Diagnosing Harmful Continuation in Answer-Correct Long-CoT Training Traces

Chen He; Fumin Shen; Lei Wang; Wenxuan Zhang; Yuhao Wu

arxiv: 2605.29288 · v1 · pith:QXBET7U4new · submitted 2026-05-28 · 💻 cs.AI

Diagnosing Harmful Continuation in Answer-Correct Long-CoT Training Traces

Chen He , Yuhao Wu , Lei Wang , Wenxuan Zhang , Fumin Shen This is my paper

Pith reviewed 2026-06-29 07:24 UTC · model grok-4.3

classification 💻 cs.AI

keywords long chain-of-thoughtsupervised fine-tuningharmful continuationpost-conclusion continuationuncertainty-geometry mismatchHarmful Continuation Cutreasoning data curation

0 comments

The pith

Removing post-conclusion continuation from answer-correct long-CoT traces improves SFT outcomes.

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

Answer-correct long chain-of-thought traces can still produce uneven fine-tuning results depending on what follows the supported answer. The paper isolates post-conclusion continuation, extra reasoning that continues after the answer appears established, and tests its effect by applying a delete-only editor that removes only the suffix while preserving the answer. Supervised fine-tuning on the edited traces yields better results than on the originals, indicating the continuation introduces harmful signals. The authors characterize the removed segments by persistent local uncertainty paired with weakened terminal-directional progress in hidden states. They also introduce Harmful Continuation Cut as a lightweight proxy to approximate the boundary of such segments.

Core claim

The paper establishes that post-conclusion continuation in answer-correct long-CoT traces is harmful to SFT training. Removing these continuations via answer-preserving suffix deletion produces improved fine-tuning outcomes. The removed segments show persistent local uncertainty together with weakened terminal-directional progress, forming an uncertainty-geometry mismatch. A lightweight boundary proxy called Harmful Continuation Cut approximates the editor-identified boundary.

What carries the argument

A delete-only editor that constructs answer-preserving suffix removal to isolate and test the effect of post-conclusion continuation.

If this is right

SFT on traces without post-conclusion continuation yields improved reasoning performance.
Data curation pipelines for CoT supervision should detect and remove such continuations.
The uncertainty-geometry mismatch offers a diagnostic for identifying low-value segments in reasoning traces.
Harmful Continuation Cut provides a scalable proxy for boundary detection without full editor intervention.

Where Pith is reading between the lines

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

Automatic detection tools built on the HCC proxy could enable large-scale cleaning of existing CoT datasets.
The same continuation pattern may degrade performance in other training methods that rely on full trace supervision.
Similar uncertainty-progress mismatches could serve as a general signal for data quality issues across model training.

Load-bearing premise

The delete-only editor removes only the post-conclusion continuation and does not introduce other systematic changes that explain the SFT improvement.

What would settle it

Training models on the original versus edited traces and observing no consistent gain in downstream reasoning accuracy after removal would falsify the claim that the continuation is harmful.

Figures

Figures reproduced from arXiv: 2605.29288 by Chen He, Fumin Shen, Lei Wang, Wenxuan Zhang, Yuhao Wu.

**Figure 3.** Figure 3: Operational hidden-state progress of retained [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

**Figure 4.** Figure 4: Post-training uncertainty diagnostics for gen [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Operational uncertainty-progress diagnostics [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: , we select 6 subjects that require different types of knowledge, including college physics, college biology, clinical knowledge, professional psychology, high school statistics, and high school biology. From the results, we can see that HCCbased SFT outperforms Vanilla-based SFT across all subjects, and achieves comparable performance to the editor-based reference. These results suggest that models trai… view at source ↗

**Figure 7.** Figure 7: Additional uncertainty-side diagnosis. (a) [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗

**Figure 8.** Figure 8: Additional geometry-side diagnosis. (a) Token-normalized hidden displacement of retained reasoning and editor-removed continuation. (b) ECDF of token-normalized forward progress. Editor-removed continuation is more concentrated in low-progress regions under the operational proxy. D.2 Additional analysis of Test Datasets Case study [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗

