Pith. sign in

REVIEW 3 major objections 5 minor 2 cited by

Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured Multi-Turn Decomposition

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read By decomposing chain-of-thought into explicit multi-turn interactions with per-turn answers, MinD cuts output tokens and time-to-first-token by up to ~70% while keeping competitive accuracy on math reasoning benchmarks.

desk verdict Useful efficiency method with a credible mechanism, but the accuracy-maintenance claim needs error bars and a clearer presentation; worth a serious referee. read the letter →

arxiv 2505.19788 v2 pith:DSDBGVVL submitted 2025-05-26 cs.AI

Zihao Zeng , Xuyao Huang , Boxiu Li , Hao Zhang , Zhijie Deng This is my paper
classification cs.AI
keywords multi-turnreasoningchain-of-thoughtGRPOefficiencytokenreductiontime-to-first-tokenlargemodelsthinkingunits
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Large reasoning models spend most of their output tokens on repeated attempts, verifications, and alternative solution paths that no longer change the answer. This paper claims that this redundancy can be removed by restructuring chain-of-thought into explicit multi-turn interactions, where each turn contains one thinking unit plus a candidate answer, and the model commits to an answer early. The proposed MinD method first fine-tunes the model on multi-turn traces segmented by a teacher model, then applies GRPO reinforcement learning to prefer correct responses with fewer turns. On DeepSeek-R1-Distill models of 1.5B and 7B parameters, this yields up to ~70% lower output-token usage and up to 4.2x faster time-to-first-token while keeping accuracy competitive on MATH-500, AIME24, AMC23, and GPQA-Diamond. The central claim is that making thinking units explicit is what turns an uncontrollable long chain of thought into a controllable, efficient reasoning process.

What carries the argument

The load-bearing object is the multi-turn decomposition format of Equation (6): <think>u1</think>a1 <think>u2</think>a2 ... <think>un</think>an, where each thinking unit $u_k$ is paired with an intermediate answer $a_k$. This format makes the unit structure of vanilla chain-of-thought explicit and decodable, which is what allows the number of reasoning steps to be controlled. Three components carry the argument: GPT-4o-based unit segmentation and intermediate-answer completion; the unit-level redundancy rate $URR = (n - n^*)/n$, which quantifies the waste being targeted; and the GRPO reward $R = R_{\text{format}} + R_{\text{accuracy}} + R_{\text{unit}}$, where the per-token normalization $1/|o_i|$ in the GRPO objective is what implicitly biases the policy toward short, correct outputs. The $R_{\text{unit}}$ term is what prevents the multi-turn structure from collapsing back into a single monolithic chain of thought during optimization.

What would settle it

Run MinD on a benchmark where the original model's measured unit-level redundancy is near zero; if token usage still drops substantially, the savings are not coming from removing redundant thinking units and the paper's mechanistic explanation is wrong. Alternatively, compare the accuracy of MinD's first-turn answer $a_1$ alone against the original model's final-answer accuracy at the same token budget; a large gap would show that the speedup sacrifices reasoning the original model would have done.

Watch

Extended reading notes

Core claim

MinD's central claim is that the conventional chain-of-thought of a large reasoning model is a chain of discrete 'thinking units,' each of which can produce a candidate answer, and that most of these units are redundant: the paper measures unit-level redundancy rates of 69.8% (1.5B) and 35.8% (7B) on MATH-500. The method converts a standard think-then-answer trace into the multi-turn sequence <think>u1</think>a1 <think>u2</think>a2 ... by using GPT-4o to segment the chain of thought into units and to complete the intermediate answer after each unit. The model is then supervised fine-tuned on this format and optimized with GRPO, whose per-token normalization implicitly rewards shorter correct completions, so the model learns to answer in fewer turns without an explicit turn-count penalty. Across the reported benchmarks the 1.5B model cuts output tokens by 52-76% and the 7B model by 18-49%, with accuracy that stays within a few points of the original (and in several out-of-distribution cases exceeds it).

Load-bearing premise

The pipeline assumes that the teacher model's split-up of the reasoning traces and its intermediate answers are accurate enough that training on them does not make the model dumber; if those training targets contain systematic mistakes, the token savings would come at the cost of answer quality.

