Efficient Reasoning with Balanced Thinking
Authors: Yulin Li
Tengyao Tu
Li Ding
Junjie Wang
Hui-Ling Zhen
Yixin Chen
Yong Li
Zhuotao Tian
Venue: ICLR 2026 Poster (2026)
Keywords: Large Reasoning Models
Efficient Reasoning
Primary area: foundation or frontier models, including LLMs

Abstract: Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they often suffer from overthinking, expending redundant computational steps on simple problems, or underthinking, failing to explore sufficient reasoning paths despite inherent capabilities. These issues lead to inefficiencies and potential inaccuracies, limiting practical deployment in resource-constrained settings. Existing methods to mitigate overthinking, such as suppressing reflective keywords or adjusting reasoning length, may inadvertently induce underthinking, compromising accuracy. Therefore, we propose \textsc{ReBalance}, a training-free framework that achieves efficient reasoning with balanced thinking. \textsc{ReBalance} leverages confidence as a continuous indicator of reasoning dynamics, identifying overthinking through high confidence variance and underthinking via consistent overconfidence. By aggregating hidden states from a small-scale dataset into reasoning mode prototypes, we compute a steering vector to guide LRMs’ reasoning trajectories. A dynamic control function modulates this vector’s strength and direction based on real-time confidence, pruning redundancy during overthinking, and promoting exploration during underthinking. Extensive experiments conducted on four models ranging from 0.5B to 32B, and across nine benchmarks in math reasoning, general question answering, and coding tasks demonstrate that \textsc{ReBalance} effectively reduces output redundancy while improving accuracy, offering a general, training-free, and plug-and-play strategy for efficient and robust LRM deployment. Code and models will be made publicly available.

Published as a conference paper at ICLR 2026
EFFICIENT REASONING WITH BALANCED THINKING
Yulin Li
Tengyao 15
Li Ding' Junjie Wang'
Huiling Zhen
Yixin Chen'
Yong Li
Zhuotao Tian
PHuawei Noah's 's Ark Lab TTinghua University "The Chinese University of Hong Kong $Zhongguancun Academy Shhenzhen Loop Area Institute
Harbin Institute of Technology (Shenzhen)
ABSTRACT
Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they often suffer from overthinking, expending redundant computational steps on simple problems, or underthinking, failing to explore sufficient reasoning paths despite inherent capabilities. These issues lead to inefficiencies and potential inac- curacies, limiting practical deployment assource-constraiie settings. Existing methods to mitigate overthinking, such suppressing reflective keywords ad- justing reasoning length, may inadvertently induce underthinking, compromising accuracy. Therefore, we propose REBALANCE, training-free framework that achieves efficient reasoning with balanced thinking. REBALANCE leverages con- fidence as a continuous indicator of reasoning dynamics, identifying overthink- ing through high confidence variance and underthiking via consistent overcon- fidence. By aggregating hidden states from a small- dataset into reasoning mode prototypes, we compute steering vector to guide LRMs' reasoning trajec- tories. A dynamic control function modulates this vector's strength and direction based on real-time confidence, pruning redundancy during overthinking, and pro- moting exploration during underthinking. Extensive experiments conducted on four models ranging from 0.5B to 32B, and across nine benchmarks in math rea- soning, general question answering, and coding tasks demonstrate that REBAL- ANCE effectively reduces output redundancy while improving accuracy, offering a general, training-free, and plug-and-play strategy for efficient and robust LRM deployment. Code is available at http:://gihuu..com/ye li---iiie--Baaaanee..
INTRODUCTION
Recent advances in Supervised Fine- Fine-Tuning (SFT) and Reinforcement Learning (RL) have substan- tially enhanced the reasoning capabilities of Large Reasoning Models (LRMs) (Jacch et al. 2024
Guo et al. 2025 Team 2025). However, LRMs may exhibit overthinking (Chen et al.] 2024b), allocating redundant reasoning steps to simple problems. This redundancy ineurs substantial com- putational costs with marginal performance gains (Sui et al. (2025), and may introduce hallucina- tions (Sun et al. 2025). Thus, overthinking severely limits the practical deployment of LRMs in resource-constrained environments.
Recent efforts (Yue et al./ 2025) have been made to mitigate overthinking by shortening reasoning chains. However, these approaches primarily target overthinking and may overlook the critical is- sue of underthinking (Wang et al.] 2025c), where LRMs fail sufficiently explore valid reasoning paths despite possessing the inherent capability to solve the problem, as shown in Fig. T Specif- ically, WWang al.) (2025a). Ma et al. (2025b), and and [Chn et al. (2025b) suppress keywords indicative of reflection and exploration, but indiscriminately affect both redundant and valuable reasoning, inevitably causing underthinking. Another direction (Zhang et al., 2025c Lou al., 2025 Huang et al.) 2025a) adjusts reasoning length based on problem difficulty via SFT or RL, yet often penalizes lengthy reasoning (Su et al.) (025b)) or dilutes rewards for control tokens (Fang et al., 2025). Such designs may cause decision boundary collapse (Lou et al. 2025), biasing models toward overly short reasoning chains and inducing underthinking. Hence, a key question arises: How can we mitigate overthinking without inducing underthinking, achieving efficient reasoning with balanced thinking?
"Equal Contribution
Corresponding Author (tianzhuotao hit.edu.cn)

