Your Models Have Thought Enough: Training Large Reasoning Models to Stop Overthinking
Authors: Jinyi Han
Ying Huang
Ying Liao
Haiquan Zhao
Zishang Jiang
Xinyi Wang
Xikun Lu
Guanghao Zhou
Sihang Jiang
Jiaqing Liang
Weikang Zhou
Zeye Sun
Fei Yu
Yanghua Xiao
Venue: ICLR 2026 Poster (2026)
Keywords: Large Language Models
Efficient Reasoning
Overthink
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Large Reasoning Models (LRMs) have achieved impressive performance on challenging tasks, yet their deep reasoning often incurs substantial computational costs. To achieve efficient reasoning, existing reinforcement learning methods still struggle to construct short reasoning path during the rollout stage, limiting effective learning. Inspired by Evidence Accumulation Models, we find that LRMs have accumulated sufficient information early in reasoning, making further reasoning steps redundant. Based on this insight, we propose Just-Enough Thinking (JET), which trains models to proactively terminate unnecessary reasoning. JET performs trajectory truncation during rollout to expose the model to short, distributionally consistent reasoning paths. Besides, it uses a quality-controlled length reward to better encourage concise reasoning while maintaining correctness. Extensive experiments demonstrate that JET significantly improves reasoning efficiency without sacrificing accuracy. In particular, JET delivers a 4.6% accuracy improvement while reducing the output length by 46.3% on the Olympiad benchmark using DeepSeek-R1-Distill-Qwen-1.5B. Our code is available in the GitHub.

Published as a conference paper at ICLR 2026
YOUR MODELS HAVE THOUGHT ENOUGH: TRAINING LARGE REASONING MODELS TO STOP OVERTHINKING
Jinyi Han 9, Ying Huang ? Ying Liao Zishang Jiang 9, ,iikun Lu Haiiuun Zhao
Xinyi Wang*, Guanghao Zhou Sihang Jiang * Weikang Zhou
Zeye Sun & Fei Yu * Yanghua Xiao .Jiaqing Liang 4
"Shnnghai Institute of Artificial Intelligence for Education, East China Normal University
School of Data Science, Fudan University
School of Data Science and Engineering, East China Normal University
College of Computer Science and Artificial Intelligence, Fudan University + Ant Group
ABSTRACT
Large Reasoning Models (LRMs) have achieved impressive performance chal- lenging tasks, yet their deep reasoning often incurs substantial computational costs. To achieve efficient reasoning, existing reinforcement learning methods still strug- gle to construct short reasoning path during the rollout stage, limiting effective learning. Inspired by Evidence Accumulation Models, we find that LRMs have accumulated sufficient information early in reasoning, making further reasoning steps redundant. Based this insight, we propose Just-Enough Thinking (JET), which trains models to proactively terminate unnecessary reasoning. JET performs trajectory truncation during rollout to expose the model to short, distributionally consistent reasoning paths. Besides, it uses a cunttly--ontoolled length reward to better encourage concise reasoning while maintaining correctness. Extensive exper- iments demonstrate that JET significantly improves reasoning efficiency without sacrificing accuracy. In particular, JET delivers a 4.6% accuracy improvement while reducing the output length by 46.3% on the Olympiad benchmark using 1.5B. Our code available in the GitHub
DeepSeek- -R1 R1-Distiii
INTRODUCTION
Large Reasoning Models (LRMs) have achieved substantial performance gains on highly structured and complex reasoning tasks, such as mathematical problem solving (Shao et al., 2024) and competi- tive programming (Jiang al., 2024). Their reasoning process involves elaborate intermediate steps, reflective selffverification, and exploring alternative solution strategies (Min et al., 2024). These developments are widely regarded as an approximation of human System-2 cognitive processes (Qu al., 2025a; Li et al., 2025). Models including OpenAl ol (OpenAl, 2025) and DeepSeek-RI (Guo et al., 2025) empirically demonstrate this capability in practice.
Despite these advances, the System-2 style reasoning of LRMs incurs substantial computational costs. LRMs frequently perform more reasoning steps than necessary and consuming excessive computational resources to reach correct answers, phenomenon (Chen et al., 2024; Luo et al., 2025b). This contrasts sharply with the efficiency and adaptability of human cognition. Therefore, developing methods that maintain high accuracy while improving computational efficiency has become a central challenge for LRMs.
refer to as "overthinking"
To address the challenge of reasoning efficiency, Reinforcement Learning (RL) has emerged as a promising paradigm (Hu et al., 2025b; Liu et al., 2025b). The core idea is to use additional reward signals to guide model generation foward answers that are both correct and concise. Existing approaches fall into two main categories. (i) adaptive thinking mode selection methods equip models with multiple reasoning modes (think/no-thinkk via Supervised Fine- Fine-Tuning (SFT), and then use RL with thinking rewards to select the most suitable mode for each problem (Zhang et al., 2025; Wu
Corresponding authors https:/gtthhb.com///yiiian///nn -nough-Thinkk

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Published as a conference paper at ICLR 2026
al., 2025a; Huang al., 2025a). (ii) length-based optimization approaches introduce explicit length rewards to encourage more concise reasoning (Team et al., 2025; Gao et al., 2025).
Effective reward-based methods depend exposing the model to diverse samples, yet LRMs naturally favor verbose outputs, rarely generating short reasoning trajectories. This inherent verbosity biases the training data, as shown in the Figure 1a, leading to flawed reward signal that fails encourage concise reasoning (Wu et al., 2025b). A straightforward solution is to compress long answers or provide external short answers. Yet, such interventions introduce a significant distribution mismatch between the model's natural generation distribution and the artificially shortened samples, which destabilizes gradient updates and impairs learning within the model's own generative process (Huang et al., 2025b).
To overcome these limitations, it's necessary to identify short, distributionally consistent reasoning trajectories from the model's 's own long reasoning chains. Inspired by Evidence Accumulation Models (Lee & Cummins, 2004) in cognitive science, which describe that human decision- meeisinn-making is a dynamic process where information is integrated until threshold is reached, after which further evidence serves only to support the decision. hypothesize that LRM reasoning operates similarly, The early part of reasoning trajectory already contains enough information to determine the final answer, and continues to generate results redundant reasoning. Our pilot experiments also support this hypothesis.
Building on this core insight, we propose Just-Enough Thinking (JET), a method that trains models to proactively terminate unnecessary thinking. Specifically, JET operates through two key components. First, we additionally perform trajectory truncation during RL rollout, exposing the model to reasoning paths of varying lengths while keeping them aligned with the model's natural generation. Second, we introduce a quality-controlle each reasoning group. if multiple trajectories reach the correct answer, we use the shortest correct trajectory as reference and assign length reward to all correct trajectories, where the reward decreases gradually with trajectory length.
length reward to better guide the model toward efficiency. For
Our main contributions are as follows:
Inspired by the Evidence Accumulation Models, we propose Just-Enough Thinking (JET), which leverages trajectory truncation to expose the model to reasoning paths of different lengths during rollout, and length-aware rewards guide more efficient reasoning.
We show empirically that LRMs accumulate most of the necessary information early in the reasoning process. Truncating rollouts allows us construct short reasoning paths that stay aligned with the model's natural generation distribution, providing a principled basis for training JET.
We provide theoretical analysis showing that two-stage truncation strategy preserves effective learning signals, allowing the policy model to learn reliably from shortened reasoning trajectories.
Extensive experiments demonstrate that JET delivers strong gains in efficient reasoning complex tasks. On Olympiad, for example, JET improves accuracy by 4.6% while reducing output length by 46.3% on DeepSeek- Disttlisttllllai Distill-Qwen--le -wen- The model's proactive stop-thinking behavior also generalizes well to other reasoning benchmarks.
2 PIL.TT EXPERIMENTS
LRMs often produce long multi-step reasoning sequences, but it remains unclear whether the full sequence is necessary to arrive at the correct answer. In this section, we investigate whether the model accumulates sufficient information during the initial reasoning steps to produce correct answers. Therefore, conduct pilot experiments the MATH500 dataset with DeepSeek-Distill Qwen-7B. We mainly evaluate how limiting the model to only the early portion of its reasoning sequence affects answer correctness.
2.1 TASK DEFINITION
Let M be a large reasoning model. For a given problem E Q M generates sequence of intermediate reasoning states S = [81, ..,.....e 81.] and produces final answer Oq. where I. denotes
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Published as a conference paper at ICLR 2026
TAver Lokgn
2400
Lengga
2.20
2000
ToMAn Lengghh
Pr Seaaty
50
55
loea
000
Smalll Proportion
20.0%
2000
300
203
3003
2500
0003
100
625
Teken
Traxctiin Bullo
O.fffett
comprsaaion
Figure 1:
The token length distribution of 500 answers generated by DeepSeek-RI-Distill Qwen-7B on math problem. Answers shorter than 1,000 tokens are extremely rare, showing that LRMS hard to produce short answers their own. b. The effect truncation ratios on the -afeeeek-R--ssii----e-eeee
Accuracy Retention Ratio and token compression for the the MATH500 dataset. c. The effect of truncation ratios on the Error Correction Ratio and token compression for wrong answers.
model on
the total length of reasoning steps. Following the standard autoregressive generation paradigm, this process is formulated as:
P(S, og q) = II P(s, (2.) P(o, q. S.).
(I)
We define a truncated reasoning sequence S as the first [T intermediate reasoning process of the full sequence, where s((T) [$1. 82, S 1 T [0, 1). At the truncation point, forced stop-thinking signal žstop is inserted, instructing the model to stop thinking and produce a final answer A(T) based on the available intermediate reasoning steps.
6 P(og s Zuog);
To quantify the model's performance under truncation, we use three complementary metrics that capture correctness and efficiency:
Accuracy Retention Ratio (ARR). It measures the proportion of originally correct problems that
remain correct after truncation. Let Qcorree = { Q o, = o be the set of problems correctly solved with the full reasoning sequence, where og is the ground-truth answer. Then
ARR(T)
Σ
I(a(T) =
Qeorrett
QE
Error Correction Rate (ECR). It measures the proportion of initially incorrect problems that become
correct after truncation. Let Qincorrett { Q 0g F 0 be the set of problems that are incorrectly solved with the full reasoning sequence. Then
ECR(T)
1
I(06 (T)
0g),
(4)
Qincomeet
Qussmmn
where of the answer after truncating the reasoning sequence.