Editorial extensions

If this is right

  • On MATH-500 the 1.5B MinD model uses 68% fewer tokens (1719 vs 5389) while keeping accuracy at 82.8% versus the original 85.4%.
  • The efficiency transfers out of distribution: for the 1.5B model, token usage drops 69% on AIME24, 76% on AMC23, and 52% on GPQA-Diamond.
  • Because every turn ends with a candidate answer, a user or system can halt or continue the reasoning at any turn, giving explicit control over the compute spent.
  • GRPO is the component that produces the compression: SFT alone increases token usage (5655 vs 5389 on MATH-500), and removing the unit-compactness reward makes the multi-turn structure degenerate.
  • The 7B model gains accuracy on out-of-distribution benchmarks (+5.6 points on AMC23 and +5.0 on GPQA-Diamond) while also cutting tokens, suggesting the method does not simply trade quality for brevity.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The turn-level structure suggests a natural early-exit scheme: a system could stop after turn k once a confidence measure on $a_k$ passes a threshold, turning MinD into an adaptive compute allocator.
  • Because the brevity bias in GRPO is implicit rather than an explicit turn penalty, the same 'multi-turn decomposition + RL' recipe could transfer to other verbose generation settings, such as code repair or multi-step tool use, wherever intermediate checkpoints are natural.
  • The URR metric could serve as a pre-training diagnostic: on problems where the base model already has low unit-level redundancy, MinD's gains should be small, so the method could be applied selectively to high-URR queries.
  • The paper's 'done is better than perfect' principle implies a testable hypothesis about where the efficiency-accuracy tradeoff lives: whether later turns mostly catch genuine mistakes in the first answer, or mostly re-confirm it; measuring first-turn accuracy against final accuracy would settle this.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes Multi-Turn Decomposition (MinD), a method that converts the monolithic think-then-answer chain-of-thought of large reasoning models into a sequence of explicit multi-turn interactions, where each turn contains one thinking unit and an intermediate answer. The authors construct SFT data by using GPT-4o to segment R1-Distill model traces into thinking units and to generate intermediate answers, then apply GRPO with reward terms for format compliance, answer accuracy, and unit compactness. Experiments on DeepSeek-R1-Distill-Qwen-1.5B/7B across MATH-500, AIME24, AMC23, and GPQA-Diamond report up to roughly 70% reduction in output tokens and up to 4.2x reduction in time-to-first-token while claiming competitive accuracy. The paper also connects the efficiency gains to an empirical unit-level redundancy analysis of the base models.

Significance. If the central claim holds, MinD provides a simple and appealing way to reduce the over-reasoning of LRMs by making thinking units explicit and user-controllable, with efficiency gains that are large and consistent across two model sizes. The efficiency evidence is genuinely strong: token reductions of 50-70% on several benchmarks and TTFT speedups of 2-4x are the kind of concrete, falsifiable results that make this paper useful to the community. The paper does not fit a model to produce its efficiency claims, and the GRPO-based reduction is measured rather than enforced by an explicit length penalty, which strengthens the empirical finding. However, the accuracy-maintenance part of the headline claim is not statistically supported because the out-of-distribution benchmarks are very small and no uncertainty quantification or multiple-seed results are reported. There is also an internal inconsistency in the reported GPQA-Diamond numbers. With these issues addressed, the paper would be a solid contribution to efficient-reasoning research.

