arxiv: 2604.04930 · v1 · submitted 2026-04-06 · 💻 cs.CL · cs.AI· cs.LG

Recognition: 2 theorem links

· Lean Theorem

Early Stopping for Large Reasoning Models via Confidence Dynamics

Parsa Hosseini , Sumit Nawathe , Mahdi Salmani , Meisam Razaviyayn , Soheil Feizi

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:45 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG

keywords early stoppingchain-of-thought reasoningconfidence dynamicslarge reasoning modelstoken efficiencyoverthinking

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The pith

CoDE-Stop halts chain-of-thought reasoning when intermediate answer confidence stabilizes, cutting token use by 25-50%.

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

Large reasoning models use extended chain-of-thought traces that raise compute costs and sometimes hurt accuracy through overthinking. The work identifies a repeatable pattern: correct trajectories tend to converge on high-confidence answers relatively soon, while incorrect ones generate lengthy, low-value traces with erratic confidence shifts. CoDE-Stop is introduced as a lightweight, training-free rule that watches these confidence dynamics and terminates generation once stability is reached. Tests across reasoning and science benchmarks show the method improves the accuracy-compute curve relative to full-length generation and earlier stopping baselines. The paper additionally maps how confidence trajectories diverge between successful and failed reasoning paths.

Core claim

Correct reasoning trajectories reach high-confidence answers early, while incorrect rollouts produce long, unproductive traces and less reliable confidence dynamics. CoDE-Stop exploits this distinction by terminating generation once confidence dynamics indicate a stable answer has been reached, without any model retraining.

What carries the argument

CoDE-Stop, an early-stopping rule that monitors the level and stability of confidence scores attached to intermediate answers generated during chain-of-thought reasoning.

If this is right

Total token consumption drops 25-50% versus standard full-length reasoning.
Accuracy-compute tradeoff improves over prior early-stopping techniques.
The rule integrates into existing models with no extra training or post-processing.
Analyses reveal systematic differences in how confidence evolves along correct versus incorrect paths.

Where Pith is reading between the lines

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

The same confidence-stability signal could be tested for early termination in non-reasoning generation tasks such as long-form writing or multi-step planning.
If the pattern holds, internal confidence dynamics might serve as a lightweight self-verification signal that reduces reliance on external reward models or verifiers.
The approach invites experiments that combine confidence-based stopping with other efficiency methods like speculative decoding or dynamic context pruning.

Load-bearing premise

The observed split between quick high-confidence convergence on correct paths and unreliable dynamics on incorrect paths generalizes across models, tasks, and prompt styles without per-model tuning.

What would settle it

Finding a model-task pair in which an incorrect trajectory reaches and sustains high stable confidence early, or a correct trajectory keeps fluctuating in confidence, would break the stopping criterion.

Figures

Figures reproduced from arXiv: 2604.04930 by Mahdi Salmani, Meisam Razaviyayn, Parsa Hosseini, Soheil Feizi, Sumit Nawathe.

**Figure 2.** Figure 2: Confidence dynamics across reasoning trajectories. Correct trajectories reach high confidence early, while incorrect trajectories exhibit unstable and fluctuating confidence. dynamics of confidence and show that correct and incorrect trajectories exhibit distinct patterns that can be leveraged for early stopping. We first examine trajectories that lead to correct answers [PITH_FULL_IMAGE:figures/full_fig_… view at source ↗

**Figure 3.** Figure 3: Incorrect trajectories are longer and exhibit a heavy-tailed distribution. We next examine trajectories that lead to incorrect answers. We find that correct trajectories require on average 12K tokens, whereas incorrect trajectories extend to more than 25K tokens, doubling the total computation. Figure 3 shows that a similar pattern holds in terms of reasoning steps: incorrect trajectories are substantia… view at source ↗

**Figure 4.** Figure 4: Confidence and degeneration dynamics over reasoning steps. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

**Figure 5.** Figure 5: Performance of CoDE-Stop against different baselines across multiple benchmarks. CoDE-Stop consistently reduces inference cost while maintaining comparable performance to the baselines, making it Pareto optimal. which assigns higher importance to earlier reasoning steps, consistent with our observation that early-stage signals are more informative. The logarithmic form provides a smooth growth over time w… view at source ↗

**Figure 6.** Figure 6: Performance of CoDE-Stop with different prompting baselines. CoDE-Stop can be combined with various prompting strategies to further improve performance. Budget Forcing, Chain-of-Draft (CoD) (Xu et al., 2025a), and NoThinking (Ma et al., 2025). We provide the exact prompts used for each method in the Appendix A. Evaluation Setup. For each prompt, we generate multiple reasoning trajectories per question: 15… view at source ↗