Length Compression Ratio. It quantifies the reduction in reasoning tokens achieved by relying solely on the truncated prefix to produce the final answer.
|S T
LCR(T)
(5)
J.q]
2.2 PILOT EXPERIMENTS AND ANALYSIS
We first establish baseline by allowing the model generate complete reasoning traces and its final
answers. We then truncate the reasoning process, retaining only the first 75%, 50%, and 25% of the original reasoning chain, and insert stop-thinking cue, like "Wait, have enough information to
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Published as a conference paper at ICLR 2026
get the final answer. Therefore, the final answer is..." (as shown in the left of Figure 2), to prompt the model to produce the final answer from the partial reasoning. We evaluate its performance under these conditions.
LRMs retain most of their accuracy even when large portions of the reasoning chain are removed. As shown Figure 1b, restricting the model the first 75% of its reasoning preserves over 90% of the originally correct solutions, and using only the first half still yields correct answers on roughly three-quarters of those problems. Even with just quarter of the reasoning, nearly half of the originally correct solutions survive. This indicates that the essential problem-solving information is accumulated early and later steps contribute little correctness.
Furthermore, truncating reasoning trajectories can turn some initially incorrect answers into correct ones, effectively mitigating the "overthinking" problem. Figure Ic shows that progressively short- ening the reasoning chain increases the fraction of previously incorrect solutions that are corrected. Redundant or excessive reasoning steps sometimes mislead the LRMs. Limiting the reasoning process appropriately improves overall answer accuracy.
Truncation also yields substantial reductions in token consumption. Full reasoning requires an average of about 2,391 tokens per problem. Retaining only the first three-quarters of the reasoning chain reduces token consumption by roughly 25% with almost no accuracy loss. Even when using only the first 25% of the chain, token usage drops to about 75% of the original. Although accuracy decreases at this point, the model still delivers reasonable performance at a significantly lower computation cost.
Highlights
LRMs accumulate most of the erucial problem-solving information early in their reasoning process, allowing them to maintain high accuracy while significantly reducing the number of reasoning tokens.
3 METHODS
Motivated by our observation that LRMs accumulate most problem-solving information in the early stages of reasoning, we introduce JET, a reinforcement learning approach based on DAPO (Yu et al., 2025). JET trains LRMs to stop reasoning once sufficient information has been gathered, enabling them to produce accurate answers and concise reasoning steps.
3.1 TWO-STAGE ROLLOUT CONSTRUCTION
To expose the model diverse reasoning behaviors and facilitate efficient policy learning, we propose a two-stage rollout construction strategy, illustrated in Figure 2. This approach generates both full and truncated reasoning trajectories, enabling the model to balance correctness and brevity during reinforcement learning.
Stagel: Full Reasoning In the first stage, the model generates complete reasoning trajectories through its standard autoregressive process. These full trajectories capture all intermediate steps and the final answer, providing a comprehensive reference the model's 's natural reasoning behavior. They serve a foundation for constructing truncated trajectories in the next stage and allow sampling of diverse reasoning paths for RL rollouts.
Stage2: Trajectory Truncation This stage constructs shorter reasoning paths by truncating full trajectories. Building the full trajectories from Stage 1, we construct shorter reasoning paths by truncating at various intermediate steps. At each truncation point, we insert an explicit stop-thinking cue that instructs the model to immediately output conclusion, rather than continuing further. This ensures that truncated trajectories remain consistent with the model's generation distribution while introducing diverse early-stopped reasoning paths.
Identify the Truncation Position. Determining the optimal truncation point is non-trivial. truncating too carly may produce incorrect answers, while truncating too late adds redundant steps. Exhaustive search is computationally costly and reduces RL efficiency. To address this, we introduce Progressive Farly-Stopping (PES), which generates sequence of truncated variants along each full trajectory,
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Published as a conference paper at ICLR 2026
How many positive whole number divisors does 196 have?
Stage1
Full reasening path
Pull
Truncated trajectory
I'll stort by finding its prims
factorization.
First, divide 196 by 2...
Stage2
First, divide by
Next, divide 49 by 7...
("step-tinkknng
q
stop-thinking
O2
(Omit ressoning stope)
get the findl arower:
Weit. have enough information
q
thinking
Therefore, the final onswer
Stop--hinkkngg
ToTal Takens: 2480
Tokenss 1326
Truncated trajectory
Figure 2: Left: An example a truncated reasoning trajectory; Right: The process of Two-stage Rollout Construction.
formalized as:
lo A&L,
k= 0,0,,,.... K
(6)
where tu is the initial cutoff, At is the predefined step size or quantile interval, and K controls the number of truncations. At each cutoff tg, explicit stop-thinking cue prompts the model to immediately produce its final answer.
This progressive truncation strategy (i) preserves consistency with the model's generation distribution, (ii) likelihood of capturing an optimal or near-optimal cutting point t", and (iii) provides diverse early-stopped trajectories that guide the model to learn when halt reasoning effectively.
Together, the two stages generate diverse reasoning trajectories that guide the RL policy to reason efficiently without sacrificing accuracy. Full trajectories provide comprehensive coverage, while truncated trajectories encourage concise reasoning, enabling the model to balance correctness and brevity.
3.2 REWARD AND OBJECTIVE DESIGN
Another critical component in RL training is the design of reward system, which guides the model toward desired behaviors. Our reward mechanism consists of two main components: base reward that encourages correct and well-formatted answers, and length reward that encourages concise reasoning.
Base Reward. Following standard RL practices, the base reward combines two components: the format reward ry and the correctness reward Taco The format reward ry E {0, 1} ensures proper answer presentation by requiring final answers to be enclosed in \boxed[) tags. The correctness reward race E 10, evaluates answer accuracy through exact string matching with the ground-truth solution.
Length Reward. To encourage concise reasoning without compromising correctness, we design an uccuracy-conditione length reward based on the following principles:
Correctness first:
Only correct responses are eligible for length rewards, ensuring that accuracy remains the primary objective.
Conciseness preference: Among correct responses, shorter reasoning paths receive higher rewards, promoting brevity in reasoning.
Per-question normalization: Rewards are measured relative to the shortest and longest correct responses for each question, preventing biases caused by differing length distributions across questions.
Formally, let C = (1) = 1 denote the set of correct responses for question. Within this set, we define Lmin minjec lj and lmax maxjec lj as the lengths of the shortest and longest correct responses, respectively. The length reward for response is then:
{
Emax- Emox Lmox (miutt)) (1 +
ifieC
re(
i
Here, a controls the rate at which reward decays with length, ö E (0, 1) sets a minimum reward for the correct responses, and avoids division by zero when all correct responses have equal length.
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Total Reward. The total reward combines the contributions of correctness, formatting, and concise- ness to guide the model toward accurate, well-formatted, and concise reasoning. Formally, for sample the total reward is defined as:
R(i) wy-ry(() + Wace (1) we re(i)
where (())), (1), and re( are the format, correctness, and length rewards, respectively, and Wf, Woeer and Wy non-negative coefficients that balance their contributions. By combining these components, the reward function ensures that the model not only produces correct answers but also maintains proper formatting and favors concise reasoning paths.
Training Objective. The original DAPO algorithm optimizes the policy by sampling a set of outputs {o,) for each input query q and corresponding answer A, with the objective formulated as: (f) =B ()tt)))D((o)fEana (o:)
lo:1
(9)
min (rs, clip (0),
Slow + Shigh) Ar..)
Σ
0
no and the advantage estimate is Here, the importance sampling ratio is (0)) Tea fore 9.0),ee)?
A R: (i--menn| (.
std( (R))
Unlike standard DAPO, which computes the loss only on full reasoning trajectories, JET incorporates both full trajectories and those obtained by truncating a reasoning chain and then completing By including these truncate-aand-ooppll reasoning once sufficient information has been gathered, producing answers that are both correct and concise. The complete algorithm of JET is shown in Appendix 1.
trajectories in the objective, the policy learns to stop
3.3 GRADIENT ANALYSIS OF TRUNCATED ROLLOUTS IN JET
In JET, the truncation operation appends a short stop-thinking sentence followed by the final answer. This operation does not introduce significant discontinuities in the sequence-level probability distri- bution and thus preserves the stability of RL updates. Here, we analyze its effect under the DAPO objective (Eq. 9).
Consider trajectory (( {oi, 1, ... Og, mm,) where the first T tokens [oi,1. 0i,T) are sam- pled from the old policy Tew and the last m tokens {ou,T+1,.... mm) (PT+1,.... PT+mm
form a forced suffix with m < 'T The log- pe-probabbiity of the full trajectory under the current policy
my factorizes autoregressively as
T+m
log TH (o:) =
Σ
log mo(oia 0i.1a-1) +
Σ 1=
log mo (0,
(10)
Equivalently, it is represented
T+m
no (o.)) =o(o C, C= To( 01,12-1)
II
(11)
I=T+1
Since m < T, the suffix factor C' contributes negligibly to the overall trajectory probability.
The stability of RL updates depends token-level importance ratios (f). For prefix tokens (L < T) sampled from T the trust-region constraint ensures Ta RS Tew- so that (0) Rs 1 and the ratios remain within the clipping bounds. For the forced suffix tokens > T), although the policy may assign different probabilities, their impact is limited because (i) their importance ratios are clipped to prevent extreme updates, and (ii) their total number m small.
Consequently, assuming the forced suffix has non-negligible probability under the old policy, the sequence- importance ratio satisfies
na (0.) Ta (o:)
T+m
II (T. t=1
(0) ~ 1. t=T+1
II
(12)
This shows that appending a short forced suffix preserves the sequence-lever distribution and ensures stable policy updates within the trust region.

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Published as a conference paper at ICLR 2026
Table 1: Performance of different baselines across various math tasks. Values in parentheses under ACC indicate the accuracy change Aace relative to the Base. For each benchmark, the value under Length reports the mean token compression ratio across all samples in that benchmark. The value under the AVG column further averages these per-benchmark compression ratios to summarize the overall compression performance.
Methods
GSM8K
MATH500
AIME24
AMC
Olympiad
AVG
ACC
Length
ACC
Length
Length
ACC
Length
ACC:
Length
Length
DeepSeek-
RI-Distill
Qwen-L..B
Base
468
79.6
3617
28.7
11046
7644
47.0
7679
58,9
SET
65.4
559 (4154)
78.8 c4x:
2591 (244)
27.7 c3m
9139 (123)) 57.2
5394 (20/))
42.5 (43)
5532 (330) 57.5 6144)
3583 (:a.))
DPO
80.2
(-13.))
(267)
24.0
9966 (43) c4.0
5482 (23.5)
:40
5929 (22.0) 57.2 (37)) 3744 (249)
DARO
826 (-76-9
85.8 (46.)) 3106
(161) (01 8583 (32%) (633
6366
46.6.09) 5822 (m)h) 61,1 (+h.) 3822 (70)
AdaThink
66.0
(46.00
79.6 (4a0) 1905 (-0.3) 23.7 (510 7434 (327) 58.7
3983 (47.) 49.8 (+30) 4706
(367)) 58.8 (32) 2948 (:0.))
