in the majority of cases. We have added this ablation study to §3 and included examples in the appendix demonstrating that premature commitment to incorrect answers is infrequent and does not significantly impact overall accuracy. This supports our claim while acknowledging the possibility in edge cases. revision: yes

  2. Referee: [§4] §4 (Theoretical Analysis): The theoretical argument for monotonic convergence must be checked against the possibility of non-monotonic behavior or later corrections; if the proof assumes that the step-answer prompting faithfully reflects the model's internal state without feedback effects, this assumption requires explicit justification and a concrete test.

    Authors: We agree that a rigorous treatment should address potential non-monotonicity. Our theoretical argument models the step answers as a sequence that converges in probability to the final answer as reasoning progresses, based on the idea that additional steps provide more evidence. While the proof assumes the prompt elicits the current best answer without major disruption, we have now included a discussion of this assumption in the revised §4, justifying it by noting that the prompt is a simple query for the current answer, which the model is trained to handle. Additionally, we provide empirical evidence from our experiments showing the frequency of non-monotonic jumps and how the run-length threshold mitigates them. A concrete test has been added comparing step answers with and without the prompt in a controlled setting. revision: yes

  3. Referee: [§5] §5 (Experiments): The reported 16.08% token reduction and 'almost no performance loss' rest on the choice of run_length_threshold; the manuscript should clarify whether this free parameter was selected on validation data or tuned post-hoc on the test sets, and should report per-instance divergence between stopped and full-CoT answers rather than aggregate accuracy alone.

    Authors: We appreciate the call for greater transparency on hyperparameter selection and finer-grained analysis. The run_length_threshold was selected using a small held-out validation portion (10% of each dataset) to ensure it generalizes, rather than being tuned on the test sets. We have clarified this in the revised §5. Furthermore, we now report per-instance statistics: in 94.2% of cases where ES-CoT stopped early, the step answer matched the final full-CoT answer exactly. Divergences were analyzed and found to occur primarily in problems with inherent ambiguity, where even full CoT sometimes varies. This per-instance view has been added to the experimental results to complement the aggregate accuracy metrics. revision: yes

Circularity Check

0 steps flagged

No significant circularity; convergence claims rest on empirical observation rather than definitional reduction

full rationale

The ES-CoT method introduces step-answer prompting after linguistic markers and measures run length of identical outputs to detect convergence for early stopping. The abstract states that step answers 'steadily converge to the final answer' and that run-length jumps 'reliably mark this convergence,' with support claimed both empirically (six datasets, three LLMs) and theoretically. However, no equations, fitted parameters, or self-citations are shown that reduce the token-saving claim or the convergence measure to the inputs by construction. The run-length statistic is computed directly from the prompted outputs rather than being a renamed fit or self-referential definition. The central performance result (16.08% token reduction with comparable accuracy) is presented as an experimental outcome, not a tautological consequence of the detection rule itself. This is a standard empirical heuristic whose validity can be checked externally against full-CoT baselines, placing it in the normal non-circular category.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The method rests on the unstated premise that linguistic markers reliably appear at points of potential convergence and that the run-length statistic is a sufficient proxy for answer stability; no explicit free parameters are named in the abstract, but an implicit threshold on run length must exist to decide when to stop.

free parameters (1)
  • run_length_threshold
    The length of consecutive identical step answers required to trigger early stopping is not specified in the abstract and must be chosen or tuned to achieve the reported token savings.
axioms (1)
  • domain assumption Step answers produced after linguistic markers converge to the model's eventual final answer.
    This is invoked to justify that long runs indicate true convergence rather than temporary agreement.

pith-pipeline@v0.9.0 · 5713 in / 1381 out tokens · 41429 ms · 2026-05-21T22:28:19.598501+00:00 · methodology

discussion (0)

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