---

Published as a conference paper at ICLR 2026
Overthinking (Baseline)
Deoppiork Rt -----ill-- Qwee-TB
Step So, the solution is all (-v3.0) 0) (8).
TeDoleoc (0un))
Step Weit, but when x zero,
4x less then 24 includes zero.
Stop 2: But me doubls-check. Say 1 What about v3 How about = 2 (ut me check x x=-2 <omitted>
Answert v3. (3) v
Redundant Reasoning 1659 tokens
Underthinking (Current Method)
MIGH
Step Therafore, we transtorm the incquality back, and get -/3 < 4. or < x v3, So, excluding zero scems correct.
Answerl (3.0eec(-33.000..222 (3.0)(
Insufficient Reasoning
534 tokens
Belanced Thinking (Ours)
Scep So, that means x must sarisfy v3 x 0 < v3
DeepSeck RR------------ Derro
venty, For 0:-4 4-4400 0f
(0) 21, included.
Answer:
Consise Sufficient Reasoning 875 tokens
(a) Qualitative comparison.
(b) Quantitative comparison.
Figure 1: Qualitative and quantitative comparisons with previous ofate-of-the-art methods for mitigating overthinking. (a) Given the question "For what real values of x is -4 x + 4zx < 217", the model first ob- tains intervals -3, 0) and (0, /3). and then verifies i = is included. However, the baseline Guo et al. 2025) redundantly checks irrelevant values after correctly validating x = 0, causing overthinking. Current mili- gation methods (Yang al. [2025b) overly suppress necessary reflection, leading to underthinking. Our method dynamically controls the reasoning state, effectively balancing these two extremes. (b) REBALANCE outper- forms previous state-of-the-ann method (Chen et al.) (2025b) across multiple mathematical reasoning datasets and model scales (0.5B-32B), reducing reasoning length while simultaneously improving accuracy.
Key observations. To address this issue, we need to develop a dynamic mechanism capable of ex- plicitly modeling and controlling both overthinking and underthinking. Though recent works (Zhang et al. 2025a Yang et al. (2025b; Lin et al. (2225a) have achieved dynamic control by adopting manu- ally designed metries to adaptively retain or discard entire reasoning paths, this rigid binary selection may sacrifice the potentially valuable intermediate reasoning steps, thus still risking underthinking. This motivates us to investigate a continuous and reliable indicator of reasoning states for providing dynamic fine-gras ine-grained reasoning control.
As shown in Fig. 2 we can observe that the confidence values correlate with LRMs' reasoning be- haviors. Specifically, high confidence variance may reflect frequent indecisive switching between different reasoning paths, causing redundant steps and delayed answer convergence, i.e., overthink- ing. Conversely, consistent overconfidence can lead to premature commitment to incorrect reasoning paths, i.e., underthinking. Thus, confidence can be leveraged as an indicator of reasoning dynam- ics. Given that LRMs' internal reasoning states are inherently represented by their hidden states (Su et al. 2025a), this observation prompts us to consider whether the efficient reasoning can be achieved through balanced thinking. by dynamically adjusting hidden states according to confidence levels.
Our solution. In this work, we propose ReBalance, a training-free method that achieves efficient Reasoning with Balanced thinking. To achieve dynamic control between overthinking and under- thinking, we first identify reasoning steps indicating overthinking and underthinking from a small- scale seen dataset, aggregate their corresponding hidden states into reasoning mode prototypes, and compute a steering vector that encodes the transition between them, i.e., from overthinking to un- derthinking. Since the steering vector captures the model's inherent reasoning dynamics, exhibits strong generalization across diverse unseen data, as demonstrated in our experiments.
With this steering vector, we further introduce a dynamic control function that modulates the strength and direction of the vector based on the model's confidence at each step. When signs of overthinking emerge, the steering amplified to prune redundancy. Conversely, when underthinking inferred, steering is reversed to promote exploration of alternative reasoning paths. This adaptive mechanism effectively balances reasoning depth across various contexts, enhancing efficiency without compro- mising the reasoning abilities.
Extensive experiments across four models ranging from 0.5B to 32B, and on nine benchmarks cov- ering math reasoning, general question answering, and coding tasks, demonstrate the effectiveness and strong generalization capabilities of REBALANCE. Notably, REBALANCE not only reduces output length but also improves the accuracy. To summarize, contributions are follows:
2

---

Published as a conference paper at ICLR 2026
As the current methods struggle balance between overthinking and underthinking, identify that confidence can serve as continuous and reliable signal for characterizing both overthinking and underthinking in LRMs, enabling fine-grained behavioral control.
To achieve dynamic reasoning control, we propose REBALANCE, efficient and training- free framework that dynamically steers the reasoning trajectory of LRMs by modulating their internal state based on confidence estimates.
Extensive experiments across different models and tasks demonstrate that REBALANCE improves both inference efficiency and accuracy, offering plug-and-play solution for boosting the efficiency LRMs without compromising performance.
BACKGROUND AND MOTIVATION
2.1 PRELIMINARIES
In the following, to investigate the dynamics of the reasoning process large reasoning models (LRMs), introduce the computation of stepwise confidence and confidence variance. Stepwise confidence measures the degree to which the model consistently adheres to the same reasoning path, while confidence variance between different steps quantifies the frequency of switching between different reasoning paths. The discussion of related work is presented in Appendix G
Stepwise confidence. For each token position E Ta we can define the tokenwise maximum predicted probability pt max.ev Ps x<e).. Then, we can obtain the confidence c, of the reasoning step S,, which is the geometric average of these maxima across all tokens in the step:
C, exp
Σ TET
Pt max
T.
Confidence variance. capture short-term fluctuations in confidence, we compute the confi- dence variance Var(-) over recent steps. Since long-term history is less relevant, we focus on local variability by calculating the variance within a sliding window of size [W| Z 1, and we can define the window W, for the s-th step W, {max(1, s-W+1),.... .., Then, with the average step confidence ε = jw, Σjew, Cj within the window Ws, we can obtain the confidence variance for the s-th step Var(c,; W.) as:
s).
0. W.
W. 1,
Var(cs; W.)
Σ (c-
(c--))
W. 2 2.
(2)
JEW.
To this end, regardless of the current stepwise confidence level, high Var(o;; W.) indicates fre- quent switching among different reasoning paths, which may force the model to continue generating redundant reasoning steps instead of concluding, leading overthinking. Differently, consistently high with low Var(c.; W.) implies premature commitment and potential underthinking. These statistics will guide the dynamic control mechanism that will be introduced later.
2.2 KEY OBSERVATIONS
As discussed above, existing approaches designed to mitigate overthinking effectively reduce the length of inference outputs, yet struggle to achieve satisfactory accuracy. To investigate the underly- ing reasons, we analyze how the length of reasoning sequences relates to the ground-trute reasoning length for both correctly and incorrectly answered samples, before and after applying methods in- tended to mitigate overthinking, as shown in Fig. [[a). Specifically, we collect inference samples under three conditions: the original model, the model after applying existing methods, and the model after applying our proposed method. We utilize ground-truth as proxy for ideal reasoning length.
The trade-off between overthinking and underthinking. Theoretically, if an overthinking mit- igation approach effectively reduces redundant reasoning steps, the reasoning sequence lengths of
3