**Figure 9.** Figure 9: Case study of harmful continuation after SFT. The left part indicates the reasoning process of a model [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗

read the original abstract

Long chain-of-thought (CoT) traces are widely used as supervision for reasoning-oriented LLM SFT, yet answer-correct traces can still lead to markedly different fine-tuning outcomes. We study post-conclusion continuation in answer-correct long-CoT data: a continuation where the answer appears sufficiently supported, but the trace continues with additional reasoning that remains in the supervised target. To test its training effect, we use a delete-only editor to construct answer-preserving suffix removal and compare CoT-based SFT on the original and processed traces. We observe improved SFT outcomes after removing the editor-identified post-conclusion continuation, suggesting that this continuation is harmful to training in our setting. We therefore refer to this empirically supported phenomenon as harmful continuation. Beyond this intervention, we further characterize the removed post-conclusion continuation through uncertainty and hidden-state progress. We observe persistent local uncertainty together with weakened terminal-directional progress, forming an uncertainty--geometry mismatch. Finally, we instantiate Harmful Continuation Cut (HCC), a lightweight boundary proxy that approximates the editor-identified post-conclusion continuation boundary.

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

The paper names harmful continuation in correct long-CoT traces and claims removing it helps SFT, but supplies no numbers or controls to show the effect is real.

read the letter

The main thing to know is that the authors flag post-conclusion continuation in answer-correct long-CoT traces as harmful to SFT and say a delete-only editor that cuts the suffix improves outcomes. They also characterize the removed text by persistent local uncertainty paired with weak terminal progress in hidden states, then offer HCC as a lightweight proxy for the cut point.

What is new is the specific isolation of this post-answer drift as a distinct problem rather than lumping it with general length or correctness issues. The uncertainty-geometry mismatch is a clean way to describe the pattern they observe.

The paper does a reasonable job of explaining why this matters for reasoning data and giving a practical intervention plus a simple diagnostic.

The soft spots are substantial. The abstract contains no dataset sizes, no performance deltas, no error bars, and no baseline comparisons, so the central claim cannot be checked. The stress-test concern holds: even if the editor is described as delete-only and answer-preserving, there is no evidence that length shifts, EOS placement, or tokenization changes were ruled out as alternative explanations for any SFT gain. Without those checks the causal link to harmful continuation does not follow.

This is aimed at people who curate or filter CoT data for math and logic training. A reader might borrow the HCC idea for their own work, but the paper is still at the observation stage.

I would not send it to peer review in its current form. It needs the quantitative results and the control experiments before the claim can be evaluated.

Referee Report

2 major / 1 minor

Summary. The paper claims that answer-correct long-CoT traces contain post-conclusion continuation that harms downstream SFT performance. It constructs processed traces via a delete-only, answer-preserving editor that excises this suffix, reports improved SFT outcomes relative to the original traces, characterizes the excised segments via persistent local uncertainty combined with weakened terminal-directional hidden-state progress (an uncertainty-geometry mismatch), and introduces Harmful Continuation Cut (HCC) as a lightweight boundary proxy.

Significance. If the central empirical result holds after proper controls and reporting, the work would be significant for reasoning-oriented LLM training pipelines: it isolates a specific, removable defect in otherwise correct CoT supervision that measurably degrades fine-tuning, supplies a practical editor and a cheap proxy (HCC), and links the defect to measurable uncertainty and geometry signals. These elements could directly inform data-cleaning practices without architectural changes.

major comments (2)

[Abstract] Abstract: the central claim of 'improved SFT outcomes' after editor intervention is stated without any quantitative results (accuracy deltas, dataset sizes, number of traces, baselines, error bars, or statistical tests), so the magnitude, reliability, and reproducibility of the effect cannot be evaluated.
[Abstract] Abstract (description of delete-only editor and 'answer-preserving suffix removal'): no verification is supplied that the only systematic difference between original and processed traces is excision of the post-conclusion continuation. Absent token-level diff statistics, length-distribution comparisons, EOS-placement checks, or control removals of matched-length non-terminal segments, incidental artifacts (shorter targets, changed tokenization boundaries) could explain any SFT delta independently of the claimed harmful continuation.

minor comments (1)

The phrase 'uncertainty--geometry mismatch' is used to characterize the removed segments but is not given an explicit operational definition or formula in the abstract; a precise quantification (e.g., how terminal-directional progress is measured in hidden states) would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below and will revise the manuscript to improve clarity and rigor where the points identify gaps.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim of 'improved SFT outcomes' after editor intervention is stated without any quantitative results (accuracy deltas, dataset sizes, number of traces, baselines, error bars, or statistical tests), so the magnitude, reliability, and reproducibility of the effect cannot be evaluated.