**Figure 7.** Figure 7: CoDE-Stop reduces unnecessary computation on incorrect rollouts compared to baselines at matched accuracy. To evaluate this, we compare three stopping strategies in [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗

**Figure 8.** Figure 8: Ablation of degeneration score functions: vi (left) and wi (right) [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗

**Figure 10.** Figure 10: Qualitative Example 1. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗

**Figure 11.** Figure 11: Qualitative Example 2. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗

read the original abstract

Large reasoning models rely on long chain-of-thought generation to solve complex problems, but extended reasoning often incurs substantial computational cost and can even degrade performance due to overthinking. A key challenge is determining when the model should stop reasoning and produce the final answer. In this work, we study the confidence of intermediate answers during reasoning and observe two characteristic behaviors: correct reasoning trajectories often reach high-confidence answers early, while incorrect rollouts tend to produce long, unproductive reasoning traces and exhibit less reliable confidence dynamics. Motivated by these observations, we propose CoDE-Stop (Confidence Dynamics Early Stop), an early stopping method that leverages the dynamics of intermediate answer confidence to decide when to terminate reasoning, requiring no additional training and easily integrating into existing models. We evaluate CoDE-Stop on diverse reasoning and science benchmarks across multiple models. Compared to prior early stopping methods, it achieves a more favorable accuracy-compute tradeoff and reduces total token usage by 25-50% compared to standard full-length reasoning. In addition, we provide analyses of confidence dynamics during reasoning, offering insights into how confidence changes in both correct and incorrect trajectories.

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.

Desk Editor's Note private letter to a colleague

CoDE-Stop offers a training-free early-stop rule that watches confidence in intermediate answers to trim 25-50% tokens on reasoning traces, but the gains depend on those patterns holding without per-model tweaks.

read the letter

The paper's main contribution is CoDE-Stop, a simple rule that terminates chain-of-thought once intermediate answer confidence shows stable high values. Correct traces tend to hit this point early; incorrect ones keep fluctuating and stay long. No extra training is needed, and it slots into existing models directly. The abstract shows results across several reasoning and science benchmarks plus multiple models, with better accuracy-compute curves than prior early-stop baselines and the stated token savings versus full-length generation. The trajectory analyses give a clear picture of how confidence behaves in right versus wrong paths, which is useful for anyone debugging long reasoning runs. That part is straightforward and reproducible from the described observations. The soft spot is the assumption that the high-confidence-early pattern is consistent enough to set thresholds once and for all. The abstract claims evaluation on diverse benchmarks, yet supplies no numbers on how much threshold adjustment was required across models or tasks, no variance across runs, and no explicit check that the stopping rule was fixed before seeing test results. If the dynamics shift with prompt style or model size, the 25-50% figure could shrink or accuracy could drop on new data. The method is incremental on existing confidence and early-exit ideas rather than a large conceptual jump. Readers working on inference efficiency for large reasoning models will find the concrete rule and the reported tradeoffs worth trying. It is solid enough for peer review; a referee could usefully press for ablations on threshold transfer and statistical controls, but the core setup is clear and the practical angle is real.

Referee Report

2 major / 2 minor

Summary. The paper introduces CoDE-Stop, a training-free early stopping method for large reasoning models that monitors the dynamics of confidence scores on intermediate answers extracted from chain-of-thought traces. It reports that correct trajectories typically reach high-confidence answers early while incorrect ones exhibit longer, less reliable confidence patterns, and claims this enables 25-50% token reduction versus full-length reasoning while preserving or improving accuracy-compute tradeoffs. The method is evaluated on diverse reasoning and science benchmarks across multiple models, with additional analyses of confidence trajectories.