I [aser-D)
84.9
1073 (117
85.2 (110) 2434 (m9) 30.0
7271 (317) (1.)
4355
53.2 (447) 4113
6388-199 3221 (16)
Laser-DE
1179 (4151.0)
84.2 (+10)
2798 (324) 29.7 (+/m 7960 (279) (1..
5018
(313) 50.5 (-15) 5265
62.7 (400
(-7.)
LCRI
c5.0
4433
(533
77.6
1851 (100)) 19.0
7155 (157) 66.4
3897
(1600) :4.0
4193
$4,4 (445
2682 96:1
JET
83.8 62.
605 (22930
83.0 (42.))
2072 (4227)
(3.3)
6641 (:39)) 66.1 (2.1 3872
(48.3)
(1.6
(448)
412] (463)
63.3 (44.0)
2710 (29.))
DeepSeck.R1-D R1---still- Bieenll-Qwwen-BB
Base
87.0
469
92.0
2918
51.3
9812
78.9
6013
63.1
6782
74.5
4026
SPT
87.3
438
91.4
2568
48.7 (3:0 9814 (400)
78.6
5836
62.7
644]
73.7
3862 (53)
DPO
86.1 cath
438 (48)
90.0 c20
2590
(3.0 (11.7)
9552 (2:))
77.3 (1.))
5797 (1/)
6044.2)
6165 (42))
73/4 61.)) 3839 633)
DAPO
90.1
583
91.6
2720:40
53.3 62.0) 8414
o227
4903
63.4
(43)
5361
76.0 (-1.) 3405 (73)
AdaThink
(1. 304 (353)
8-78 1325 (S68) 50.7 (60) 8131 (171) 77.2 3871 (55 61.3
4656
73.2
2720 (318)
Laser
91.6 965 (-10500
92.0 waa 1950
52.7
6361
3505 (x38
64.7
3755
76.8
2649 (43)
Laser-DE
w1. 5 948 (-100.)
92.4 (400) (942
(3.7 5809 (111) (+1)) 3357 (117) (4.6
76.9 (+2-0) 2554 (:37)
LCRI
cuu
386
87.6
1313
(33
6329
76.5
3173 (472)
59.1
3575 (47.3)
71.8
2238 (-1.5)
IET
86.1 com) 324 13.9)
91.2 (4
2091 (7777)
(4.7) 7981 (117) (1. 4301 (34.5) 63.9 (e:)) c283
75.2
(40.0)
2999 (367)
4 EXPERIMENTS
4.1 EXPERIMENT SETUPS
Models. To assess the effectiveness of JET, adopt two representative and widely used LRMs, Deepseek-RA. --Dsstill-Qwen' and 1.5B (DeepSeek-A) AI et al., 2025), as backbone models.
Datasets. (i) Training data. We construct DAPO-MATH 2, We then remove all Chinese-language problems, resulting in 14,564 examples. (ii) Test data. We evaluate our model across diverse suite of benchmarks to assess its performance in both in-domain and out-of-domain scenarios. For in-domain mathematical reasoning, we use datasets of varying difficulty, including AIME 20243, MATH500 (Lightman al., 2023), GSMSK (Cobbe et al., 2021), AMC and Olympiad (He et al., 2024) problems. To measure out-of- -f--odm---mnn generalization, we test the model on GPQA-Diamond, CommonsenseQA, and a subset of MMLU created by sampling 2,000 problems per subject. Finally, to ensure statistical robustness, all experiments on AIME 2024 and AMC are independently repeated 10 times.
diixeeddfficully training dataset by combining MATH and
Metrics.Wa evaluate model performance using the following four metrics: Accuracy (Acc) for correctness, Output Length (Length) for conciseness, Accuracr change Aace to track performance shifts, and the token compression ratio (Eq. 5) to measure efficiency.
Baselines. In addition to backbone models, we compare JET with several efficient reasoning methods. Specifically, (1) Supervised Fine-tuning (SFT): Following OVERTHINK (Chen et al., 2024), we construct training datasets from the shortest correct answers generated during our method's 's rollout stage to fine-tune the backbones; (2) Direct Preferene Optimization (DPO): We also create preference dataset, by labeling the shortest correct answer as "chosen" and the longest as "reject"; (3) DAPO: A widely used RL algorithm for enhancing reasoning capabilities. analogous to R1; (4) AdaptThink (AdaThinke (Zhang et al., 2025): An RL algorithm that teaches reasoning models when think or think to solve a given query; (5) IC-R1 (Cheng et al.): A GRPO-based method that improves reasoning efficiency by pruning invalid steps; (6) Laser (Liu et al.. 2025a): It's also an RL-based method that promotes reasoning efficiency by using a step-function reward based on target sequence length. Detailed experimental settings and hyperparameters for these baselines are provided in the Appendix A.
htttss//huggingffac.
httpps//hwngginggaaa.cooaaaaatss covdanasets/maat-aiajaamee
https://huggnngffcc... hhans///hhggnnfaac.o.ooaarrra/a/ www.areee//AAAAM//aaaaaaaaaaaaia
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Table Generalization ability of different methods on other reasoning tasks. Values in parentheses under ACC indicate the accuracy change Aace relative to the Base. For each benchmark, the value under Length reports the mean token compression ratio across all samples in that benchmark. The value under the AVG column further averages these per-benchmark compression ratios to summarize the overall compression performance.
CSQA
EPQA-Diamonn
MMLU
AVG
Methods
ACC
Length
ACC
Length
ACC
Length
ACC
Length
Base
44,2
787
32.3
5619
43.8
1306
40.1
1370
SFT
(-20)))))
740 (40)
34.9 (+26)
5288 (-59)
44.7 (40.1)
1122 (-14.1)
42.2 (-2.1)
1227 (-877)
DS- DS-Qwen-H LSB
DPO
44.6 (-0.4)
690 (-123)
30.3 (220))
(460)
44.5 (10.7)
1143 (-12.5)
39.8 (-0.3)
1221 (-10.3)
DAPO
(-1.4)
479 (-39.)
37.9 (+5.6)
4591 (-18.)
46.5 (+1.8)
(:3.4)
43.3 (+3.2)
938 (:30.6)
AdaptThink
(-38)
887 (-127)
32.3 (40.0)
4601 (-18.1
44.5 (c20)
1224 (-6.3)
41.6 (-1.5)
1299 (33.9)
Laser-D
47.1 (+2.9) 685 (-1300)
34.3 (+2.0)
5352 (-4.8)
47.2 (+3.4)
1143 (-12.5)
42.9 (+2.)
1223 (:10.1)
Laser-DE
47.8
685 (1130)
33.8 (+1.5)
5575 (0.8
46.2 (+2.4)
1217 (-6.9
42.6 (+2.5)
1279
LCRI
(-o.)
431 (45.2)
33.8 (41.5)
3678 (:34.5)
42.7
744 (-43.))
40.3 (40.2)
802
JET
(-14))
407 (-483)
43.4 (+11.))
4182 (-25.6)
44.6 (+0.8)
715 (-45.3)
44.5 (+4.4)
806 (-39.7)
Base
63.7
631
47.5
6359
60.1
1022
57.1
1191
SFT
65,4 (-1.7)
593 (660)
45.5 (-2.))
5418 (-148)
60.2 (+0.1)
1044 (+2.2)
57.0 (-0.))
1136
DPO
65.9 (-22)
586 67.1)
49.0
5519 (-132)
61..1
947
58.3 (+1.2)
1083
DS-Qwen-
DAPO
65.7 (-2.0)
519 (.17.)
51.0
5485 (-13.))
58.9 (:13)
907 (-11.)
58.5 (+1.4)
1034 (-14.3)
Adap(Think
65,9
(15.)
(4.1)
4820 (-24.)
57.3 (28)
840 (-17.)
55.9 (:26)
962 (-19.0)
Laser-D
(-2.1 600 (4.9)
(1.5
4417 (:30.)
62.3 (42.2
1059 (43.6)
59.7(42.6)
1090 (.10.6)
Laser-DE
64.8 (-1.)) 553 (-12.)
46.5 (-1.)
3884 (-38.9)
63.4 (+3.3)
1027 (+0.5)
58.2 (+1.1)
1023 (-16.9)
LCRI
(-1.) 404 (:360)
50.0 (225)
3429 (-46.1)
56.5 (336)
611 (40.2)
$7.2 (+0.1)
700 (-205)
JET
(-27)
531 (118)
52.5 (+5.0)
5530 (-130)
63.9 (+38)
(-15.9)
60.9 (+3.8)
1013 (-14.9
4.2
MAIN RESULTS
JET achieves substantial output length reduction without compromising aceuracy, demonstrat- ing superior efficiency. Some methods such as LCR1 attain higher compression but at the cost of accuracy. For example, on MATH500 with the 7B model, LCR1 reduces length by over 50% but drops accuracy by 4.4pp, undermining the goal of efficient reasoning. In contrast, JET consistently attains large reductions, averaging 39.7% on the 1.5B model, while maintaining or even improving accuracy across tasks, reflecting more favorable efficiency and accuracy trade-off.
JET shows outstanding performance on challenging mathematical reasoning tasks. On high- level competition datasets such as AIME24 and AMC, JET provides notable gains. With the 7B model, it achieves 54.0 accuracy on AIME24 (+2.7 over Base) and 81.0 on AMC (+2.1 over Base). These improvements indicate that JET efficiently captures critical reasoning steps, reduces redundant computation, and produces higher-quality solutions. Its output is also shorter than other methods, showing a more efficient reasoning process.
JET also demonstrates a distinct advantage on simpler tasks. While other methods, such as Laser, achieve comparable performance JET some tasks, they tend to produce unnecessarily long reasoning even for easy problems. For instance. GSM8K, Laser increases token usage by 105.8%, suggesting that its dynamic difficulty-aware mechanism does not adapt effectively to tasks of varying difficulty. In contrast, JET maintains consistent balance between accuracy and output length across all task difficulties.
JET maintains stable performance across model scales and families. We further evaluate JET us- ing DeepSeek-RL DeepSeek-RI-Distilll RI-Distiil-LLlmma- as the backbone (Table 3). On 8B model. JET improves accuracy by 2 points while reducing token usage by 31% on in-domain tasks. These results demonstrate that JET 's reasoning strategy is robust and generalizes effectively across models of varying sizes.
