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arxiv: 2605.22211 · v1 · pith:IYHAY4R4new · submitted 2026-05-21 · 💻 cs.AI

CLORE: Content-Level Optimization for Reasoning Efficiency

Yuyang Wu , Qiyao Xue , Guanxing Lu , Weichen Liu , Zihan Wang , Manling Li , Olexandr Isayev This is my paper

Pith reviewed 2026-05-22 05:47 UTC · model grok-4.3

classification 💻 cs.AI
keywords reasoning efficiencycontent optimizationreinforcement learninglarge language modelsmathematical reasoningDPOpost-trainingrollout editing
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The pith

Editing correct reasoning traces to remove repetition improves the accuracy-efficiency trade-off in language model post-training.

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

The paper claims that existing methods for efficient reasoning in large language models focus too much on controlling response length and leave the actual content of reasoning steps weakly supervised. CLORE addresses this by using an external model to edit only correct on-policy rollouts, deleting repetitive segments, illegible content, or reasoning that continues after the answer is found while keeping the final answer intact. These edited pairs are then trained with an auxiliary reference-free DPO objective alongside normal policy gradient updates. The approach keeps changes local so the edited examples stay close to the original policy distribution. Experiments on two 7B models across five math benchmarks show better accuracy and efficiency together with compatibility to other training techniques.

Core claim

CLORE improves reasoning efficiency by editing correct on-policy rollouts. An external augmentation model deletes repetitive segments, illegible or task-irrelevant content, and superfluous reasoning after the solution is established, while preserving the final answer. The resulting augmented-original pairs are optimized with an auxiliary reference-free DPO objective alongside standard policy-gradient training. By restricting augmentation to correct trajectories and performing local deletion, CLORE keeps edited rollouts close to the policy distribution and mitigates off-policy mismatch.

What carries the argument

Content-level editing of correct on-policy rollouts by an external augmentation model, followed by auxiliary reference-free DPO optimization.

If this is right

  • The accuracy-efficiency trade-off improves on mathematical reasoning benchmarks for models such as DeepSeek-R1-Distill-Qwen-7B and Qwen2.5-Math-7B.
  • The method remains compatible with GRPO, DAPO, Training Efficient, and ThinkPrune.
  • Content analyses show reductions in repetitive reasoning, illegible content, and post-answer exploration.
  • Content-level supervision acts as a complementary direction to length-level control.

Where Pith is reading between the lines

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

  • The editing approach could be tested on non-mathematical tasks such as code generation or scientific explanation to check for broader applicability.
  • Jointly training the augmentation model with the main policy might reduce dependence on a separate external model.
  • Direct content supervision of this kind could be combined with length-based rewards to further refine reasoning traces.

Load-bearing premise

The external augmentation model can reliably delete only repetitive, illegible, or post-answer segments from correct trajectories without introducing factual errors or changing the final answer, and the resulting edited rollouts remain sufficiently close to the policy distribution to avoid harmful off-policy effects.

What would settle it

If running CLORE on the five mathematical reasoning benchmarks produces lower accuracy or worse efficiency metrics than standard RL post-training without the editing step, the central claim would be falsified.

Figures

Figures reproduced from arXiv: 2605.22211 by Guanxing Lu, Manling Li, Olexandr Isayev, Qiyao Xue, Weichen Liu, Yuyang Wu, Zihan Wang.