---

Published as a conference paper at ICLR 2026
Baseline
Dynasor-Col
DEER
NoThinking
Behalance((Urss)
Hereet
Length (Correcl)
8000
6000
4000
2000
Length (Wrong)
8000
6000
4000
2000
Reference Length
Length
Length
Length
Reference Length
(a)
(b)
Figure (a) Effects overthinking mitigation on reasoning modes. We compare the distributions of rea- soning lengths for correct and incorrect predictions before and after applying overthinking mitigation methods.
The reduction in reasoning lengths for correct and incorrect predictions indicates the degree to which over- thinking is alleviated and underthinking is introduced, respectively. Existing methods significantly introduce underthinking, whereas method effectively achieves balanced reduction of both. (b) Correlation be- tween confidence and reasoning modes. We observe that the overthinking samples exhibit higher confidence variance compared to normal samples, while underthinking samples show persistently high confidence levels.
correctly answered samples should accordingly decrease. Conversely, if such methods introduce un- derthinking by prematurely truncating necessary reasoning, resulting in errors, the reasoning lengths for these incorrect samples should also decrease. As shown in Fig. 2 2a), both existing methods and our proposed approach significantly mitigate overthinking. However, existing methods introduce no- table underthinking, whereas proposed approach maintains reasoning length distribution similar to the original model, demonstrating superior balanced thinking capacity.
Consequently, addressing the critical issue of simultaneously mitigating overthinking and preventing underthinking becomes essential. Achieving this requires explicit modeling of these two reasoning modes. Intuitively, questions correctly answered by the original model but incorrectly answered after applying overthinking mitigation methods are likely due to restricted exploration, indicating underthinking. Conversely, questions correctly answered by both the original and mitigated models with shortened reasoning sequences likely reflect the successful reduction of redundant steps, indi- cating overthinking. Based on these categorizations, we analyze changes in stepwise confidence and confidence variance relative to normal reasoning, as illustrated in Fig. 2().
Confidence indicates reasoning states. Our analysis reveals that overthinking typically coincides with higher confidence variance, indicative of hesitation across reasoning steps, while underthinking characterized by persistently high confidence levels, reflecting premature commitment to incorrect reasoning paths without sufficient exploration. These findings support our proposal that confidence serve continuous and reliable indicator of the model's reasoning state, enabling fine-grained behavioral control. A comprehensive analysis, including the correlation between confidence and reasoning length (Appendix A.2)) inertia effects of confidence states (Appendix (A.3). confidence variations across models (Appendix A.4), model keywords and confidence states (Appendix [A.6). and the discriminability of confidence in latent space (Appendix (A.5) (p..
are provided in the Appendix.
3 METHOD
3.1 OVERVIEW
In this section, present REBALANCE, training- free framework designed to dynamically balance overthinking and underthinking, thereby improving efficiency without compromising accuracy.
Specifically, REBALANCE first explicitly models reasoning states prone to overthinking under- thinking using stepwise confidence and confidence variance (Sec. 3.2). Next, it utilizes these identi- fied states to extract distinct steering vectors from deep-layer hidden states, capturing key behavioral patterns of different reasoning modes between overthinking and underthinking (Sec. (.3).. Finally, the steering vectors will be controlled by dynamic function that adaptively modulates steering
4