major comments (3)
  1. [Table 3] The claim that MinD 'maintains competitive performance' on out-of-distribution benchmarks is not statistically established. AIME24 and AMC23 contain roughly 30 problems and GPQA-Diamond 198, yet Table 3 reports accuracy differences without error bars, confidence intervals, or repeated-seed results. For example, on AIME24 (1.5B), MinD scores 30.0% versus 26.7% for the original model, a difference of one problem, and the 95% confidence interval on this difference is approximately +/-23 percentage points. On GPQA-Diamond (1.5B), the reported drop from 32.3% to 31.3% is a difference of two questions. The token and TTFT reductions are likely real, but the 'while maintaining competitive performance' clause is therefore consistent with a material OOD accuracy degradation. The authors should report binomial confidence intervals, run multiple seeds, or explicitly soften the performance-maintenance claim.
  2. [Tables 3 and 4] There is an internal inconsistency in the GPQA-Diamond numbers for the 1.5B MinD model: Table 3 reports accuracy 31.3 and 4690 tokens, while Table 4 reports accuracy 37.4 and 4345 tokens for the same setting. Because Table 3 is used for the headline results and Table 4 is used for the ablation discussion, at least one of these tables is wrong. The authors must reconcile these values and verify all reported numbers, since this kind of discrepancy undermines confidence in the rest of the tables.
  3. [Sections 3.3 and 4.3] The Unit Compactness Reward R_unit is not operationalizable from the manuscript. Section 4.3 states that it penalizes reasoning turns containing multiple exploratory trajectories and that this is 'detected by linguistic cues such as phrases like double-check,' but no detection algorithm, rule, or prompt is given, and Appendix B does not include the corresponding template. Since the ablation in Figure 6 shows that removing R_unit leads to structural collapse and the paper identifies this reward as essential, the exact method cannot be reproduced from the text. Please specify the detection mechanism or provide the exact evaluation prompt.
minor comments (5)
  1. [Section 3.1] The citation for GRPO is incorrect: the text cites reference [4] (DeepSeek-R1) where GRPO was introduced by DeepSeekMath (reference [23]). Please fix the reference.
  2. [Section 3.2] The word 'Concretley' should be spelled 'Concretely'.
  3. [Section 4.1 and Table 3] The manuscript does not state whether the baseline rows for ThinkPrune, Dynasor, and DEER were re-run under the same evaluation protocol or taken from the original papers. Since decoding settings such as temperature and maximum token count can substantially affect both accuracy and token usage, please clarify this point.
  4. [Appendix B] The prompt used for intermediate answer generation (step 3 in Figure 3) is not included, and the rejection-sampling procedure is described only briefly. Without these details, the SFT data construction cannot be fully reproduced.
  5. [Table 5] The word-frequency counts in Table 5 are not normalized by the number of examples, which makes cross-benchmark comparisons difficult to interpret. Please report per-example or normalized frequencies.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the efficiency gains and accuracy results are measured outcomes of an SFT-plus-GRPO pipeline, not quantities fitted from the benchmarks or forced by a self-citation chain.

full rationale

The paper's derivation chain is empirical and self-contained. The unit-level redundancy rate (Equation 5) is a diagnostic measurement on original LRM traces; it is not used to fit any parameter of MinD, and the paper explicitly notes that the 68.1% token reduction 'aligns well, though not directly' with the 69.8% redundancy rate rather than equating them. The SFT data transformation (Figure 3) uses GPT-4o to segment CoTs and complete intermediate answers; this is a data-format conversion, not a prediction derived from the same benchmark numbers. The GRPO reward (Equation 7, Table 1) contains no explicit length or turn-count penalty, and the paper states 'we do not introduce an explicit reward term regarding the number of turns'; the token and TTFT reductions are therefore measured outcomes of training rather than enforced by construction. The Runit heuristic is a hand-set structural regularizer, not a fitted parameter. The only overlap with the authors' own prior work is the Dynasor baseline (reference [6], co-authored by Hao Zhang), which is used as a comparison point rather than as a justification for MinD's design, so it is not load-bearing. Concerns about small OOD test sets (AIME24, AMC23, GPQA-Diamond) and absent error bars are statistical robustness issues, not circularity. No equation in the paper reduces to its own inputs, and no fitted parameter is renamed as a prediction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The main free parameters are reward weights and GRPO settings; the key assumptions are about the reliability of GPT-4o segmentation and the sufficiency of prefix answers. No new physical or model entities are postulated.

free parameters (3)
  • Reward weights (R_format, R_accuracy, R_unit) = +1/-1, +2/-2, -0.3/0
    Hand-chosen in Table 1; no sensitivity analysis is provided, and the unit compactness penalty (-0.3) is central to preserving the multi-turn structure.
  • GRPO training settings (learning rate, epochs, rollouts) = 1e-6, 1 epoch, 10 rollouts
    Section 4.1; chosen without reported tuning or ablation.
  • Threshold for intermediate answer correctness in URR = not specified
    Equation (5) requires judging whether a prefix yields a correct final answer; the matching rule (exact match vs. semantic) is not stated.
assumptions (4)
  • domain assumption GPT-4o reliably segments CoT traces into discrete thinking units, and the intermediate answers generated for prefixes are plausible completions.
    Section 3.2 and Appendix B; the entire SFT data pipeline depends on this.
  • domain assumption A prefix that yields a correct intermediate answer is a sufficient stopping point; later units are redundant.
    Equation (5) defines unit-level redundancy; this assumes correctness of the prefix alone is the right measure of sufficiency.
  • domain assumption GRPO's per-token normalization creates an implicit bias toward shorter outputs that is strong enough to reduce turn count without an explicit length reward.
    Section 3.3 relies on prior observations; the paper validates this empirically in Figure 5 but does not isolate the mechanism.
  • domain assumption The multi-turn format preserves the model's reasoning capabilities after SFT.
    Section 4.2; if the format degrades reasoning, the efficiency gains are partially a trade-off against quality.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured Multi-Turn Decomposition." pith.science (2026). https://pith.science/paper/DSDBGVVL