Figure 1
Figure 1. Figure 1: Overview of CLORE. Existing length-based efficient reasoning methods regulate response length but fail to remove low-quality reasoning content. CLORE improves efficient reasoning by augmenting correct trajectories to delete repetitive, illegible, and uninformative reasoning segments, while remaining compatible with existing efficient RL training methods. These patterns characterize a form of redundant unin… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the CLORE framework. The policy model samples multiple reasoning tra￾jectories, the correct ones are augmented to remove low-quality content, and the resulting preference pairs are optimized via a reference-free DPO objective jointly with policy-gradient training. of correct trajectories. We restrict augmentation to correct trajectories because they generally contain less low-quality reasoning … view at source ↗
Figure 3
Figure 3. Figure 3: DeepSeek-R1-Distill-Qwen-7B training dynamics, with average response length on DAPO￾Math-17K and validation accuracy on OlympiadBench, MATH500, AMC2023 (left to right). Minerva. On Qwen2.5-Math-7B, the gains are more pronounced, with average AE improvements mostly exceeding 1.0 for GRPO, DAPO, and ThinkPrune. The largest improvement is observed when applying CLORE to GRPO on Minerva, where the AE score inc… view at source ↗
Figure 4
Figure 4. Figure 4: Qwen2.5-Math-7B training dynamics, with average response length on DAPO-Math-17K and validation accuracy on OlympiadBench, MATH500 and AMC2023 (left to right). 5.2 Is Content-Level Optimization Complementary to Length-Level Control? The evaluation results in [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Ablation study on the DPO weight and augmentation model. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Augmentation analysis. training in [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Repetitive reasoning analysis. level supervision reduces repetitive reasoning patterns. We compare the base model, GRPO, and GRPO with CLORE using repetition metrics rep-4, rep-8, and the longest re￾peated span [47]. GRPO increases repetition relative to the base model, suggesting that rewards alone may rein￾force redundant reasoning during RL training. In contrast, GRPO+CLORE reduces both local n-gram rep… view at source ↗
Figure 8
Figure 8. Figure 8: Post-answer reasoning analysis. solution, measured as the fraction of generated reasoning that appears after the final answer has been reached, com￾puted as the average percentage of post-answer tokens in responses. The base model frequently continues reasoning after reaching the solution, while RL training only par￾tially reduces this post-answer content. Adding CLORE consistently suppresses this behavior… view at source ↗
Figure 9
Figure 9. Figure 9: Illegible reasoning analysis. quality by measuring the presence of uninterpretable or task-irrelevant content through averaged LLM-as-a-judge result over various strong commercial models5 . The base model and standard RL-trained methods still produce il￾legible reasoning, showing that final-answer correctness alone does not sufficiently control intermediate reason￾ing quality. CLORE consistently reduces su… view at source ↗
read the original abstract

Reinforcement learning post-training has improved the reasoning ability of large language models, but often produces unnecessarily long, repetitive, or semantically opaque reasoning traces. Existing efficient reasoning methods mainly regulate response length through explicit budgets or length-aware rewards, leaving intermediate reasoning content weakly supervised. We propose CLORE, a content-level optimization framework that improves reasoning efficiency by editing correct on-policy rollouts. CLORE uses an external augmentation model to delete repetitive segments, illegible or task-irrelevant content, and superfluous reasoning after the solution is established, while preserving the final answer. The resulting augmented--original pairs are optimized with an auxiliary reference-free DPO objective alongside standard policy-gradient training. By restricting augmentation to correct trajectories and performing local deletion, CLORE keeps edited rollouts close to the policy distribution and mitigates off-policy mismatch. Experiments on DeepSeek-R1-Distill-Qwen-7B and Qwen2.5-Math-7B across five mathematical reasoning benchmarks show that CLORE improves the accuracy--efficiency trade-off and remains compatible with GRPO, DAPO, Training Efficient, and ThinkPrune. Content-level analyses further show that CLORE reduces repetitive reasoning, illegible content, and post-answer exploration, supporting content-level supervision as a complementary direction to length-level control.

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

2 major / 2 minor

Summary. The manuscript proposes CLORE, a content-level optimization framework for LLM reasoning efficiency. It uses an external augmentation model to edit correct on-policy rollouts via local deletions of repetitive, illegible, or post-answer segments (while preserving the final answer), then optimizes the resulting augmented-original pairs with an auxiliary reference-free DPO objective alongside standard policy-gradient training. Experiments on DeepSeek-R1-Distill-Qwen-7B and Qwen2.5-Math-7B across five mathematical reasoning benchmarks report improved accuracy-efficiency trade-offs, with compatibility to GRPO, DAPO, Training Efficient, and ThinkPrune; content analyses show reductions in repetitive and post-answer content.

Significance. If the augmentation preserves reasoning validity, CLORE supplies a complementary content-level supervision signal to existing length-based or reward-shaping approaches for efficient reasoning. The reported compatibility with multiple RL methods and the supporting content-level analyses would strengthen the case for content supervision as a distinct direction.