---

Published as a conference paper at ICLR 2026
Data Collection
Inforence with Dynamic Steering
Layer i
Layer n
Specified Layer
Steering
Extraction
Weight
Specified Layer
Layer
Control Function
Layer
In
S
inin
<ithink
Ourpur
$
<ithink
Outpur
Large Reasoning Model
Prompr
inin
<tthink
Prompt
Un
S,
</thinks
Offline Stage
Online Stage
reasoning step
into
Step end signol
Confilence of i-lh step Confidince variance of the i-ll step
Figure Illustration of the REBALANCE framework. first perform offine one-pass data collection on a small-scale seen dataset. At each step. the steering vector is extracted at the first token of the specified layer based on confidence, and dynamic function is fitted according to model behaviors. During deployment, the dynamic function outputs steering weights based the model's 's real-time confidence online, thus balancing between overthinking and underthinking.
strength and direction, ensuring balanced thinking during the reasoning process (Sec. 3.4). Collec- tively, these complementary components provide precise, adaptive, and efficient control the reasoning process. The overview is shown in Fig.
3.2 EXPLICIT MODELING OF OVERTHINKING AND UNDERTHINKING
Building upon the insights that confidence serves reliable indicator of overthinking and un- derthinking, we first formally define these reasoning states and then explicitly model them using confidence metrics.
Definitions of overthinking and underthinking. Let the <think>... </think> trajectory be segmented into steps .1.....55 S by the delimiter mentioned See 2.1 Denote the partial rea- soning up to step $ by rs. and the induced answer distribution (if forced to stop at s) by Tsi let prediction d, arg max n, under a specified decoding rule, then we define the stability index as:
min { de d, for all s' 2 and d, is conrect.
(3)
The stability index s* serves signal to distinguish different reasoning modes. Specifically, A trajectory may exhibit overthinking if it continues after s*. Conversely, it exhibits underthinking if it stops at step with incorrect prediction d, while there exists s' with correct dy. These definitions formalize the notions redundant computation after convergence the correct answer and premature termination before sufficient reasoning.
Explicit modeling with confidence. Then, the above definitions can be instantiated using the step- wise confidence c, and the confidence variance v, Var(c,; W,) introduced in Sec. 2.1 With small-scale seen dataset that has been used for training, we can obtain the empirical quantiles (Hyn- dman & Fan 1996) Qe(-) and Qe(-) and thresholds
T Qo(9r), тe = Qqx)))
+.' Qu()). -. QQ(()))
where 0 gr. QH 1 specify the lower and upper quantiles, respectively. Then, with these thresholds, we can classify the reasoning steps into two sets O and U:
(s:
To 2.22 J.,, u- (s: T A A. S Tt).
(5)
Concretely, as illustrated in Fig. 2bb, the overthinking set O contains instances characterized by high reasoning variance and low confidence, reflecting unstable oscillating reasoning trajectories.
5

---

Published as a conference paper at ICLR 2026
On the other hand, the underthinking set U comprises with low variance and persistently high confidence, indicating premature convergence and a tendency toward underthinking. Instances not belonging to O U can be treated as normal and excluded from further analysis.
3.3 CONFIDENCE BAADEEEDEE-B-EDDDD STEERING VECTOR EXTRACTION
In this section, based on the modeling of overthinking and underthinking introduced in Sec 3.2 we extract prototypical representations of both reasoning modes from the hidden states of LRMs via an offiine, single forward pass. Then, the resulting prototypes enable the construction of steering vector that delineates the trajectory from overthinking to underthinking, thereby facilitating fine- grained behavior control.
One-pass prototype extraction. To obtain prototypes, we perform a single offline inference pass over small seen dataset Deenn segmenting reasoning steps by the delimiter \n) During this pass, we automatically select the optimal deep-layer based on LRMs' intrinsic separability of reasoning modes (see Appendix A.5), from which we collect hidden states h, at the first token ( of each step. h1 serves as a compact encoding of step-level intent (Yang et al. 2025b) and, under causal masking, conditions the generation of all subsequent tokens within the step. We find that deeper lay- ers exhibit stronger discriminability between reasoning modes and improved generalization across datasets, analyzed in Appendix A.55
Then, with the hidden stages and the tags O and U mentioned in Sec. 3.2 for each step, we can obtain the overthinking and underthinking prototypes, i.e., d2 and n², respectively:
1
(6)
KEM
Steering vector construction. The prototypes o and n² denote the representations leading to overthinking and underthinking respectively. The steering vector is then defined the direction from underthinking 4 U to overthinking n
P 1 1
With the steering vector ulate the behavior during inference, we adjust the initial token ,,ي of each step as follows:
formalize the transition between two reasoning modes. To mod-
(8) where as represents the signed steering weight at step s, combining the steering strength A and direction 5,. When 8, = +1, we can address underthinking by stimulating the exploration of alter- native reasoning paths. Conversely, 8, -1 mitigates overthinking by encouraging commitment. These adjustments conceptually establish the boundaries within which the model's 's reasoning pro- cess operates, aiming to maintain a balanced state that ensures efficient and effective reasoning.
h و h,g a. v, a² = A. 8n, 2.20, 8, E (+1,-11)
3.4 MODEL BEHAVIOR -HAADD DYNAMIC CONTROL FUNCTION
Considering the evolving nature of model states and contexts over time, we introduce dynamic control function that adaptively adjusts steering strength and direction during inference. Motivated by Sec. 2.2] which shows that the confidence correlates with reasoning modes, the steering weight can be deemed as the output of a continuous function g(c,, Uz) with respect to the current confidence C. and variance Eg.. Therefore, the steering weight ag, strength X, and direction 8, are defined as:
a, g(.,, v.) = 6, Ao
(9)
During inference, at each step s, we obtain the confidence c2 and variance Us, set a, = g(.,, v.), and inject o, v at the first token ((') for the selected layer in Eq. (8). This keeps trajectories be- tween the overthinking and underthinking boundaries while adding no extra forward passes beyond standard decoding. Concretely, the dynamic control function g(cs, U.) formulates as:
glc.. v.)= sigufc. 7 B(c., v.) tanh Te
(10)
Steering direction 6, Steering strength