@misc{pith2026250519788,
  author       = {Pith},
  title        = {Pith review of: Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured Multi-Turn Decomposition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DSDBGVVL}},
  note         = {Machine review of arXiv:2505.19788}
}
read the original abstract

Large Reasoning Models (LRMs) are criticized for the excessively lengthy Chain-of-Thought (CoT) to derive the final answer, suffering from high first-token and overall latency. Typically, the CoT of LRMs mixes multiple thinking units; each unit attempts to produce a candidate answer to the original query. Hence, a natural idea to improve efficiency is to reduce the unit number. Yet, the fact that the thinking units in vanilla CoT cannot be explicitly managed renders doing so challenging. This paper introduces Multi-Turn Decomposition (MinD) to decode conventional CoT into a sequence of explicit, structured, and turn-wise interactions to bridge the gap. In MinD, the model provides a multi-turn response to the query, where each turn embraces a thinking unit and yields a corresponding answer. The subsequent turns can reflect, verify, revise, or explore alternative approaches to both the thinking and answer parts of earlier ones. This not only makes the answer delivered more swiftly, but also enables explicit controls over the iterative reasoning process (i.e., users may halt or continue at any turn). We follow a supervised fine-tuning (SFT) then reinforcement learning (RL) paradigm to realize MinD. We first rephrase the outputs of an LRM into multi-turn formats by prompting another LLM, and then tune the LRM with such data. Observing that the tuned model tends to consume even more tokens than the original one (probably due to that the multi-turn formats introduce additional answer tokens), we advocate leveraging RL algorithms like GRPO to prioritize correct outputs with fewer turns. Trained on the MATH dataset using R1-Distill models, MinD can achieve up to ~70% reduction in both output token usage and time to first token (TTFT), while maintaining competitive performance on reasoning benchmarks such as MATH-500, AIME24, AMC23, and GPQA-Diamond.

Figures

Figures reproduced from arXiv: 2505.19788 by the authors.

Figure 1
Figure 1. An illustration of responses from DeepSeek-R1-Distill-Qwen-7B and the transformed [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Left: An example of a standard CoT from DeepSeek-R1, naturally containing multiple discrete thinking units (the start of each new unit is marked with an orange highlight). Right: Empirical analysis of unit-level redundancy, which is calculated based on Equation (5), in R1-distilled models on the MATH-500 dataset, showing an average redundancy rate of 69.8% for the 1.5B model and 35.8% for the 7B model. where t denot… view at source ↗
Figure 3
Figure 3. Transforming think-then-answer LRMs into a multi-turn reasoning paradigm, consisting [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: The distribution of reasoning turns for MinD at [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Left: Comparison of GRPO training with and without Runit on MATH-500 for different 1.5B model checkpoints, showing Average Output Tokens for each. Removing Runit leads to instability and collapse in output length. Right: An illustrative case comparing the outputs of GR…

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fewer Tokens, Smaller Cache: Reward-Coordinated Efficient Reasoning

    cs.AI 2026-08 conditional novelty 6.0 of 10

    ReCo coordinates KV-cache compression, reflection-token logit penalties, and confidence-based early stopping under one per-step process reward, reducing tokens and latency while largely preserving accuracy.

  2. Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey

    cs.AI 2025-07 conditional novelty 3.0 of 10

    A comprehensive review that categorizes methods for shortening and adaptively triggering chain-of-thought reasoning in large language models.