4.3 GENERALIZATION ANALYSIS OF JET
JET exhibits robust generalization across domains and difficulty levels in commonsense rea- soning tasks, indicating that its effectiveness stems from an optimized, domain aagnosic reasoning framework rather than incidental factors. Although initially developed for mathematical reasoning, JET achieves consistently strong results on CSQA (commonsense judgment). GPQA (professional reasoning), and MMLU (multidisciplinary evaluation), underscoring its versatility.
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AMC
Olympiad
Average Token Length
4200
68
4500
0.5
ET
0.4
51
3900
0.3
64 4000
0.1
3600
60 3500
45
0.0
W/
Fix
PES
PES
90
a. Perf ormance of different rollout strategies during RL
Steps
Evolution of KL loss throughout RL training
training on AMC and Olympiad
Figure 3: Performance of different rollout strategies during the RL training. b. Evolution of KL loss throughout RL training using the DS-Distill DistilllQwenn backbone.
MATHEDO
wno PES
1300
AVE
Al MEZ4
Sigmaid Рxp
Com Time 163
800
Trainin Time
2500
2000
2003
Ours
1200
600
800
MMLUU
2000
MBK
400
800
.500
300
GPOA
9000
ANC
650
Steps
Steps
Olympiad
Figure 4: Comparison of rollout generation time and over- all RL training time with and without PES. PES accelerates RL training by producing shorter reasoning trajectories.
Figure 5: Average output token length of JET across three length-reward strategies on nine benchmarks.
The largest gains emerge the challenging GPOA-Diamond benchmark, highlighting JET 's capacity to handle complex semantic structures, a central element of its generalization. On this dataset, JET delivers +5.0 improvement with the 7B model, substantially outperforming other methods, and an even larger +1 +11.1 gain with the 1.5B model. The better performance of JET on difficult tasks indicates that JET enhances the model's ability capture deep reasoning structures, rather than relying on superficial pattern matching, and maintains high performance even in unfamiliar domains.
4.4 IMPACT OF
ANSWER DIVERSITY ON TRAINING
To explore how answer diversity from the PES strategy affects training, we compare PES with fixed-position truncation (Fix) and full reasoning without early stopping (w/o PES). The results are show Figure 3, and the additional analysis in Appendix C.8. PES improves reasoning accuracy and efficiency by generating paths of varying lengths, exposing the model to different strategies and teaching it when early stopping is beneficial. In contrast, w/o PES suffers from error accumulation in long sequences, and Fix cannot adapt problem complexity.
4.5 DISTRIBUTION CONSISTENCY DURING JET TRAINING
We examine whether JET preserves the model's native probability distribution by analyzing KL- divergence dynamics during training, using DS-Qwen-1 1.5B as the backbone and comparing with DAPO. As shown in Figure 5, JET consistently maintains lower KI. throughout training. indicating that its truncation strategy does not introduce distributional distortions. This suggests that even when reasoning is truncated, JET preserves the model's sequence-level probability structure, enabling stable reinforcement learning without deviating from the original output distribution.
4.6 EFFICIENCY ANALYSIS OF PES
PES achieves significant computational efficiency. Instead of performing costly search for the optimal truncation points, PES employs a simple and effective progressive approach to approximate the optimal reasoning length. By truncating full reasoning trajectories, PES reduces computational overhead during rollout. The shortened trajectories require fewer forward passes and enable faster gradient computation, further accelerating RL training. Compared the baseline strategy that generates full reasoning chains, PES achieves to a five-fold speedup in rollout generation and policy optimization, shown in Figure

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4.7 LENGTH REWARD DESIGN ANALYSIS
We compare our length reward with three alteraaiies:: linear (Linear), exponential decay (Exp), and Sigmoid (Arora & Zanette, 2025a). Linear assigns scores proportionally reasoning length, Exp penalizes longer outputs more sharply, and Sigmoid provides a smooth transition, moderately penaliz- ing intermediate lengths while strongly discouraging very long reasoning. Detailed formulations are in Appendix C.6, and results are shown in Figure 5. Across eight datasets, the weighted linear reward effectively reduces reasoning length. By encouraging shorter outputs while ensuring a minimum reward, it preserves essential reasoning steps and removes redundancy, achieving good balance between accuracy and efficiency (Table 4). Further analysis is provided in Appendix C.6.
5 RELATED WORK
Enhancing Deep Thinking Capability in LLMs. Unlike the rapid, heuristic-driven behavior of LLMs, slow-thinking reasoning systems enhance their capabilities by introducing deliberate and analytical reasoning (Snell et al., 2024). These approaches employ test-time scaling, enabling models to allocate more computation and time to reasoning before responding to challenging questions (Muennighoff et al., 2025b). Reinforcement Learning (RL) has become the main technique for building strong reasoning abilities in LLMs during post-training stage. OpenAl's ol model (Jaech al., 2024) was the first large-scale use of RL for deep reasoning, showing excellent performance on complex tasks. Soon after, DeepSeek-R2 -R (DeepSeek- AI et al., 2025) became the first open- source model to match ol 's performance, making these techniques available more researchers. This progress has led to many powerful long reasoning models, including Gemini 2.5 (Comanici al., 2025), QwQ (Team, 2025), and Phi-4 (Abdin al., 2024). Recent work has shown that Reinforcement Learning with Verifiable Rewards (RLVR) (Chu et al., 2025; Betley et al., 2025) can greatly improve model's performance on challenge reasoning task, especially in mathematics and programming (Shao et al., 2024; Hu et al., 2025a).
Efficient Reasoning. While detailed reasoning often leads to more correct answers, the redundant thought process of LRMs greatly increases the inference time and computational cost, a problem known as "overthinking" (Sui et al., 2025; Feng et al., 2025). Many work have proposed methods to improve reasoning efficiency from different perspectives.
One group of methods sets a fixed token budget for reasoning. They directly control the length of reasoning by setting explicit token limits (Muennighoff al., 2025a; Sun et al., 2025; Aggarwal & Welleck, 2025; Anthropic, 2025). Examples include CoT-Valve (Ma et al., 2025) and L1 (Aggarwal & Welleck, 2025). However, it is hard choose the right budget for problems of different difficulty levels. Another line of work teaches the model to adapt its reasoning length to the difficulty of the question. For example, Adarl (Luo et al., 2025a) and DAST (Shen et al., 2025) build preference datasets to train the model to decide by itself whether to use a "think" or "no-think" mode for each query (Lou et al., 2025; Zhang et al., 2025; Bai et al., 2023). Another growing body of work explores reinforcement learning to achieve efficient reasoning. Methods such as O1-Pruner (Luo et al., 2025b; Qu et al., 2025b; Dai et al., 2025), ThinkPrune (Hou et al., 2025), and Kimi (Team et al., 2025) add length-based based penalties to the reward function to encourage concise but accurate reasoning.
CONCLUSION
In this paper, we propose Just-Enough Thinking (JET), a method that trains LRMs to proactively terminate unnecessary reasoning and achieve effficintt reasoning. JET tackles the difficulty that LRMs seldom produce short reasoning paths during reinforcement learning, leading to biased training samples. Artificially constructed short answers often diverge from the model's natural probability distribution, which hinders effective learning. Inspired by Evidence Accumulation Models, we design a two-stage rollout strategy, where one stage applies trajectory truncation to construct short reasoning paths consistent with the model's natural distribution. This enables the model to observe multiple reasoning paths for the same question that differ in both length and correctness. We also introduce a controlled length reward to guide the model toward more efficient reasoning. Experiments on two representative LRMs demonstrate that JET significantly reduces output length without sacrificing accuracy, and this efficiency generalizes effectively other reasoning tasks.
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7 ACKNOWLEDGMENTS
This work was supported by Ant Group.
8 ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics. In this study, no human subjects animal experimen- tation was involved. All datasets used were sourced in compliance with relevant usage guidelines, ensuring violation of privacy. We have taken care to avoid any biases or discriminatory out- comes in our research process. No personally identifiable information was used, and no experiments conducted that could raise privacy security concerns. We are committed maintaining transparency and integrity throughout the research process.
REPRODUCIBILITY STATEMENT
We have made every effort to ensure that the results presented in this paper are reproducible. All code and datasets have been made publicly available in repository facilitate replication and verification. The experimental setup, including training steps, model configurations, and hardware details, is described in detail in the paper. Additionally, All datasets are publicly available, ensuring consistent and reproducible evaluation results. We believe these measures will enable other researchers to reproduce our work and further advance the field.
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APPENDIX
A EXPERIMENTS DETAILS
Hardware and Software Platform. All experiments are conducted on workstations equipped with four NVIDIA A800 PCIe GPUs with 80GB memory each, running Ubuntu 20.04.6 LTS. Our implementation is based on the Verl (Sheng al., 2025) framework.
Training Configuration. We generate rollouts using temperature sampling (T = 1.00 with enforced aud-of-sequence tokens, and employ vLLM for efficient batched decoding with 128 rollout slots and paged attention. During RL training, the maximum prompt length is set to 2,048 tokens, and the maximum response length is 10,000 tokens. Training is conducted for 100 steps with batch size of 64, and the actor model is optimized using the Adam optimizer with a learning rate of x 10-6.
During the rollout stage, employ a grouped sampling strategy with group size of G 12 per problem. Specifically, we first generate three complete answers. To obtain partial reasoning trajectories, the initial truncation point is set to 4 25% of the original length, the increment A/ is 25%, and we perform = 3 truncations. At each truncation point, the model required to produce a final prediction. This process yields nine short answers and three full answers, resulting in total of 12 responses per query.
For RL optimization, the clipping parameters are low of 0.2 and a high of 0.28. Reward weights are assigned as Wase 0.9, 0.1, and = 1 to balance accuracy, formatting, and output length during training.
Inference Contiguration. During inference, we set the temperature to 0.6, the maximum model length 30,000 tokens, the maximum tokens to 16,000 tokens, and top-p to 0.95.
Length Reward Design. In Section 4.7, we introduce two additional length reward strategies: the linear reward and the exponential decay reward. The linear reward defined as:
Emax -2 max Emin +5 0,
if E CC otherwise.
rr(i)
(13)
The sigmoid length reward to provide smooth penalty for reasoning length (Arora & Zanette, 2025b), which is calculated by:
l - Pz
ifi
(1)
a
Ox +
(14)
0,
otherwise,
where 4, is the token length of the i-th response, Pz = Eye and 0g = /Varzec(4)) are the mean and standard deviation of lengths over all correct rollouts for x, a()) = I+e- the sigmoid
function, a E [0, 1] is a hyperparameter, and e > 0 a small constant for numerical stability.
The exponential decay reward replaces the linear term with exponential function, and is defined as EmAx-EL
(1 -1-00 5) 5, i C.
re(i)=
Eax Emin 46
(15)
0,
otherwise.