---

Published as a conference paper at ICLR 2026
Methed
Pantl1
#Tikens
Pantl11
#Tiskns]
Pasci2
#iikens
Parci2
#iskens
Parti2
Flikene
Parti1
#Tskens
Deppiek DRL-Dooll-Orrmll
Boor
79.6
2516
13.3
13401
2001
13734
76.1
1001
03.5
K230
41.2
KTB-
040 30
2512
11894
00311
531
6812
T100
NoThl
1583
6996
1473
355
5367
5455
NoRal Wed 44031 beta33
18.0
3665
30.0
8225
15.5
7574
15.1
631
13.0
5300
406
4795
Draar S001000
172
3064
26.7
18354
25-7
13462
311
1005
12.3
6058
42A
1829
3239
08783
8044
754
5884
711T
Relladaee
(Durd)
3474
19988
19573
5301
1336
Sudix
(13.4)
23.155
(1:30.0)
(-14,55]
(14.7)
(-10.25)
(12.50
(-24.29)
(1:10.01
(-50.25)
(12.7)
(-17.9%)
Box Soclae (Daro pul. 1003
89.8
3009
46.7
19923
35.0
19323
1922
1058
$5.0
6315
56.1
1250
(in) Do Noteee 3000)
90.0
5127
16.7
19963
16.7
50148
81.5
174
85.0
7661
17.5
508
2567
1891
1919
676
5155
1850
294
1911
No
4750
Dysor
88.2
2723
46.7
1964
33.5
11599
$7.6
732
15.0
5121
55.4
T437
Dar 20040
90.
2643
43.3
10112
15.7
1825
19.0
611
17.5
5164
539
6261
Masi Stsmmn (1areg 50280
2239
8651
480
4440
913
631
Roelin
4470
040
2050
18212
11:25
027
5340
6054
DIEA
99.0
5926
66.7
1971
16.7
11835
65.8
434
55.0
6537
16.1
7057
1896
5885
399
1400
18997
19724
1332
1485
StAl
3727
10333
59401
1569
6126
T131
908
3611
733
9964
55.7
18087
96.5
1641
1908
6330
66.3
125T
w. Roodie
(46.3)
(-15555)
(46.0)
(-13.35
1445.00
[-15 656
(41.20
(-33 (55
(45.00
(-27 65g
(45.7)
(-2.66
QwQ-218
Socler Ierm 2013
4535
66.7
14342
53.3
12527
56.3
1506
7021
56.7
8219
(iD)
3516
15435
13289
070
6217
83.2
TIGK
Nothi
2012
50897
11.33
1326
7139
K112
Nowal Wedersss
99.8
2579
66.7
5190
63.3
8970
56.3
942
63.5
4717
12.6
6323
94.3
4176
63.3
18156
55.3
1065
58.0
0661
3536
10344
11384
1221
6041
1311
117773 3379
9800
8344
8835
1519
95.3
1662
780
1879
633
11575
96.8
1359
95.1
6061
686
7832
w. Rooliw
(40.0)
(-10.350)
69:.3)
(- 92315
(-:000)
(-5.300 350
(40.5)
(-16.10
(47.5)
(-13.000
(43.00
(-9 190
Table I: Performance on math reasoning benchmarks. Metrics include Pass@1 1 (1) and #Tokens (L) on six math reasoning henchmarks. Changes are shown in orange for Pass@1 and blue for #Tokens. FlashThink and TrimR repraduced according to the paper.
The steering direction 8, is determined by the sign function sign(c, -! where the confidence threshold - is obtained as Eg. (4). It takes a negative value when confidence is below the high- confidence threshold (c, if to mitigate overthinking, and positive value when confidence is
above this threshold (c. > - to alleviate underthinking. This guarantees the steering consistently directs the state away from the nearer reasoning boundary.
The steering strength A is composed of two parts: (1) soft saturation tanh(Ic. - - and (2) variance-awer amplitude (cc,, v.). Specifically, regarding the soft saturation tanh aln - - 1)
smooth, saturating growth in Ica - avoids abrupt changes and keeps the mapping monotone in e, for any fixed v.. The soft saturation function guarantees the steering strength grows gradually as the state approaches the reasoning boundary, ensuring numerical stability.
Differently, the variance-awar amplitude B(c,, ) is a model behavior-based scalar amplitude that adapts across models based the step confidence e, and variance Vg. It is required to indicate the model's ' current thinking status, shifting between moderate and overthinkin/underthiikngg reason- ing modes. To this end, the amplitude function be formulated as:
Bm + (Bo Bm)) (C,,,, va)
if c. S The and Us To
H
B(cs, Us)
Bm, (B.. Bm) v(C., Bm
if 2 Te and E Tu. otherwise.
(11)
Bm. B., and B, are adaptive mode boundaries representing moderate, overthinking, and underthinking, respectively. (b(c, v.) denotes conditioned gating function whose output ranges from 0 to to ensure smooth transitions. The thresholds (7 T Th. 7/)) are obtained in Eq. (4). Following the reasoning mode definitions outlined in Eq. 5 when C. S T and V, 2 7 indicating state of overthinking, the transition occurs between B., and B,, Differently, when c, 2 7H and U, S T!!, indicating a state overthinking, the transition should be performed between Bm, and B,, Notably, B,, and B, adaptively derived from models without manual tuning.
In Eq. 11
In this context, the amplitude B(c,, serves as an indicator of the current reasoning status, com- plemented by the saturation function, which ensures the numerical stability of the final steering
7