Reference graph

Works this paper leans on

37 extracted references · 14 canonical work pages · cited by 2 Pith papers

  1. [1]

    L1: Controlling how long a reasoning model thinks with reinforcement learning, 2025

    Pranjal Aggarwal and Sean Welleck. L1: Controlling how long a reasoning model thinks with reinforcement learning, 2025

    2025

  2. [2]

    Over-reasoning and redundant calculation of large language models, 2024

    Cheng-Han Chiang and Hung yi Lee. Over-reasoning and redundant calculation of large language models, 2024

    work page 2024

  3. [3]

    Training verifiers to solve math word problems.arXiv preprint arXiv:2110.14168, 2021

    Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems.arXiv preprint arXiv:2110.14168, 2021

    arXiv

  4. [4]

    Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025

    DeepSeek-AI. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025

    2025

  5. [5]

    Open r1: A fully open reproduction of deepseek-r1, January 2025

    Hugging Face. Open r1: A fully open reproduction of deepseek-r1, January 2025

    2025

  6. [6]

    Reasoning without self-doubt: More efficient chain-of-thought through certainty probing

    Yichao Fu, Junda Chen, Yonghao Zhuang, Zheyu Fu, Ion Stoica, and Hao Zhang. Reasoning without self-doubt: More efficient chain-of-thought through certainty probing. InICLR 2025 Workshop on Foundation Models in the Wild, 2025

    2025

  7. [7]

    Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.arXiv preprint arXiv:2501.12948, 2025

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.arXiv preprint arXiv:2501.12948, 2025

    arXiv 2025

  8. [8]

    Training large language model to reason in a continuous latent space, 2025

    Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason E Weston, and Yuandong Tian. Training large language model to reason in a continuous latent space, 2025

    work page 2025

Show all 37 references
  1. [9]

    Thinkprune: Pruning long chain-of-thought of llms via reinforcement learning, 2025

    Bairu Hou, Yang Zhang, Jiabao Ji, Yujian Liu, Kaizhi Qian, Jacob Andreas, and Shiyu Chang. Thinkprune: Pruning long chain-of-thought of llms via reinforcement learning, 2025

    2025

  2. [10]

    Openai o1 system card.arXiv preprint arXiv:2412.16720, 2024

    Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. Openai o1 system card.arXiv preprint arXiv:2412.16720, 2024

    2024 arXiv

  3. [11]

    Prompt-based length controlled generation with multiple control types, 2024

    Renlong Jie, Xiaojun Meng, Lifeng Shang, Xin Jiang, and Qun Liu. Prompt-based length controlled generation with multiple control types, 2024

    2024

  4. [12]

    Search-r1: Training llms to reason and leverage search engines with reinforcement learning, 2025

    Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Za- mani, and Jiawei Han. Search-r1: Training llms to reason and leverage search engines with reinforcement learning, 2025

    2025

  5. [13]

    C3ot: Generating shorter chain-of- thought without compromising effectiveness

    Yu Kang, Xianghui Sun, Liangyu Chen, and Wei Zou. C3ot: Generating shorter chain-of- thought without compromising effectiveness. InProceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 24312–24320, 2025

    2025

  6. [14]

    Let’s verify step by step.arXiv preprint arXiv:2305.20050, 2023

    Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s verify step by step.arXiv preprint arXiv:2305.20050, 2023

    2023 arXiv

  7. [15]

    Cppo: Accelerating the training of group relative policy optimization-based reasoning models.arXiv preprint arXiv:2503.22342, 2025

    Zhihang Lin, Mingbao Lin, Yuan Xie, and Rongrong Ji. Cppo: Accelerating the training of group relative policy optimization-based reasoning models.arXiv preprint arXiv:2503.22342, 2025

    2025

  8. [16]

    Understanding r1-zero-like training: A critical perspective, 2025

    Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin. Understanding r1-zero-like training: A critical perspective, 2025. 10

    2025

  9. [17]

    O1-pruner: Length-harmonizing fine-tuning for o1-like reasoning pruning, 2025

    Haotian Luo, Li Shen, Haiying He, Yibo Wang, Shiwei Liu, Wei Li, Naiqiang Tan, Xiaochun Cao, and Dacheng Tao. O1-pruner: Length-harmonizing fine-tuning for o1-like reasoning pruning, 2025

    2025

  10. [18]

    American invitational mathematics examination - aime 2024, 2024

    Mathematical Association of America. American invitational mathematics examination - aime 2024, 2024

    2024

  11. [19]

    Australian mathematics competition - amc 2023, 2023

    Australian Academy of Science. Australian mathematics competition - amc 2023, 2023

    2023

  12. [20]

    OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Flo- rencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haiming Bao, Mohammad Bavarian, Jef...