For all experiments, the hyperparameters are fixed as a = 1.2, 5 = 0.05, and e = x 10-8
Baseline Implementation.
We provide detailed description of the baseline implementation.
For Adap(Think, LCRI, and Laser, we initially attempted to reproduce the results using the official GitHub repositories provided in their papers. However, reproduced results did not fully match the reported performance. To ensure fairness and avoid weakening the original results, we instead directly download the trained models from Hugging Face and evaluate them with the prompt configurations specified the papers, while adopting the inference parameters listed in Appendix A.
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For both the SFT and DPO baselines, sample 12 answers per problem from JET under the Two-Stage Rollout. The shortest correct answer serves as the SFT training target and the preferred response for DPO, while the longest answer is designated as the rejected response for DPO. This process yields training sets of 5.8K samples for DeepSeek-RI Distill-Qwen-7B and 5.6K samples for the 1.5B model. Both models fine-tuned with LoRA for one epoch using using a cutoff length of 4,096 tokens, learning rate of 1.0e-5, and maximum of 100,000 training samples.
For DAPO, we employ the same training data and parameter settings JET. The only modification lies in the rollout and reward configurations, where the length reward is removed and only the accuracy and formatting rewards retained.
Table 3: Performance of different baselines across various math tasks on DS- DS-Distill-Llama-S
Values in parentheses under ACC indicate the accuracy change Aace relative to the Base. For each benchmark, the value under Length reports the mean token compression ratio across all samples in that benchmark. The value under the AVG column further averages these per- berchenchmrkk compression ratios to summarize the overall compression performance.
GSMSK
MATH500
AIME24
AMC
Olympiad
AVG
ACC
I ength
ACC
Length
ACC
I Length
ACC
I Length
ACC
Length
ACC
I Length
DeepSeck- Distiil-Llama---
1756
88.0
169
8193
69.4
77.6
34.7
72.8
6249
DPO
415 6364)
78.6
3215
(453
11314 (04)
764 (05) 6450 (443)
49.5
6868
6244 c0m
4288 (21.)
DAPO
81.7 1420
1027
(-1.5)
614)
3258 (242)
40.8 (42)
9701 (a18)
77.4 was
5463 (214)
52.4 (.))
6463 (:11)
67.8 (14)
4081 (25.))
91.1 142.9
1002
(4299
6..
2922 (300))
41.0 (800
9231 6045)
76.8 :0.n 5261 (30.0 59.1 (6.7)
5822 (289; 71.4 (220
3821 (30.))
Table 4: Comparison of accuracy for three reward strategies on eight benchmark datasets.
Strategy
GSM8K
MATH500
AIME24
AMC
Olympiad
CSQA
GPQA
MMLU
AVG
Ours
83.8
83.2
32.0
66.1
51.6
45.6
43.4
44.6
56.3
Linear
82.6
82.6
31.7
64.2
50.2
45.6
38.9
45.2
55.1
Exp
85.3
84.6
33.0
63.6
52.3
46.0
43.4
45.9
56.8
Sigmoid
81.7
84.0
30.7
66.6
46.1
45.5
38.4
46.0
54.9
Table 5: Effect of accuracy (Waee) and length (wr) reward coefficients model performance.
GSMBK
MATH500
AIME24
AMC
Olympiad
AVG
Methods
ACC
Length
ACC
Length
ACC
Length
ACC
Length
ACC
Length
ACC
Length
Base
76.0
468
79.6
3617
28.7
11046
63.3
7644
47.0
7679
58.9
4765
uy 1.0, Wore
0.9
83.8
83.2
2072
32.0
6641
3872
51.6
4121
2710
ar = 0.9, Wace
1.0
82.6
726
82.2
2459
28.7
7731
66.0
4783
49.3
4852
61.8
3243
0.0,
0.9
84.2
886
84.0
2997
29.3
9822
65.9
5671
48.7
6044
62.4
3974
1.0, Wace
1.5
81.4
985
85.4
3591
30.7
10332
66.8
6727
47.7
6963
62.4
4547
Wt = 1.5, Waae
1.0
79.8
981
83.4
3553
25.7
10532
64.1
6406
48.6
6959
4482
w incorrect
78.0
871
82.4
3823
29.7
9711
64.2
7452
46.7
6997
60.2
4658
B ALGORITHM
Based on the description in Section 3. we present the pseudocode of the JET algorithm in Algo- rithm 1, which outlines its key steps and facilitates the reproducibility of our method.
C FURTHER ANALYSIS
C.I PERFORMANCE EVOLUTION DURING RL TRAINING
We track how model performance evolves throughout JET training across multiple downstream tasks for both DeepSeek- -isktiDitill--- DistilllQwenn Qwen-1.5B and 7B. Figures 6 and illustrate the main trends.
The average token length decreases significantly over training steps, while accuracy remains stable or improves, indicating that method successfully encourages concise yet accurate responses. In early
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PCA
MATH 900
COMBC
790
15.00
600
600
1530
600
5000
103
00
1600
sTep
STap
1800
4045 2014
1002
6030
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5000
63
100 22
60
K0
10 120
4000
00
10
10
2000
30
10
Figure 6: Evolution of accuracy and output length of the DeepSeek-Distill-uuee 1.5B model across benchmarks during JET training.
Per Leggs
0400
ANTH 550
Lengl
600
C504
490
0000
81
1000
30
$142
214p
AME Vokee Leggh
1000
6030
62
40300
60:0
10/00
5000
1000
3000
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2500
5000
Figure 7: Evolution of accuracy and output length of the DeepSeek-Diin Sireee--Dsssill-ee----- model across
benchmarks during JET training.
stages, both models exhibit relatively long outputs with moderate accuracy, reflecting exploration stage where the policy is still learning to balance quality and brevity. As training proceeds, output length drops sharply, especially on structured reasoning tasks such as MATH500, GSM8K, and AMC, where the average token count decreases by more than 50%. Crucially, this compression coincides with sustained or improved accuracy, indicating that shorter outputs are semantically meaningful rather than merely truncated.
The 7B model converges faster and exhibits smoother trends than the 1.5B variant. It achieves higher final accuracy and more consistent length reduction, likely due to its stronger generalization capacity and richer representations. Nonetheless, the smaller model also shows substantial improvement, confirming that the method scales effectively across model sizes.
Overall, the in-domain metrics trace a clear progression from long, low-efficiency outputs to shorter, more precise responses with stable performance. This trajectory highlights the success of our RL design in aligning model behavior with the dual goals of correctness and conciseness.
C.2 FURTHER ANALYSIS OF JET 'S PERFORMANCE ON OTHER MODELS
To further validate JET 's effectiveness, we evaluate using DeepSeek-RI-DD -------iill---aaaa--e as the backbone. Since other baselines were not evaluated with this model, we report only a subset of them, and the results ar shown in Table 3.
JET demonstrates strong generalization across backbones, maintaining stable balance between accuracy and output length. Compared to the base model, it improves accuracy by points while reducing average token usage by %. On out-of-domain tasks, JET reduces token usage by 29% while gaining 0.8 points in accuracy, showing that its efficiency and performance advantages extend to larger-scale models.
We also analyze JET on the 1.5B model by plotting accuracy versus average output length in a Pareto-style diagram ( as shown in Figure 8). Methods appearing toward the top-left of the figure
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Algorithm 1 Just-Enough Thinking (JET) Algorithm
Require: Initial policy Te, training set Q rollout size C, policy updates
Ensure: Trained policy
1: for step 1 .M do
Sample mini-batch Q. C
3: Save current policy: TT. f- 75
4: for each E 2. do
5:
Stage 1: Full trajectory rollout
6:
Generate Gtul complete reasoning trajectories. [o.t Ghall To 1
7: Stage 2: Truncated trajectory rollout
8:
for each oi and truncation ratio T do
9:
Truncate full T, append stop-thinking cue, and complete: dur maws g, 0000² zax0) end for
10:
Collect all trajectories Og and compute rewards Tace, ry: (Eq. 14)
12:
Compute token-level advantages A.. for all trajectories end for
13:
14:
for iteration = to μ. do
15:
Update policy ma by maximizing JDAPo (Eq. 9)
16:
end for
17:
end for
return IT
DS-Dist
SFT-15B
DPO-15B LCR1- 158
DAPO-15B
AdaptThink-158
Laser-D-1.5B
Laser-DE-1155
Ours
GSMBK
MATH500
Olympiad
GPQA
0.85
0.84
0.52
0.44
*
Accuracy
0.82
0.40
0.48
0.80
0.36
0.75
0.44
0.78
0.70
0.76
0.40
400
600
800
1000
2000 2400
2800 3200 3600
4000 s000 6000 7000
4000 4500 5000 5500
Average Token Length
Average Token hength
Average Taken Lergth
Average Taken Length
Figure 8: JET demonstrates strong performance across most tasks.
achieve better trade-offs between correctness and efficiency. Across most tasks, JET consistently occupies this favorable region, indicating substantial gains in efficient reasoning compared other approaches.
C.3 ANALYSIS OF PROACTIVE STOP-THINKING IN JET
To validate the adaptive reasoning capability of the JET method, we evaluate its performance on mathematical problems of varying difficulty from the MATH500 dataset, and compare it with the manual truncation method (75% cutoff). The results are shown Figure 11.
The JET method proactively stop reasoning based task difficulty, significantly reducing token consumption while maintaining high accuracy. This advantage stems from the fact that JET is trained with different reasoning lengths via reinforcement learning, allowing the model to learn the optimal reasoning depth for different tasks. In contrast to manual truncation, which uses a fixed 75% cutoff, the fixed truncation may lead to insufficient reasoning for simpler tasks and overly shallow reasoning for more complex tasks. The ability of JET to dynamically adjust reasoning depth prevents over-reasoning and ineffective reasoning during inference, enabling adaptive adjustments based on task complexity.
C.4 EFFECT OF ACCURACY AND LENGTH REWARD COEFFICIENTS
JET uses three data formats during training. Since the template only constrains the model to output the final answer in \boxed{] form and the backbone already shows strong format following. In this section, we do not study the influence of formatting on performance. Instead, this experiment focuses how the accuracy and length rewards shape model behavior during JET training. We use
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2300
2300
4000
96
Training Time (s)
w/rur
Resporre hergth
w/ cur
Aveer ageage Token Length
2600
4200
Accut ocy (99)
1500
3500
1200
2800
2100
600
1400
80
20
30
50
60
20
30
70
50
60
Step
Step
Figure 9: Impact of curriculum learning on JET training. From left to right: RL training time per step, rollout generation time, token output length, and accuracy on MATH500, all measured using the DeepSeck-Distill-Wwen. model.