---

Published as a conference paper at ICLR 2026
SCIENCE
COMMONSENSE
PROGRAMMING
GPQA-D
Stratery
LodeeeeBench
Method
P1
#Tokens
Pass11
#Tokens
Pass@1
#Tokens
DeepSeck-HR
Ri-Dissill--een--t
Baseline
17.1
8727
12,509
Ours
21.7 (++4.)
69902 (- 20.9%)
67.7 (+-4.)
401 401(-77 (-7.8%
22.5(++30)
622 (7.15)
DeepSeck KI-Disfll-Qwen-78
Buseline
33.8
7.392
88.1
350
44.0
9
39.4(+55.0
5 (-.9999
(+0.0))
310(-11.4%)
46.5
( (-12%)))
Owen3-14BB
Ruseline
7.5
267
83.5
1101
Ours
67 (+666)
5779 (-72,-2 22.1%)
(++0.111)
(-2.0%))
(-11)
6088
(-11.3%)
QwQ-32B
Baseline
63.1
7 7422
93.6
274
87.5
6.222
(+4..)
(- 15.2%)
(4+2.)
3.33%)
(-0.)
(-
Table 2: Generalization capabilities other non-math tasks. Metries include Pass (1) and #Tokens (L). Changes are shown orange for Pass@ and blue #Tokens.
Math500
GSM8K
Olympiad
Method
Pass@1
#Tokens
Pass f
#Tokens
Pass @ 1
#Tokens 4
ReBalance (Ours)
83.0
3474
78.3
765
43.9
7235
W. = 5
82.4
3686
78.2
910
42.2
7343
IW, = 10
81.0
4084
77.8
940
44.7
7679
В. = 0
82.0
3343
78.1
761
39.4
7147
Вμ 0.2
83.6
3600
80.5
850
44.2
7923
B, 0.5
81.4
3543
77.7
890
41.3
8773
Table 3: Ablations on the R1-1.5B backbone across difficulty levels. We analyze performance changes on
three math benchmarks with varying difficulty: Math500 (medium). GSM8K (easy), and Olympiad (hard). Metrics are Pass accuracy (%) and generated token numbers. Arrows indicate change relative to our original REBALANCE: Acc. increase, 4 decrease; Tokens decrease, increase.
strength. More details, theoretical derivations, and proofs regarding the mode boundaries and the gating function are provided in Appendix B due to the page limit.
EXPERIMENT
Evaluation is conducted on mathematics reasoning datasets: MATH-50P (Lightman et al.
2023b), AIME24 (AI-MO 2024a), AIME25 (OpenCompass
2025), , AMC23 (AI-MO b024b).
GSMSK (Cobbe et al. 2021), and OLYMPIADBENCH (He et al.
2024 y: scientific reasoning dataset,
GPQA DIAMOND (Rein al. 2024); commonsense reasoning dataset, STRATEGYQA (Geva al.
2021; and code reasoning dataset, LIVECODEBENCH (Jain et al.) 2024). Besides, the proposed steering extraction and dynamic control function fitting are performed for each backbone once and held fixed across all unseen benchmarks for evaluation. 500 randomly sampled MATH (Hendrycks) al. 202T) problems are utilized during these processes, and the sensitivity analysis shown in Fig. c). More comprehensive experimental details, including the baseline introductions, are pro-
vided Appendix D
4.1 MAIN RESULTS
Math reasoning. As shown Tab, REBALANCE outperforms all baselines on six math reason- ing benchmarks spanning diverse difficulties and distributions. Without introducing any auxiliary models or additional inference stages, simultaneously improves Pass by up to 7.0 points and reduces the average generated token count by most 52.3%. Beyond these overall gains, the results also reveal several consistent behavioral patterns across different model families.
In particular, prompt-based reasoning suppression methods such NoThinking affect distilled mod- els much more severely than non-distilled ones. For example, large accuracy drops are observed on the DeepSeck-RI RI-Disiill models, while the impact Qwen3-14B and QwQ-32B remains com- paratively modest. This suggests that distilled reasoning models are more sensitive to prompt-level manipulations of the reasoning structure. We also observe that methods relying external auxiliary
8

---

Published as a conference paper at ICLR 2026
Rrodl Tihmn
Rerget PN
Saiee
Fosett Medoa Screott
3000
200
023
T72+
Saarey
3V0
Litokiicca
(a)
(b)
(c)
(d)
Figure (a-b) Layerwise Performance MATH-500 for (a) RI-7B and (b) QwQ-32B. (c) Sensitivity sample size for steering vector extraction. (d) Performance with cross-domain vectors.
models for early-exit decisions, such TrimR and FlashThink, generally underperform approaches based on intrinsic model signals, such as DEER. This suggests that external stopping criteria may not faithfully reflect the internal reasoning state of the target model, whereas intrinsic signals provide a more reliable and lightweight control mechanism.
Other reasoning scenarios. We evaluate REBALANCE in a cross-domain setting, fixing the steer- ing vector and control surface across tasks. As shown in Tab. 2 without domain-specific tuning, REBALANCE maintains Pass@1 and reduces the reasoning length by at most 29.9% on challenging scientific reasoning, programming, and simpler commonsense QA tasks. These results demonstrate strong cross-domain generalization.
Furthermore, the confidence signals extracted from mathematical reasoning generalize well across different task domains. On more challenging tasks, such as GPQA and code generation, the pro- posed method significantly improves effective reasoning while reducing redundant computation. In contrast, on relatively saturated tasks like Strategy QA, the method behaves more conservatively, as the model exhibits fewer overthinking states be captured.
Compatibility with NPU devices. To further validate the generality of REBALANCE, we evaluate its performance NPU devices using the NPU-native LRM Ennenaaaede -ebbbddddd--B-V.J (Chen et al. 2025a). REBALANCE consistently demonstrates strong balanced thinking capabilities. The implementation details and results are provided Appendix CC10 due to page limit.
4.2 ABLATION STUDY
Impact of static a, control. We ablate the dynamic schedule by fixing the stecring weight ag. As shown in Fig. [ positive a (U + O) improves accuracy but increases reasoning length, intensifying with larger lo, (e.g., a, = +3 on QwQ-32B yields a 147% token increase). Negative a, reduces tokens at the expense of accuracy. These results motivate dynamic adapting to instance difficulty.
Impact of the steering layer. We test generalization by fitting steer- ing vectors and control surfaces at various layers (selected by depth ratio). Fig. a-bb) shows that steering at any tested depth reduces token count without harming accuracy. The strongest trade-off ap- pears in mid-to-late layers, consistent with our probing analysis (Ap- Figure Static a. Control on pendix[A.5 where representations from these layers exhibit the high- est confidence separability and thus incur minimal noise.
MATH-500, Top: RI-7B; Bot- tom: QwQ-32) 32B.
Steering vector choice and generalization. tors from math datasets of varying scale and cross- dross-domain science corpus (GPQA), fitting control surface for each. Vectors generalize across datasets: REBALANCE improves efficiency while pre- serving accuracy. A clear trend emerges: vectors from harder datasets prioritize accuracy gains over token savings, aligning with the method's mechanism: harder data induces a conservative surface prioritizing correctness, while easier data yields more aggressive one favoring token savings.
shown in Fig, c-dd), we estimate steering vec-
Impact window size W.l-- Rows 4 and Tab. show how the control-surface window size [W, affects reasoning. Empirically, larger windows substantially increase token usage, consistent