    2024

  13. [21]

    Specrea- son: Fast and accurate inference-time compute via speculative reasoning.arXiv preprint arXiv:2504.07891, 2025

    Rui Pan, Yinwei Dai, Zhihao Zhang, Gabriele Oliaro, Zhihao Jia, and Ravi Netravali. Specrea- son: Fast and accurate inference-time compute via speculative reasoning.arXiv preprint arXiv:2504.07891, 2025. 11

    2025 arXiv

  14. [22]

    Gpqa: A graduate-level google-proof q&a benchmark.arXiv preprint arXiv:2311.12022, 2023

    David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman. Gpqa: A graduate-level google-proof q&a benchmark.arXiv preprint arXiv:2311.12022, 2023

    2023 arXiv

  15. [23]

    Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y . K. Li, Y . Wu, and Daya Guo. Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024

    2024

  16. [24]

    Hawkeye:efficient reasoning with model collaboration, 2025

    Jianshu She, Zhuohao Li, Zhemin Huang, Qi Li, Peiran Xu, Haonan Li, and Qirong Ho. Hawkeye:efficient reasoning with model collaboration, 2025

    2025

  17. [25]

    Efficient reasoning with hidden thinking, 2025

    Xuan Shen, Yizhou Wang, Xiangxi Shi, Yanzhi Wang, Pu Zhao, and Jiuxiang Gu. Efficient reasoning with hidden thinking, 2025

    2025

  18. [26]

    Hybridflow: A flexible and efficient rlhf framework.arXiv preprint arXiv: 2409.19256, 2024

    Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chuan Wu. Hybridflow: A flexible and efficient rlhf framework.arXiv preprint arXiv: 2409.19256, 2024

    2024 arXiv

  19. [27]

    R1-searcher: Incentivizing the search capability in llms via reinforcement learning.arXiv preprint arXiv:2503.05592, 2025

    Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, and Ji-Rong Wen. R1-searcher: Incentivizing the search capability in llms via reinforcement learning.arXiv preprint arXiv:2503.05592, 2025

    2025 arXiv

  20. [28]

    Token assorted: Mixing latent and text tokens for improved language model reasoning, 2025

    DiJia Su, Hanlin Zhu, Yingchen Xu, Jiantao Jiao, Yuandong Tian, and Qinqing Zheng. Token assorted: Mixing latent and text tokens for improved language model reasoning, 2025

    2025

  21. [29]

    Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, et al. Kimi k1. 5: Scaling reinforcement learning with llms.arXiv preprint arXiv:2501.12599, 2025

    2025 arXiv

  22. [30]

    Qwen3, April 2025

    Qwen Team. Qwen3, April 2025

    2025

  23. [31]

    Chain-of-thought prompting elicits reasoning in large language models

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems, volume 35, pages 24824–24837, 2022

    2022

  24. [32]

    Tokenskip: Controllable chain-of-thought compression in llms.arXiv preprint arXiv:2502.12067, 2024

    Heming Xia, Weilin Wang, Han Yu, Xin Wang, Xiangning Lin, and Ming Zhou. Tokenskip: Controllable chain-of-thought compression in llms.arXiv preprint arXiv:2502.12067, 2024

    2024

  25. [33]

    Dynamic early exit in reasoning models, 2025

    Chenxu Yang, Qingyi Si, Yongjie Duan, Zheliang Zhu, Chenyu Zhu, Zheng Lin, Li Cao, and Weiping Wang. Dynamic early exit in reasoning models, 2025

    2025

  26. [34]

    Towards thinking-optimal scaling of test-time compute for llm reasoning, 2025

    Wenkai Yang, Shuming Ma, Yankai Lin, and Furu Wei. Towards thinking-optimal scaling of test-time compute for llm reasoning, 2025

    2025

  27. [35]

    Dapo: An open-source llm reinforcement learning system at scale, 2025

    Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, Haibin Lin, Zhiqi Lin, Bole Ma, Guangming Sheng, Yuxuan Tong, Chi Zhang, Mofan Zhang, Wang Zhang, Hang Zhu, Jinhua Zhu, Jiaze Chen, Jiangjie Chen, Chengyi Wa...

    2025

  28. [36]

    Draft & verify: Lossless large language model acceleration via self-speculative decoding

    Jun Zhang, Jue Wang, Huan Li, Lidan Shou, Ke Chen, Gang Chen, and Sharad Mehrotra. Draft & verify: Lossless large language model acceleration via self-speculative decoding. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors,Proceedings of the 62nd Annual Meeting of the ...

    2024

  29. [37]

    [split]” between every two consecutive Reasoning Rounds. –- Problem: {question} Solution: {prediction} –- Please give the solution with “[split]

    Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. Llamafactory: Unified efficient fine-tuning of 100+ language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System ...

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.