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arxiv: 2606.17890 · v1 · pith:E3KT6VUYnew · submitted 2026-06-16 · 💻 cs.CL

Dynamic Rollout Editing for Reducing Overthinking in RL-Trained Reasoning Models

Zihao Wei , Wenjie Shi , Liang Pang , Jingcheng Deng , Shicheng Xu , Shasha Guo , Zenghao Duan , Jiahao Liu
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Jingang Wang Huawei Shen Xueqi Cheng
This is my paper

Pith reviewed 2026-06-27 01:16 UTC · model grok-4.3

classification 💻 cs.CL
keywords overthinkingchain-of-thoughtreinforcement learningGRPOreasoning modelscredit assignmenttrajectory editing
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The pith

Editing successful trajectories during GRPO training breaks the feedback loop that amplifies overthinking in reasoning models.

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

The paper argues that overthinking arises in GRPO-style reinforcement learning because sequence-level credit assignment rewards both the necessary reasoning prefix and any unnecessary continuation after an answer appears. Early in training, successful rollouts already tend to contain slightly more overthinking than unsuccessful ones for the same prompt, and this small difference grows because the algorithm cannot separate the two parts. Dynamic Rollout Editing intervenes on successful trajectories by keeping the verified prefix, shortening the rest, and preferring the edited version inside the same group of rollouts. This weakens the reinforcement for extra thinking without removing the path that reached the correct answer. Readers would care if the method produces shorter, cheaper reasoning traces at inference time while keeping task accuracy intact.

Core claim

In rollouts sampled at the onset of GRPO training, successful trajectories exhibit a slightly higher degree of overthinking than unsuccessful trajectories for the same prompts. Because GRPO assigns credit at the full-sequence level, both the solution-reaching prefix and the unnecessary continuation receive positive update signal, allowing the initial imbalance to grow into more severe overthinking. Dynamic Rollout Editing preserves the accepted verified prefix, edits the remaining thinking, and prefers the edited trajectory within the same RL group, weakening the preference signal for unnecessary thinking without penalizing the reasoning needed to reach the answer.

What carries the argument

Dynamic Rollout Editing (DRE), which preserves the verified prefix of a successful trajectory, edits the post-answer continuation, and substitutes the edited version when forming preferences inside each RL group.

If this is right

  • Training with DRE produces models whose generated chains of thought contain less unnecessary reasoning after the answer has emerged.
  • Task performance stays comparable because the solution-reaching prefix remains unchanged and still receives reinforcement.
  • The early imbalance between successful and unsuccessful trajectories no longer grows into larger overthinking during later training steps.
  • The intervention works across multiple tasks without altering the underlying GRPO update rule.

Where Pith is reading between the lines

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

  • If the early imbalance is the main driver, similar prefix-preserving edits could be tested on other sequence-level RL algorithms that assign credit to full trajectories.
  • Shorter output lengths at inference time would reduce both latency and compute cost for deployed reasoning models.
  • DRE might combine with existing decoding-time stopping rules to produce additive reductions in overthinking.

Load-bearing premise

Successful trajectories already contain slightly more overthinking than unsuccessful ones at the start of GRPO training, supplying the seed for an amplifying feedback loop.

What would settle it

Running standard GRPO training and measuring that the degree of overthinking does not increase across training steps, or running DRE and finding no measurable drop in overthinking relative to the baseline.

Figures

Figures reproduced from arXiv: 2606.17890 by Huawei Shen, Jiahao Liu, Jingang Wang, Jingcheng Deng, Liang Pang, Shasha Guo, Shicheng Xu, Wenjie Shi, Xueqi Cheng, Zenghao Duan, Zihao Wei.

Figure 1
Figure 1. Figure 1: Schematic view of training-time overthinking. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Behavioral evidence from an R1-Zero-like GRPO run of Qwen3-4B-Base. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of dynamic rollout editing. After [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Evaluation on selected AIME24 samples across training steps. Colors and markers indicate different [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Leave-one-out ablations evaluated on selected AIME24 samples across training steps. The [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Representative old-policy probability mis [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Single-prompt rollout progression across training. Each panel is an abridged excerpt from the stored [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: A concrete accepted edit produced by dynamic rollout editing. The shared thinking prefix is preserved [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Paired Raw Model and DRE-trained outputs for an AIME24 prompt where both models reach the verified [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Supplementary representative semantic-dynamics analysis for the original model and the DRE-trained [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
read the original abstract

Long-form chain-of-thought reasoning can improve LLM performance on complex tasks, but models often continue generating unnecessary reasoning after a correct answer has emerged. We refer to this behavior as overthinking. We study this phenomenon from the perspective of GRPO-style reinforcement learning (RL) post-training, framing it as a training-time credit-assignment problem rather than merely a decoding-time stopping problem. In rollouts sampled at the onset of GRPO training, we observe that successful trajectories can exhibit a slightly higher degree of overthinking than unsuccessful trajectories for the same prompts. This early imbalance provides a starting point for an undesirable feedback loop: because GRPO assigns sequence-level credit, it cannot distinguish the solution-reaching prefix from the unnecessary continuation that lengthens a successful trajectory. Both receive positive update signal, allowing the initial imbalance to grow into more severe overthinking during training. To address this issue, we introduce Dynamic Rollout Editing (DRE), a training-time intervention for successful trajectories that continue thinking after answer emergence. DRE preserves the accepted verified prefix, edits the remaining thinking, and prefers the edited trajectory within the same RL group, weakening the preference signal for unnecessary thinking without penalizing the reasoning needed to reach the answer. Experiments across diverse tasks show the effectiveness of DRE.