major comments (2)
  1. [Abstract and Methods] Abstract and Methods: The central claim that CLORE improves the accuracy-efficiency trade-off via content-level supervision rests on the external augmentation model performing only safe, local deletions on correct trajectories without introducing factual errors, changing solution paths, or altering the final answer. No quantitative checks (edit-induced answer change rate, semantic similarity, or human-rated reasoning quality) are reported on the augmented data, leaving open the possibility that observed gains arise from implicit filtering or length reduction rather than genuine content supervision.
  2. [Experiments] Experiments: The abstract reports accuracy-efficiency gains on five benchmarks but provides no details on data splits, run-to-run variance, statistical significance testing, or full hyperparameter settings for the auxiliary DPO component, preventing verification of the robustness of the claimed improvements.
minor comments (2)
  1. [Methods] Clarify in the methods section how the reference-free DPO loss is exactly formulated and weighted relative to the primary policy-gradient objective.
  2. [Experiments] Ensure all benchmark results include both accuracy and efficiency metrics (e.g., token count or latency) in the same table for direct trade-off comparison.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and outline the revisions we will make to strengthen the presentation of our results and experimental details.

read point-by-point responses

  1. Referee: [Abstract and Methods] Abstract and Methods: The central claim that CLORE improves the accuracy-efficiency trade-off via content-level supervision rests on the external augmentation model performing only safe, local deletions on correct trajectories without introducing factual errors, changing solution paths, or altering the final answer. No quantitative checks (edit-induced answer change rate, semantic similarity, or human-rated reasoning quality) are reported on the augmented data, leaving open the possibility that observed gains arise from implicit filtering or length reduction rather than genuine content supervision.

    Authors: We agree that quantitative validation of the augmentation process would provide stronger support for the claim that gains stem from content-level supervision rather than incidental effects. By construction, augmentation is restricted to correct on-policy rollouts and limited to local deletions that preserve the final answer, which keeps edited trajectories close to the original distribution. However, we acknowledge the absence of explicit metrics in the current version. In the revised manuscript, we will add an analysis subsection reporting: (i) the edit-induced answer change rate (verified to be zero by construction but quantified across the dataset), (ii) semantic similarity (e.g., embedding cosine similarity) between original and augmented trajectories, and (iii) a small-scale human evaluation of reasoning quality on a random sample of 100 pairs. These additions will appear in the Methods and Experiments sections to address the concern directly. revision: yes

  2. Referee: [Experiments] Experiments: The abstract reports accuracy-efficiency gains on five benchmarks but provides no details on data splits, run-to-run variance, statistical significance testing, or full hyperparameter settings for the auxiliary DPO component, preventing verification of the robustness of the claimed improvements.

    Authors: We concur that additional experimental details are necessary for reproducibility and to demonstrate robustness. In the revised manuscript, we will expand the Experiments section to include: (i) explicit description of training/validation/test splits for each benchmark, (ii) mean and standard deviation of accuracy and efficiency metrics across at least three independent runs with different random seeds, (iii) results of statistical significance tests (e.g., paired t-tests or Wilcoxon tests) comparing CLORE against baselines, and (iv) complete hyperparameter settings for the auxiliary DPO objective, including the DPO beta coefficient, learning rate, batch size, and number of epochs. These details will also be summarized in a new table or appendix for clarity. revision: yes

Circularity Check

0 steps flagged

No circularity: CLORE framework is empirically grounded without self-referential derivations

full rationale

The paper introduces CLORE as a practical framework that augments correct on-policy rollouts via an external model and applies auxiliary DPO alongside standard policy-gradient training. No mathematical derivations, equations, or first-principles predictions are presented that reduce the accuracy-efficiency improvements to fitted parameters defined by the result itself or to self-citation chains. The method explicitly builds on existing techniques (GRPO, DAPO, DPO) with stated assumptions about the augmentation model's behavior, and experimental results on multiple benchmarks provide independent empirical support. The central claims do not collapse into tautological redefinitions or imported uniqueness theorems.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that an external model can perform safe, correctness-preserving edits and that the resulting pairs remain on-policy enough for stable training.

axioms (1)
  • domain assumption External augmentation model deletes only superfluous content while preserving correctness and final answer
    Invoked when restricting augmentation to correct trajectories and performing local deletion to keep edited rollouts close to policy distribution.

pith-pipeline@v0.9.0 · 5772 in / 1245 out tokens · 40117 ms · 2026-05-22T05:47:02.194382+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    CLORE uses an external augmentation model to delete repetitive segments, illegible or task-irrelevant content, and superfluous reasoning after the solution is established, while preserving the final answer. The resulting augmented–original pairs are optimized with an auxiliary reference-free DPO objective alongside standard policy-gradient training.

  • IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    Experiments on DeepSeek-R1-Distill-Qwen-7B and Qwen2.5-Math-7B across five mathematical reasoning benchmarks show that CLORE improves the accuracy-efficiency trade-off

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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