DSS-Dstill-Qwen-11 Qwen-1.5B as the backbone and fixing all other RL settings, we vary the two coefficients and report the results in Table
The findings show that the length reward plays a crucial role in maintaining concise reasoning. Although PES produces short rollouts, it does not change the model's inherent preference; without the length reward, the model quickly drifts toward much longer answers.
Second, we find that the output length becomes excessively long both when the length-rewart weight is lower than the accuracy accuracy-reward reward weight and when it is higher. While the resulting trajectory lengths are similar, the underlying causes differ and both reflect forms of reward hacking. Specifically,
When the length-reward weight is lower than the accuracy-reward weight, the model primarily exploits the accuracy reward. Longer reasoning traces increase the likelihood of achieving a high accuracy score, so the model learns to extend its outputs as reliable strategy. This represents standard form of accuracy-dris -rrivnn reward hacking.
When the length-reward weight higher than the accuracy-reward weight, the accuracy reward still dominates optimization due to scale mismatch. It takes discrete values of 0 or 1, producing large and stable gradient signals, whereas the length reward is continuous and has a comparatively smaller range. As result, the gradient induced by the accuracy reward is much stronger than that from the length reward, even when its explicit weight is smaller. To avoid receiving an accuracy reward of O, the model favors longer outputs, which again reflects reward-hacking behavior on the accuracy term.
These observations demonstrate that an appropriate balance between accuracy and length rewards is essential for stable training and for preventing both forms of reward hacking.
C.5 EFFECT OF APPLYING LENGTH REWARD ONLY CORRECT ANSWERS
In this section, we investigate whether applying the length reward only to correct answers within each group leads more effective length reduction and better overall performance. We use the DS-Distill-Qwen. 1.5BB backbone and keep all training configurations identical to the main experiments. We compare two settings: one where the length reward is applied only to correct answers (ours), and another where it is applied to all answers, including incorrect ones (w/incorrect). The results are shown in Table 5.
We observe that Applying the length reward only to correct answers yields clear gains in both accuracy and output length. It achieves higher average accuracy and notably shorter responses compared the baseline, and consistently outperforms the setting that rewards all answers.
This difference arises because rewarding incorrect answers introduces conflicting learning signals. Even with zero accuracy, incorrect answers still receive length reward, causing the model to treat short incorrect and long correct answers as similarly rewarding. This weakens the intended pressure to shorten correct responses and encourages the model to produce longer outputs, form of reward hacking. Restricting the length reward to correct answers removes this interference. The gradient becomes more focused, giving the model clear incentive to shorten correct outputs. This leads to more stable learning, more effective length reduction, and improved accuracy. The results demonstrate that applying the length reward only to correct answers is crucial for achieving the desired behavior.
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Al ME24
MATH500
FOL Mawak
Average Token Length
7000
2400
Tolen Comprr casl Potto
84
Accuracy (9))
35
6500
2000
77
ARRR
28
70
30
2
1600
6000
85
Wi 0
Fix
PES
w/
Fix
PES
Level
Figure 10: Impact of PES on AIME24 and MATH500.
Figure 11: Performance of JET at different difficulty levels.
C.6 DETAILS OF THREE REWARD STRATEGIES
In the main text, we focus on the impact of the three length reward mechanisms on output length. Here, we also evaluate their effects on accuracy, with the results reported in Table
The exponential weighting reward achieves the highest average accuracy, particularly on challenging math tasks. Our weighted linear reward performs slightly lower on these tasks but remains competitive on knowledge benchmarks such as GPQA and MMLU, demonstrating stability and generalization. The standard linear reward performs worst on most tasks, indicating insufficient incentive for complex problems.
In terms length control, the linear reward is weak, leaving models prone to noise from verbose reasoning. The exponential decay reward imposes stronger penalties but can cause premature truncation, leading to incomplete reasoning and unstable accuracy on difficult tasks.
C.7 IMPACT OF CURRICULUM LEARNING ON JET TRAINING
We evaluate the effect of curriculum learning JET training using the DeepSeek-Distile DeepSeek-Distill--een--B- backbone. Figure summarizes the results.
Training without eurriculum learning demonstrates substantially higher efficiency. Each training step converges in approximately 600-800 seconds, compared to roughly 1,400 seconds per step with curriculum learning, representing nearly 50% reduction in computational overhead.
In terms of model outputs, the non-curriculum strategy produces more concise responses without compromising correctness. Both methods achieve comparable accuracy on the MATH550 dataset, whereas curriculum learning tends to generate longer answers relatively.
These observations be explained follows. Curriculum learning is designed to gradually expose the model to increasingly difficult samples, which can help in scenarios with high variability in sample difficulty when the model is prone to local optima. However, in JET setting, the training data and rollout strategy already provide diverse and informative samples across difficulty levels. As result, random sampling without curriculum learning sufficiently exposes the model to the necessary learning signals, allowing it to achieve similar or better performance with reduced training cost. Moreover, the direct exposure to diverse samples enables the model learn to produce concise yet correct answers more effectively.
C.8 FURTHER ANALYSIS OF PES
In Section 4.4, we analyze the impact of exposing the model diverse reasoning paths during training. Here, also include results AIME24 and MATH500, shown in Figure 10, and further analyze the effect PES.
Longer reasoning sequences do not always lead to better performance; they can introduce error propagation and noise that degrade final accuracy. Comparing PES with the no-PES baseline reveals counterintuitive finding: while generating complete reasoning chains might seem advantagcous, it actually underperforms PES. Across datasets varying difficulty, PES consistently surpasses all baseline strategies.
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C.9 CASE STUDIES
We present two types of cases: (i) comparisons of JET with the Base and AdaptThink models in answering questions (Cases 1), and (ii) examples where the trained JET method actively terminates its reasoning process when sufficient information has been accumulated (Cases and 3).
Case 1: Comparison with Base and AdaptThink models. In this case, the Base 7B model produces correct answer but generates an overly long response, consuming 4,087 tokens. AdaptThink produces much shorter answer, using only 636 tokens, but the response is incorrect. In contrast, JET produces a concise and correct answer with just 1,537 tokens. This demonstrates that JET effectively balances accuracy and brevity, generating answers that are both correct and substantially shorter than the baseline outpuls.
Active early stopping based confidence. During reasoning, JET detects that sufficient information has been accumulated and that its answer likely correct. The model then terminates the reasoning process early and outputs the final answer. This highlights JET 's ability to adaptively determine when to stop reasoning, reducing unnecessary computation while maintaining correctness.
Case 2:
Case 3: Early termination upon reaching the correct answer. In this case, JET identifies during reasoning that it has already obtained the correct solution. The model then terminates the reasoning process immediately and outputs the final answer. This demonstrates JET 's ability recognize when further reasoning is unnecessary. effectively reducing redundant computation while ensuring correctness.
Overall, these cases show that JET can simultaneously achieve concise, correct, and well-formatted outputs, and that its adaptive reasoning strategy allows the model terminate reasoning when sufficient information is obtained. Compared to baseline methods, JET improves efficiency, output quality, and alignment with task-specific requirements.
Case Study 1: JET VS Base AdaptThink
1* Question */
What integer n satisfies S 18 and n 11213141 (mod 18)?
/* Base Response (Total Tokens: 4087)*
<think>
Okay, have this problem where I need to find integer such that 18 and n = -11213141 (mod 18).
Hmm, okay, so I need find the equivalent positive residue of 112213141 modulo 18. That means need to figure out what - 11213114 is congruent to modulo 18, and then express it as a number between and 17 inclusive.
First, I remember that modulo operation with negative number sometimes be tricky, so maybe should start by finding the positive equivalent -11213141 modulo 18. Alternatively, I can find the remainder when 112213141 is divided by 18. But since negative numbers can complicate things, perhaps it's easier to find the modulo of the absolute value and then adjust the sign accordingly.
more reasoning tokens omitted for brevity)
Therefore, 11213141 = (mod 18), so -11213141 = -5 (mod 18), which 13
(mod 18). So, yes, = 13.
I think that's solid. So, the integer n is 13.
</think>
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To find the integer n such that S < 18 and n 111213141 (mod 18), we need to determine the equivalent positive residue of -11213141 modulo 18.
First, we compute 11213141 mod 18. We use the property that 10 = 10 (mod 18) for and 10" (mod 18).
Breaking down 11213141 digit by digit and computing each term modulo 18: Position 0: x
Position 1: x 10 40 Position 2: 10 Position
3 x 10 30 Position 4: 10 10 Position 5: 2 x 10 20 Position 6: 10 10
Position 7: x 10 = 10
Summing these results: + 40 10 30 10 20 + 10 131
Next, we find 131 mod 18: 131 + 18 = remainder
Thus, 131 (mod 18).
Therefore, 11213141 5 (mod 18). Hence, 11121314 = -5 (mod 18).
To convert 5 to a positive residue modulo 18: -5 + 18 13
Thus, the integer n that satisfies the given condition is
AdaptThink Response (Total Tokens: 636)*/
<think></think>
To find the integer n such that < n < 18 and n = - 11223114 (mod 18), we need to determine the remainder when -11213141 is divided by 18.
First, we can simplify the problem by finding the remainder when 11213141 is divided by 18, and then take the negative that remainder, adjusting it to be within the range 0 to 17 necessary.
1. Find the remainder of 11213141 divided by 18:
We can the property that the remainder of number modulo 18 the same as the sum of its digits modulo 18. This is because 18 is multiple of 9, and the sum of the digits of number is congruent to the number modulo 9. However, for modulo 18, we need to consider the alternating of digits in groups of two from the right.
2. Calculate the alternating sum of digits in groups of two: Let's break down 11213141 into groups of two from the right: 11, 21, 41. Now, compute the alternating sum: 11 21 + 31 - 41.
Calculate step-by-step: 11 21 = 10 10 + 21 21 41 -20
3. Find the remainder of 20 modulo 18:
--20 + 18 -2 with remainder. To find the positive remainder, add 18 to -20:
-20 + 18 2 (still negative, so add another 18)
--2+ 18 16
Therefore, the remainder is 16.
4. Determine the final result:
Since we initially had - -11213141 41,, the remainder when divided by 18 is the negative of the above remainder, adjusted be within 0 to 17.
--6 mod 18 2 because 16 18 2.
Thus, the integer n that satisfies 0 < n < 18 and n -11213141 (mod 18) is:
2
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/* JET Response (Total Tokens: 1537) <think> To find the integer such that 0 S 18 and n -11213141 (mod 18), I'll start by simplifying problem.
First, I recognize that finding n modulo 18 is equivalent to finding the remainder when - -11213141 is divided by 18.