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 / 1 minor

Summary. The paper observes that at the onset of GRPO training, successful trajectories exhibit a slightly higher degree of overthinking than unsuccessful ones for the same prompts. This early imbalance is argued to initiate an undesirable feedback loop because GRPO's sequence-level credit assignment cannot separate the solution-reaching prefix from unnecessary post-answer continuation. The authors propose Dynamic Rollout Editing (DRE), which preserves the verified prefix of successful trajectories, edits the remaining thinking, and prefers the edited trajectory within the same RL group to weaken the preference signal for overthinking. Experiments across diverse tasks are claimed to demonstrate DRE's effectiveness.

Significance. If the initial imbalance is empirically confirmed and DRE reliably reduces overthinking without degrading answer quality, the approach would supply a training-time mechanism to counteract a specific credit-assignment pathology in sequence-level RL methods such as GRPO, potentially improving the efficiency of reasoning-model post-training.

major comments (3)
  1. [Abstract] Abstract: the central empirical premise that 'successful trajectories can exhibit a slightly higher degree of overthinking than unsuccessful trajectories' is stated only qualitatively, with no definition of overthinking, no token-count or length metrics, no statistical tests, no prompt counts, and no variance across tasks. This observation is load-bearing for the claimed feedback loop and the motivation for DRE.
  2. [Abstract] Abstract: no implementation details are supplied for the DRE procedure itself, including how the 'accepted verified prefix' is identified at training time, what concrete editing operation is applied to the post-answer segment, or how the edited trajectory is inserted and preferred inside the GRPO group.
  3. [Abstract] Abstract: the statement that 'experiments across diverse tasks show the effectiveness of DRE' is unsupported by any reported results, tables, baselines, ablations, or error analysis, so the data support for the central claim cannot be assessed.
minor comments (1)
  1. The manuscript would benefit from an explicit algorithmic description or pseudocode for the editing and preference steps in DRE.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback focused on the abstract. We agree that the abstract is highly condensed and will revise it to incorporate a concise definition and metric reference for the initial observation, a brief outline of the DRE procedure, and key quantitative experimental highlights. This addresses the concerns while preserving the abstract's brevity. We respond point-by-point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central empirical premise that 'successful trajectories can exhibit a slightly higher degree of overthinking than unsuccessful trajectories' is stated only qualitatively, with no definition of overthinking, no token-count or length metrics, no statistical tests, no prompt counts, and no variance across tasks. This observation is load-bearing for the claimed feedback loop and the motivation for DRE.

    Authors: We agree the abstract states the premise qualitatively. The manuscript defines overthinking explicitly as post-answer continuation after a verified correct answer, measures it via token count of the post-answer segment, and reports the slight increase (with prompt counts and task variance) in the analysis of initial rollouts. To improve self-containment we will revise the abstract to include a one-sentence definition plus mention of the token metric. revision: yes

  2. Referee: [Abstract] Abstract: no implementation details are supplied for the DRE procedure itself, including how the 'accepted verified prefix' is identified at training time, what concrete editing operation is applied to the post-answer segment, or how the edited trajectory is inserted and preferred inside the GRPO group.

    Authors: The full manuscript details these steps in the method section: the verified prefix is the initial segment up to the first correct answer (identified via verification), the editing operation truncates or replaces the post-answer continuation with a short neutral token sequence, and the edited rollout is inserted into the same GRPO group with an adjusted advantage that weakens the signal for overthinking. We will add a compact description of these three elements to the abstract. revision: yes

  3. Referee: [Abstract] Abstract: the statement that 'experiments across diverse tasks show the effectiveness of DRE' is unsupported by any reported results, tables, baselines, ablations, or error analysis, so the data support for the central claim cannot be assessed.

    Authors: The abstract summarizes the outcome; the manuscript contains the supporting tables, baselines (standard GRPO), ablations on editing variants, and per-task metrics showing reduced overthinking tokens with no degradation in final accuracy. We will revise the abstract to include one or two representative quantitative results (e.g., average token reduction) to make the claim more concrete. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper describes an empirical observation of early-training overthinking imbalance in GRPO rollouts and introduces DRE as a direct intervention that edits post-answer segments while preserving verified prefixes. No equations, fitted parameters, or self-citations are shown to reduce the central claim or method to a tautology by construction. The motivation rests on a stated qualitative observation rather than a derived result that loops back to itself, and the editing procedure is presented as an independent training-time modification without load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests primarily on a domain assumption about GRPO credit assignment and an empirical observation of initial imbalance; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)
  • domain assumption GRPO assigns sequence-level credit that cannot distinguish the solution-reaching prefix from the unnecessary continuation
    This premise is invoked in the abstract to explain why the early imbalance grows into severe overthinking.

pith-pipeline@v0.9.1-grok · 5786 in / 1292 out tokens · 50464 ms · 2026-06-27T01:16:53.709048+00:00 · methodology

discussion (0)

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Reference graph

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