---

Published as a conference paper at ICLR 2026
MATH-500
GSM8K
Olympiad
GPQA-D
Gating F Functions
Pass
#Tokens
Pass@
#Tokens
#Tokens J Pass@
#Tokens
DeepSeck-EE Ai-Distill-ween-
Boseline
79.6
4516
76.0
1018
41.2
8785
17.1
8727
Sigmoid Gate
83.0
3474
78.3
765
43.9
7235
21.7
6902
Lincar Gate
83.6
3600
79.2
800
42.5
6794
17.7
7019
Hard Step Gate
81.2
3830
79.7
820
41.8
7528
17.2
6537
Polynomial Fitted Gate
82.8
3508
79.0
798
44.2
7059
20.7
6161
ReLU Shuped Gale
84,0
3277
80.1
818
43.0
6875
19.7
6966
DeepSeek-RR Distill--wen-77
Baseline
89.8
3699
89.2
1098
56.1
7590
33.8
7392
Sigmoid Gate
92.6
2903
91.6
912
57.0
6321
39.4
5180
Lincar Gate
3113
936
6303
43.0
5589
Bard-Sitp Gate
92.0
3115
90.0
909
57.8
6319
40.4
5448
Polynomial-Fing Gate
2932
91.3
913
6206
38.9
5768
ReL. Shappe Gate
91.4
3054
91.7
928
57.0
6362
39.9
5758
Table Performance comparison of different gating functions across benchmarks. We evaluate both and DeepSeck-RI 1----ssiill-ween---e on four reasoning datasets. Metrics are Pass@) accuracy (%) and the number of generated tokens.
with the intuition that they smooth short-term fluctuations while reducing responsiveness to local anomalies. Theoretically, we show that the confidence trajectory during inference satisfies a Marko- vian continuity assumption (see Appendix A.3 small window (W,, is therefore sufficiently expressive and more sensitive to local reasoning patterns. However, with larger window, accuracy increases on the hard Olympiad benchmark, in line with prior findings that extended deliberation improves performance at the expense of longer outputs (Jin et al. 2024 Muennighoff et al., 2025)
Impact of underthinking mode boundary B., Tab. T0 (Row 6) shows that removing the derthinking mode boundary slightly reduces tokens but significantly lowers accuracy, especially on tasks demanding extended reasoning. Moderate increases (0.1 0.2: Rows 7-8) encourage deeper deliberation and boost accuracy at modest token cost, while larger increases (0.1 - 0.5) trigger overthinking and degrade performance. ing paths. The other two mode boundaries Bm and B., are adaptively determined based on model behavior, requiring no manual tuning (Appendix B
overly strong boundary disrupts these normal reason-
Impact of gating function. To investigate the sensitivity of our method to the specific form of the gating function, we conduct an ablation study by replacing the default sigmoid gate with several alternative gating strategies. As shown in Tab. 4 we evaluate four variants: linear gate, a hard-step gate, a polynomial-fited gate, and Rel.U-shaped gate. Results across multiple models, difficulty levels, and domains indicate that all variants achieve effective reasoning performance, with only minor differences observed across datasets. This demonstrates that our approach robust to the particular choice of gating function.
5 CONCLUSION
This paper analyzes the limitations of existing approaches overthinking mitigation, and we ob- serve that such attempts often introduce the countervailing problem of underthinking. Therefore, we propose REBALANCE, training-free free method that curbs overthinking while avoiding underthinking. Extensive experiments across diverse models and datasets show that REBALANCE reduces redun- dancy while preserving accuracy, achieving efficient reasoning with balanced thinking. promising future direction is to apply REBALANCE to the multi-modal reasoning scenarios.
ACKNOWLEDGEMENT
This work was supported by the Guangdong Basic and Applied Basic Research Foun- dation (2025A1515011546) and by the Shenzhen Science and Technology Program (CCY120208831005000 K/7D20040000
ZDCC20250901 0112500001110000110 13000001).
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Pranjal Aggarwal and Sean Welleck. LI: Controlling how long reasoning model thinks with reinforcement learning, 2025. URL https://arxiv. org/abs/2503.04669, 2025.
AI-MO. Aime 2024, July 2024a.
URL http://///uuggnnneeeee.e//tttttt
aimo-validat aime
AI-MO. Ame 2023, July 2024b. a aimo-val Sataalldattt iidaiiiiono on amc
URL httpss///uggiigeeee...//tttttt/s
Anthropic.
Claude 3.5
Sonnet. Accessed: 2025-11-22.
claude- 3-5 5- Sonnnett] 2024.
Daman Arora and Andrea Zanette. Training language models to reason efficiently. arXiv preprint arXiv:2502.04463. 2025.
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gian- inazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. Graph of thoughts: Solving elaborate problems with large language models. In Proceedings of the AAAI conference on artificial intelligence, volume 38, pp. 17682-17690, 2024.
Hanting Chen, Yasheng Wang, Kai Han, Dong Li, Lin Li, Zhenni Bi, Jinpeng Li, Haoyu Wang, Fei Mi, Mingjian Zhu, et al. Pangu embedded: An efficient dual-system Ilm reasoner with metacog- nition. arXiv preprint arXiv:2505.22331 22375, 2025a.
Qiguang Chen, Libo Qin, Jiaqi Wang, Jingxuan Zhou, and Wanxiang Che. Unlocking the capa- bilities of thought: A reasoning boundary framework to quantify and optimize chain-of-thouu of-thhught. Advances in Neural Information Processing Systems, 37:54872-544 -449444 2024a.
Runjin Chen, Zhenyu Zhang, Junyuan Hong. Souvik Kundu, and Zhangyang Wang. Seal: Stecrable reasoning calibration of large language models for free. arXiv preprint arXiv:2504.07986 2025b.
Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, et al. Do not think that much for 2+ 3=? on the overthinking of ol-like llms. arXiv preprint arXiv:2412.2// 187, 2024b.
Jeffrey Cheng and Benjamin Van Durme. Compressed chain of thought: Efficient reasoning through dense representations. arXiv preprint 2rXiv:2412.131711 2024.
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint 1xXiv22100.14666 2021.
Kwesi Cobbina and Tianyi Zhou Where to show demos in your prompt: A positional bias of 2025.
in-context learning. arXiv preprint
Mengru Ding, Hanmeng Liu, Zhizhang Fu, Jian Song, Wenbo Xie, and Yue Zhang. Break the chain: Large language models can be shortcut reasoners. arXiv preprint arXiv:2406.06580 2024.
Gongfan Fang, Xinyin Ma, and Xinchao Wang. Thinkless: Llm learns when to think. arXiv preprint arXiv:2505. 13379, 2025.
Yichao Fu, Junda Chen, Yonghao Zhuang, Zheyu Fu, Ion Stoica, and Hao Zhang. Reasoning without self-doubt: More efficient chain-of-the -fthhught through certainty probing. In ICLR 2025 Workshop on Foundation Models in the Wild, 2025.
Kanishk Gandhi, Denise Lee, Gabriel Grand, Muxin Liu, Winson Cheng, Archit Sharma, and Noah Goodman. Stream of search (sos): Learning to search in language, 2024. URL. htpp://arxivv. hrg/abs/2404.033688 2, 2024.
Zorik Gekhman, Eyal Ben David, Hadas Orgad, Eran Ofek, Yonatan Belinkov, Idan Szpektor, Jonathan Herzig, and Roi Reichart. Inside-out: Hidden factual knowledge 1lms. arXiv preprint arXiv:2503. 15299, 2025.
11