Instead of dealing with the negative number directly, can find the remainder of 11213141 when divided by 18 and then subtract that from 18 to get the equivalent positive remainder. To find the remainder of 11213141 divided by 18, I'll use the property that a number is congruent to the sum of its digits modulo 9. Since 18 is multiple of 9, this method is applicable.
Calculating the of the digits of 11213141:
1 + 1 + + 1 + 4 14. Now, 14 modulo is 5, which means 11213141 = 5 (mod 9).
Since 18 is twice 9, need to find number that is 5 modulo 9 but also adjusts for the fact that we're dealing with modulo 18.
Considering 11213141 - (mod 9), which simplifies (mod 9) because -5
4.
To find the corresponding modulo 18, can use the fact that 4 and 18 share the same remainder when divided by 9. Thus, 11221314 Therefore, the integer n that satisfies the given condition is 4. </think> To find the integer satisfying 0 n 18 and 111213141 (mod 18), we can compute -11213141 modulo 18.
(mod 18).
First, note that 11213141 modulo 18 equivalent to finding the remainder when 111213141 is divided by 18. Since negative remainders can be converted positive by adding 18, we can calculate:
11213141 mod 18 (18 (11213141 mod 18)) mod 18
Calculating 11213141 mod 18, find that 11213141 leaves remainder of when divided by 9 (since the sum of its digits is 14, and 14 mod 9 = 5).
( ... more reasoning tokens omitted for brevity)
Therefore, the correct final answer is 13
Case Study 2: Active Shop-Thinking of JET
** Question */
Convert the point (0, 3) in rectangular coordinates to polar coordinates. Enter your answer in the form (r: f), where r > 0 and ( < 0 < 2x.
1 JET Response */
<think>
Okay, need convert the rectangular coordinate (0,3) to polar coordinates. Hmm,
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remember that polar coordinates are represented as (r, 0), where is the radius and the angle.
First, let me recall the formulas for conversion. think r is calculated using the distance formula, which is the square of (a² + y²). So,
r= V + y².
In this case, a and
Plugging those in,
r= v02 - 32 v / 3.
So, r = That part was straightforward.
Now, need find the angle. remember that 0 can be found using the arctangent function
tan V x'
But wait, in this case, 0. Hmm, dividing zero might be issue here. Let me think about it. When x = 0, the point lies on the y-axis. Since = 3 > 0, the point is on the positive y-axis, which should correspond an angle But just to make sure, let me visualize the point (0, 3). On the rectangular coordinate system, that's located straight up along the y-axis. So, from the positive z-axis, the angle to reach there is 90%, which is 5 radians.
radians.
So, = -
Putting it all together, the polar coordinates are (3, 5). Let me just double-check if is positive and 0 is within the required range. r = 3 > 0, and _ E [0, 2T), so that's correct. think that's don't see any mistakes here. Yeah, I'm confident with this answer.
</think>
π (3,
The polar coordinates are
2
Case Study 3: Active Shonkiignn of JET
/* Question */
What is the smallest positive perfect cube that can written as the sum of three consecutive integers?
JET Response */ <think>
Okay, so need to find the smallest positive perfect cube that can written as sum of three consecutive integers. Hmm, let's see. remember that consecutive integers numbers that come one after the other without any gaps. For example, 1, 1, or 5, 6, 7. So, the question is asking for perfect cube (like 1,8, 27, etc.) that be expressed the of such triplet.
Let me start by defining the three consecutive integers. Let's call the first integer n.. Then next two integers would be + and 2. So, the sum of these three numbers would be n + (n 1) + (n + 2). Let compute that:
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+ + 1 n + 2 3n + 3.
So, the sum simplifies to 3n + 3. I can factor that as 3(n + 1). So, the sum is three times one more than the first integer in the triplet.
Now, the sum needs to be perfect cube. Let me denote the perfect cube as k:3, where k is positive integer. Therefore, have the equation: 3(n + = k.3 From this equation, can express + as 3. Since n + 1 has to be an integer (as n is an integer), &3 must be divisible by 3. That means k itself must be multiple of 3, because if prime number divides cube, it must divide the base. So, let me let k 3m, where m is positive integer.
Substituting back, is 3m, so k. (3m)² 2.mm². Plugging this into the equation:
3(n + 1) = 27m².
Dividing both sides by 3: n+ 1+ 9.
Therefore, n 9m 1. Since n is the first integer in the triplet, it just needs to an integer, which it will be long is an integer.
Now, the sum 3(n + 1) 27m must be a positive perfect cube. The smallest positive perfect cube is 1, but let's see if that works. If 1-3 1, then 3(n + 1) which would imply - 3. But has to be an integer, that doesn't it work.
+ 1 3. Next, try k33 = 8, which is 23, Then, 3(n + 1) = 8 implics n + / so n = Again, not an integer.
Moving on to the next cube, A3 = 27. Then, 3(n + 1) 27 gives n + 1 = 9, so 8. That's an integer! Perfect. So, let's check if the sum works. The triplet would be 8, 9, 10. Adding them up: 8 + 9 + 10 = 27, which is indeed 33.
Wait a second, 1et me confirm if there's a smaller cube than 27 that satisfies the condition. The cubes are 8, 27, 64, etc. We saw that 1 and 8 don't work because they don't result in integer starting points for the triplet. So, 27 is the next one, and it works. So, 27 is the smallest such cube.
Therefore, the final answer is 27. can just write that in a box.
</think>
The smallest positive perfect cube that can be written the sum of three consecutive integers
27
D LLM USAGE
Large Language Models (LLMs) were used to aid in the writing and polishing of the manuscript. Specifically, used an LLM to assist in refining the language, improving readability, and ensuring clarity in various sections of the paper. The model helped with tasks such as sentence rephrasing. grammar checking, and enhancing the overall flow of the text.
It is important to note that the LLM was not involved in the ideation, research methodology, or experimental design. All research concepts, ideas, and analyses were developed and conducted by the authors. The contributions of the LLM were solely focused on improving the linguistic quality of the paper, with no involvement in the scientific content or data analysis.
The authors take full responsibility for the content of the manuscript, including any text generated or polished by the LLM. We have ensured that the --M-eenertted text adheres to ethical guidelines and does not contribute to plagiarism or scientific misconduct.
25


Reviews:
[{"review_id": "IANPT6kCWo", "reviewer": "Reviewer_A4Dw", "summary": "This paper proposes JET, a reinforcement learning method based on DAPO to reduce unnecessary reasoning steps in Large Reasoning Models. The approach uses Progressive Early-Stopping to create truncated reasoning trajectories during training and employs a length-aware reward to encourage concise outputs. Experiments on DeepSeek-Distill-Qwen models (1.5B and 7B) show token reductions while maintaining accuracy on math benchmarks.", "questions": "See the Weaknesses described above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper demonstrates that LRMs accumulate sufficient information early in reasoning chains, providing good empirical justification for the approach.\n\n2. JET generates shortened trajectories from the model's sampled outputs, reducing distribution mismatch issues that arise from using external short answers.\n\n3. The paper includes  evaluations across multiple benchmarks and model sizes.", "weaknesses": "1. In Section 1, line 87, I think that assigning length penalties to longer correct trajectories is problematic because the model cannot guarantee that longer sequences always contain redundant information. Although the authors use a \"Correctness first\" rule where shorter responses receive better reward signals, penalizing longer correct answers does not make much sense.\n\n2. The values of non-negative coefficients w_f, w_acc, and w_ℓ are shown in the appendix as w_acc = 0.9, w_f = 0.1, and w_ℓ = 1. More diverse strategies should be explored since these coefficients significantly influence performance.\n\n3. Since JET is implemented based on DAPO, the accuracy of DAPO should theoretically represent the upper bound for JET. However, I am confused why GSM8K improves by 3.8 points when length decreases from 826 to 605, AIME24 improves by 6 points when length decreases from 8000 to 6000, and Olympiad improves by 5 points when length decreases from 5000 to 4000.\n\n4. When the model scales from 1.5B to 7B, most baseline models decrease or slightly increase sequence length on AIME24, AMC, and Olympiad. However, JET increases sequence length from 6641 to 7981 on AIME24, 3872 to 4301 on AMC, and 4121 to 5083 on Olympiad. What explains this counterintuitive behavior?\n\n5. In Table 2 regarding language reasoning tasks, JET's sequence length is 1013, which is comparable to other baseline models, indicating that JET does not achieve expected length reduction on these tasks.\n\n6. In Table 2, why do baseline models decrease length when scaling from 1.5B to 7B, while JET increases from 800 to 1000? More analysis should be provided here.\n\n7. The paper presents performance on many benchmarks but lacks analysis of model training dynamics such as KL, policy entropy, gradient norm, etc.\n\n8. In Section 3.2, there is ambiguity: does \"ℓ_min and ℓ_max are the shortest and longest correct responses\" refer to the responses themselves or their lengths?\n\n9. In Formula 6, what would happen if the \"+ δ\" term were removed?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EldjgSnS8i", "reviewer": "Reviewer_Zu3p", "summary": "The paper proposes a method called Just-Enough Thinking (JET) that trains LRMs to proactively terminate unnecessary reasoning. Specifically, the paper first performs pilot experiments to reveal that LRMs have accumulated sufficient information early in their reasoning. Based on this phenomenon, JET adopts a Progress Early-Stopping (PES) strategy to sample reasoning trajectories with varying length and introduces a linearly normalized length reward to encourage more efficient reasoning. Experimental results show that JFT significantly improves reasoning efficiency without sacrificing accuracy on 5 math benchmarks and some other reasoning benchmarks.", "questions": "My main questions for the authors directly relate to the major concerns identified above. Addressing these points during the rebuttal period would be crucial for re-evaluating my rating.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.The paper is mostly well written and presents the topic clearly.\n\n2.The pilot studies are interesting and directly supports the design of the JET method.\n\n3.The proposed method (JET) is described in sufficient detail and appears technically sound.", "weaknesses": "Major Concerns\n\n1.Potential distributional inconsistency. The paper introduces a truncation strategy that also involves inserting an additional prompt. There is insufficient evidence to confirm that this modification does not introduce a harmful distributional inconsistency problem.\n\n2.Sub-optimal performance on math tasks. The results on mathematical reasoning tasks do not clearly demonstrate the superiority of the proposed method. As shown in Table 1, the JFT-DeepSeek-Distill-Qwen-7B model underperforms the Laser-DE model in both accuracy (-0.5%) and efficiency (using more tokens).\n\n3.Insufficient experiments. The empirical evaluation is constrained to a specific set of models (DeepSeek-Distill-Qwen-1.5B and DeepSeek-Distill-Qwen-7B). The experiments should be extended to include more recent model families (e.g., the Qwen3 series) and potentially more model sizes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OzaCnf24n7", "reviewer": "Reviewer_6BQF", "summary": "Language Reasoning Models (LRMs) generally achieve higher accuracy as their reasoning depth increases; however, this improvement often comes at the cost of generating excessive tokens, which significantly slows down inference. Even when an LRM has already gathered sufficient information to reach the correct answer, it frequently continues producing unnecessary reasoning steps — a phenomenon referred to as overthinking.  \n  \nThis paper proposes a reinforcement learning framework called Just-Enough Thinking (JET), which enables LRMs to learn efficiently while suppressing overthinking. The core of JET lies in two main components: (1) a two-stage rollout construction, and (2) a reward design based on the principles of Correctness First, Conciseness Preference, and Per-Question Normalization.  \n  \nIn the two-stage rollout construction, JET uniformly truncates the model’s full reasoning trajectories at several intervals using a strategy called Progressive Early-Stopping (PES). Because PES utilizes trajectories generated directly by the model itself, it maintains the model’s natural consistency during training. Both full trajectories and truncated trajectories are then employed in reinforcement learning, allowing the model to learn when to stop reasoning without losing accuracy.  \n  \nThe reward function prioritizes correctness above all. Among responses that are correct, shorter reasoning paths are assigned higher rewards, thereby encouraging concise and efficient reasoning.  \n  \nJET is applied to DeepSeek-Distill-Qwen-1.5B and 7B models and evaluated across five mathematical reasoning benchmarks — GSM8K, MATH500, AIME24, AMC, and Olympiad — as well as three out-of-domain datasets: CSQA, GPQA, and MMLU.  \n  \nAcross both in-domain and out-of-domain tasks, JET consistently improves accuracy while substantially reducing output token length, leading to faster inference compared with the base model.  \n  \nThe analysis further examines the effectiveness of the PES strategy and compares token length variations under different length reward designs, demonstrating the efficiency and robustness of the proposed method.", "questions": "Why did you not cite the Laser methods paper? The Laser methods generally show better average performance than the proposed JET approach on the target task (mathematical reasoning), and also perform strongly on out-of-domain benchmarks. In what aspects can JET be considered to have clear advantages over the Laser methods?  \n  \nYou argue that JET preserves the model’s natural reasoning distribution. Could you provide empirical validation for this claim? The Progressive Early-Stopping (PES) uses fixed truncation ratios (25%, 50%, 75%). Did you observe any cases where these arbitrary cutoffs caused the model to stop reasoning unnaturally — even if the truncated reasoning still led to correct answers?  \n  \nExperiments are limited to the DeepSeek-Distill-Qwen models. Have you tested or considered applying JET to other models (e.g., Phi-4)?  \n  \nHow does the training time of JET compare to that of the Laser methods?  \n  \nFinally, could you elaborate on potential scenarios where JET underperforms or fails?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "### 1. Overall Assessment\nThe proposed method, JET, is both simple and effective. Because it leverages trajectories directly generated by the model, JET preserves the model’s natural consistency during training. The approach is also computationally efficient, achieving faster training speeds without sacrificing accuracy.  \n  \n### 2. Originality\nThe work presents a well-motivated application of Evidence Accumulation Theory to computational reasoning, highlighting meaningful parallels between human cognitive efficiency and model reasoning.\nThe design of the Progressive Early-Stopping (PES) mechanism and length-aware reinforcement learning is particularly well-executed. The reward function is carefully structured into three components — format reward, accuracy reward, and length reward — with the principles of Correctness First, Conciseness Preference, and Per-Question Normalization clearly reflected in the mathematical formulation.  \nUnlike previous approaches that rely on fixed token budgets or handcrafted truncation, JET autonomously learns when to stop reasoning in a self-consistent and context-sensitive manner.  \n  \n### 3. Clarity\nThe paper is clearly written and well-organized, providing a strong motivation, detailed mathematical formulation, and a comprehensive algorithmic description (as presented in the Appendix). Figures such as Figure 1 (token length distribution) and Figure 2 (two-stage rollout diagram) effectively convey the core ideas and experimental insights.", "weaknesses": "### 1. Need for Further Discussion\nSeveral aspects of the paper would benefit from deeper discussion. In the mathematical reasoning tasks, the Laser methods generally perform better on average and often outperform JET, achieving either higher accuracy or shorter token length. Moreover, Laser shows strong performance on out-of-domain datasets such as CSQA, GPQA, and MMLU. However, the paper provides almost no comparative discussion of these results, and notably, it does not cite the paper “Learn to Reason Efficiently with Adaptive Length-based Reward Shaping,” which originally proposed Laser-D/DE.  \nIn the PES (Progressive Early-Stopping) strategy, trajectories are truncated arbitrarily at 25%, 50%, and 75% of their original length. The paper does not discuss the potential side effects of this truncation. While the authors state that JET preserves the model’s natural generation distribution, one could question whether cutting reasoning trajectories at arbitrary points might itself introduce unnatural discontinuities.  \nFinally, the paper lacks a discussion of its limitations — for instance, in scalability or potential failure cases.   \n  \n### 2. Overstated Claims\nSome of the claims in the paper appear somewhat overstated.  \nAt line 400, the statement “the harder, the stronger” seems based on dataset-level observations and may be an overgeneralization. Since the study includes only eight datasets, this conclusion about performance trends appears insufficiently supported. An analysis at the sample-level difficulty would provide a stronger empirical basis for such a claim.  \nAdditionally, while the paper states that JET’s generation process remains consistent with the model’s natural distribution, there is no experiment directly validating this claim. The intuition that JET should be “natural” is understandable. However, the paper does not include comparative experiments showing how other methods—for example, those using explicit length control or artificially shortened data—may lead to unnatural generation, nor how such unnaturalness affects performance, while JET maintains naturalness. Including such comparisons would strengthen the discussion.  \nIn the 7B model, JET actually leads to accuracy drops on GSM8K and MATH500, which requires further analysis. Although the reward function is designed to favor correctness over length, these results suggest that this objective may not always hold in practice.\n  \n### 3. Experimental Scope  \nThe study primarily evaluates JET on DeepSeek-Distill-Qwen models. To strengthen claims of generality, additional experiments on other architectures (e.g., Phi-4) would be desirable.   \n  \n### 4. Minor Issues  \nTypographical errors: e.g., line 303, “we teste”; and in Equation (3), redundant parentheses “))”.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SLW7ttWoKx", "reviewer": "Reviewer_tagM", "summary": "Just-Enough Thinking (JET) tries to address the well-known **overthinking** problem in Large Reasoning Models (LRMs) through introducing (1) a trajectory truncation mechanism that helps early terminate unnecessary reasoning and increases effective rollout sizes, and (2) a quality-controlled length reward used in their DAPO-based RL policy optimization. Through experiments on `DeepSeek-Distill-Qwen` models (training through math datasets), the paper demonstrates considerable reduction in reasoning length on both in-domain and out-of-domain datasets without compromising accuracies.", "questions": "In addition to questions mentioned in the `Weaknesses` section:\n\n1. What's the rollout size for baseline methods like DAPO/AdaThink?\n\n2. Why finetune SFT/DPO with LoRA in baseline implementation (instead of full finetuning to be consistent with the RL training)?\n\n3. It seems that some important citations (esp. for the baseline methods) are missing, e.g. the Laser paper.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- Overall the paper's writing is clear and easy-to-follow.\n- The piloting experiment provides a good intuition and justification on the huge compressibility within LRM's reasoning traces (given that the full reasoning trace leads to correct answer in the first place).\n- The main experiments seem comprehensive, including a range of in-domain and out-of-domain tasks and multiple alternative efficient reasoning baselines.\n- The paper also includes concrete case studies in the appendix.", "weaknesses": "The weaknesses below are **ranked from high priority to low** \n\n_(priority in terms of how they would influence my final rating to this manuscript):_\n\n**Weakness 1: Limited evaluation beyond `Deepseek-Distilled-Qwen`**\n\nRecent RLVR research (not limited to efficient reasoning) often relies on the `Qwen` + `Math` setup. However, several studies -- such as *“RL with One Example”* [[1]](https://arxiv.org/abs/2504.20571) and *“RL with Random Rewards”* [[2]](https://www.interconnects.ai/p/reinforcement-learning-with-random) -- have raised concerns that performance gains might stem from factors like mid-training on math problems or potential data leakage, rather than the RL signal itself. While I am not asserting that such confounding effects necessarily apply here, it would strengthen the paper’s robustness to demonstrate that the reported improvements generalize to other model families beyond Qwen (e.g., the Llama series).\n\n**Weakness 2: Incomplete treatment of overthinking phenomena**\n\nWhile the paper’s pilot experiment provides an interesting motivation for truncation -- showing that correct reasoning traces are often compressible -- it only captures one side of the broader “overthinking” issue in LLM reasoning. The analysis (particularly the `ARR` metric) is conditioned on problems that are *already solved correctly* with full reasoning traces. This design primarily reflects case (i): verbose reasoning where the correct answer appears early but is followed by unnecessary continuation.  \n\nHowever, overthinking also includes case (ii): overly long or convoluted reasoning that ultimately leads to *incorrect* answers due to confusion or drift. The current truncation approach and pilot study seem to overlook this dimension. Similarly, in the reward design, the use of a constant **0** length reward for all incorrect rollouts fails to distinguish between long and short reasoning traces that are both incorrect.\n\n**Weakness 3: Missing ablations and unclear design justifications**\n\nAlthough the paper includes numerous additional experiments in the appendix, several (e.g., the curriculum learning experiment) appear unrelated to the main narrative and do not directly clarify the proposed method’s key design choices. More targeted experiments are needed, especially in two areas:\n\n1. **Ablation: Effect of PES without the length reward.**  \n   It remains unclear how much of the observed improvement comes from the proposed length reward versus PES. Since PES-based truncation effectively constructs “low-likelihood” reasoning trajectories, GRPO/DAPO training might already encourage shorter reasoning paths by increasing the likelihood of these constructed rollouts. An ablation removing the length reward would help isolate its true contribution.\n\n2. **Design Choice: Comparison with established length reward baselines.**  \n   While the paper proposes alternative reward designs and demonstrates why the chosen one performs better, prior works such as [*Training Efficient*](https://arxiv.org/abs/2502.04463) and [*ShorterBetter*](https://arxiv.org/abs/2504.21370) have already explored efficient reasoning using length-based rewards. Comparing directly against these established formulations -- rather than self-introduced variants -- would provide a more meaningful and standardized evaluation of the proposed approach.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)