Authors: We agree that the abstract would benefit from quantitative indicators to allow immediate evaluation of the effect. While the body of the paper reports accuracy deltas on reasoning benchmarks, dataset sizes (number of traces), baselines, and error bars with statistical tests, the abstract omits them for brevity. In revision we will incorporate key quantitative results (e.g., accuracy improvements, trace counts, and significance) directly into the abstract. revision: yes
Referee: [Abstract] Abstract (description of delete-only editor and 'answer-preserving suffix removal'): no verification is supplied that the only systematic difference between original and processed traces is excision of the post-conclusion continuation. Absent token-level diff statistics, length-distribution comparisons, EOS-placement checks, or control removals of matched-length non-terminal segments, incidental artifacts (shorter targets, changed tokenization boundaries) could explain any SFT delta independently of the claimed harmful continuation.

Authors: This concern is valid and highlights a need for stronger controls. The current description in the abstract does not include explicit verification. We will add, in a revised methods or appendix section, token-level diff statistics, length-distribution comparisons, EOS-placement checks, and a control experiment that removes matched-length non-terminal segments. These additions will demonstrate that performance differences arise specifically from post-conclusion removal rather than length or boundary artifacts. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical intervention is self-contained

full rationale

The paper's core claim rests on constructing answer-preserving suffix removals via a delete-only editor and measuring downstream SFT deltas between original and edited traces. This is an external intervention whose effect is evaluated against held-out training outcomes rather than being defined into existence or recovered from fitted parameters. No equations appear, no self-citation chain supplies a uniqueness theorem or ansatz, and the subsequent uncertainty/geometry characterization plus HCC proxy are presented as observational descriptions, not derivations that loop back to the editor output by construction. The derivation chain therefore remains independent of its inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no free parameters, axioms, or invented entities are stated or implied beyond the empirical naming of the observed phenomenon.

pith-pipeline@v0.9.1-grok · 5723 in / 982 out tokens · 30409 ms · 2026-06-29T07:24:20.011942+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

4 extracted references

[1]

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[2]

Stuck in traffic for 2 hours: - No movement, so he remains 180 miles away from home
[3]

Driving at 30 mph for 0.5 hours: - Distance covered: \(30 \, \text{mph} \times 0.5 \, \text{hours} = 15 \, \text{miles}\)- New distance from home: \(180 \, \text{miles} - 15 \, \text{miles} = 165 \, \text{miles}\)
[4]

Therefore, the total distance from home is 195 + 120 = 315 miles.Wait, but hold on

Driving at 80 mph for the remaining 1.5 hours: - Distance covered: \(80 \, \text{mph} \times 1.5 \, \text{hours} = 120 \, \text{miles}\)- Final distance from home: \(165 \, \text{miles} - 120 \, \text{miles} = 45 \, \text{miles}\) <think>...So, the distance he covers in 1.5 hours is 80 * 1.5 = 120 miles. Therefore, the total distance from home is 195 + 12...

[1] [1]

First 3 hours at 60 mph: - Distance covered: \(60 \, \text{mph} \times 3 \, \text{hours} = 180 \, \text{miles}\)- He is 180 miles away from home

[2] [2]

Stuck in traffic for 2 hours: - No movement, so he remains 180 miles away from home

[3] [3]

Driving at 30 mph for 0.5 hours: - Distance covered: \(30 \, \text{mph} \times 0.5 \, \text{hours} = 15 \, \text{miles}\)- New distance from home: \(180 \, \text{miles} - 15 \, \text{miles} = 165 \, \text{miles}\)

[4] [4]

Therefore, the total distance from home is 195 + 120 = 315 miles.Wait, but hold on

Driving at 80 mph for the remaining 1.5 hours: - Distance covered: \(80 \, \text{mph} \times 1.5 \, \text{hours} = 120 \, \text{miles}\)- Final distance from home: \(165 \, \text{miles} - 120 \, \text{miles} = 45 \, \text{miles}\) <think>...So, the distance he covers in 1.5 hours is 80 * 1.5 = 120 miles. Therefore, the total distance from home is 195 + 12...