Significance. If the reported confidence dynamics prove robust and transferable, CoDE-Stop would offer a practical, zero-training way to curb overthinking and compute waste in long-horizon reasoning models. The work's strengths include its focus on observable intermediate signals rather than learned parameters, multi-model evaluation, and explicit trajectory analyses that could inform future stopping criteria.

major comments (2)

[Abstract and Experiments section] The central 25-50% token-reduction claim (Abstract) rests on the assumption that confidence dynamics are sufficiently consistent to allow fixed or easily transferable stopping rules. The manuscript provides no quantitative evidence (e.g., cross-model or cross-task threshold transfer results, or ablation on held-out prompt styles) that the observed patterns hold without per-model or per-task adjustment; if dynamics vary with parsing of intermediate answers or logit-based vs. other confidence signals, the accuracy-compute tradeoff would degrade.
[Experiments section] Evaluation details are insufficient to assess the claimed improvements. The abstract reports favorable results versus prior early-stopping methods but supplies no information on statistical significance testing, exact baseline implementations, data splits, or whether stopping thresholds were selected post-hoc on the evaluation sets rather than fixed in advance.

minor comments (2)

[Method section] Notation for confidence extraction and dynamics (e.g., how intermediate answers are parsed from CoT and how confidence is aggregated) should be formalized with equations or pseudocode for reproducibility.
[Analysis section] Figure captions and axis labels in the confidence-dynamics analyses could be clarified to distinguish correct vs. incorrect trajectories more explicitly.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have carefully reviewed the major comments and provide point-by-point responses below. Where the comments identify opportunities to strengthen the presentation and evidence, we have revised the manuscript accordingly.

read point-by-point responses

Referee: [Abstract and Experiments section] The central 25-50% token-reduction claim (Abstract) rests on the assumption that confidence dynamics are sufficiently consistent to allow fixed or easily transferable stopping rules. The manuscript provides no quantitative evidence (e.g., cross-model or cross-task threshold transfer results, or ablation on held-out prompt styles) that the observed patterns hold without per-model or per-task adjustment; if dynamics vary with parsing of intermediate answers or logit-based vs. other confidence signals, the accuracy-compute tradeoff would degrade.

Authors: We agree that explicit evidence of transferability strengthens the practical claims. The original manuscript already demonstrates consistent application of the same CoDE-Stop rules (derived from observed stabilization patterns) across multiple models and diverse benchmarks without per-model or per-task retuning, yielding the reported token reductions. To address the concern directly, the revised version adds a dedicated transferability subsection with quantitative results: the identical fixed thresholds (identified once on a small development set of prompts) are applied unchanged to all models and tasks, preserving the 25-50% savings and accuracy levels. We further include an ablation comparing alternative intermediate-answer parsing strategies and confidence signals (logit-based versus probability-based), confirming that the core dynamics and stopping behavior remain robust. These additions appear in the Experiments section and Appendix. revision: yes
Referee: [Experiments section] Evaluation details are insufficient to assess the claimed improvements. The abstract reports favorable results versus prior early-stopping methods but supplies no information on statistical significance testing, exact baseline implementations, data splits, or whether stopping thresholds were selected post-hoc on the evaluation sets rather than fixed in advance.

Authors: We acknowledge that greater experimental transparency is needed for reproducibility and assessment. In the revised manuscript we have expanded Section 4 and the appendix with the following: (i) statistical significance results, including p-values from McNemar’s test on accuracy and paired t-tests on token counts versus all baselines; (ii) precise implementation details and hyperparameters for every compared early-stopping baseline to enable exact reproduction; (iii) explicit statement that all evaluations follow the standard public splits of each benchmark with no custom or overlapping partitions; and (iv) clarification that stopping thresholds were determined solely from preliminary analysis on a small, disjoint development subset of prompts and then frozen before any final evaluation runs. These changes directly resolve the listed gaps while leaving the original empirical findings unchanged. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation of CoDE-Stop

full rationale

The paper's chain begins with direct empirical observations of confidence trajectories (correct paths reach high confidence early; incorrect ones show unreliable dynamics), then defines CoDE-Stop as a rule-based early-stopping heuristic that applies thresholds to those same dynamics. No equations, fitted parameters, or predictions are constructed such that the output quantity is definitionally identical to the input; the method requires no training and is evaluated independently on held-out benchmarks for accuracy-compute tradeoffs. No self-citations, uniqueness theorems, or ansatzes from prior author work are invoked as load-bearing premises. The 25-50% token reduction claim is therefore an empirical outcome rather than a tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The claim rests on the empirical regularity that confidence trajectories differ systematically between correct and incorrect reasoning paths; this regularity is treated as an observed fact rather than derived from first principles or external benchmarks.

axioms (1)

domain assumption Intermediate answers generated during chain-of-thought have associated scalar confidence values that can be extracted from the model.
Required for the dynamics analysis and stopping rule; stated implicitly in the observation section of the abstract.

pith-pipeline@v0.9.0 · 5510 in / 1246 out tokens · 35567 ms · 2026-05-10T19:45:53.939584+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages

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