---

Published as a conference paper at ICLR 2026
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. Did aristotle use laptop? a question answering benchmark with implicit reasoning strategies. Transactions of the Association for Computational Linguistics, 9:346-361, 2021.
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song. Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-rl: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint 1.XXvv:5501/2994 2025.
Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu. Jason Weston, and Yuandong Tian. Training large language models to reason in a continuous latent space. arXiv preprint arXiv:2412.06700 2024.
Chaoqun He. Renjie Luo. Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, al. Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems. arXiv preprint arXiv:2402. 14008, 2024.
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. arXiv preprint
2021.
Shijue Huang. Hongru Wang, Wanjun Zhong, Zhaochen Su, Jiazhan Feng, Bowen Cao, and Yi R Fung. Adactrl: Towards adaptive and controllable reasoning via difficulty-aware budgeting. arXiv preprint arXiv:2505. 18822, 2025a.
Yao Huang, Huanran Chen, Shouwei Ruan, Yichi Zhang, Xingxing Wei, and Yinpeng Dong. Mitigating overthinking in large reasoning models via manifold steering. arXiv preprint
arXiv:2505.22411, 2025b.
Rob J Hyndman and Yanan Fan. Sample quantiles in statistical packages. The American Statistician, 50(4))361-36) 1996.
Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. Openai ol system card. arXiv preprint arXiv:2412.167700 2024.
Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. Livecodebench: Holistic and contamination free evaluation of large language models for code. arXiv preprint arXiv:2403.0797 2024.
Guochao Jiang, Guofeng Quan, Zepeng Ding, Ziqin Luo, Dixuan Wang, and Zheng Hu. Flashthink: An early exit method for efficient reasoning. arXiv preprint 1399499505.13949 2025.
Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, and Mengnan Du. The impact of reasoning step length on large language models. arXiv preprint arXiv:2401.04925. 2024.
Yu Kang. Xianghui Sun, Liangyu Chen, and Wei Zou. C3o:: Generating shorter chain-of- oh-inghf- without compromising effectiveness. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 39, pp. 24312-24320, 2025.
Aayush Karan and Yilun Du. Reasoning with sampling: Your base model is smarter than you think. arXiv preprint wxXiv:2510.199011 2025.
Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, et al. Training language models to self-correct via reinforcement learning. arXiv preprint arXiv:2409.11 12917, 2024.
Woosuk Kwon, Zhuohan Li. Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th symposium on operating systems princi- ples, pp. 61 611-16 1-226, 2023a.
12

---

Published as a conference paper at ICLR 2026
Woosuk Kwon, Zhuohan Li. Siyuan Zhuang. Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th symposium operating systems princi- ples, pp. 61 1-26,, 2023b.
Ayeong Lee, Ethan Che, and Tianyi Peng. How well do llms compress their own chain-of-thought? a token complexity approach. arXiv preprint arXiv:2503.01 141, 2025.
Yaniv Leviathan, Matan Kalman, and Yossi Matias. Fast inference from transformers via speculative decoding. In International Conference on Machine Learning, pp. 19274-19286. PMLR, 2023.
Peiji Li, Kai Lv, Yunfan Shao, Yichuan Ma, Linyang Li, Xiaoqing Zheng, Xipeng Qiu, and Qipeng Guo. Fastmets: A simple sampling strategy for data synthesis. arXiv preprint arXiv:2502. 1476, 2025.
Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let's verify step by step. In The Twelfth International Conference on Learning Representations, 2023a.
Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let's verify step by step. In The Twelfth International Conference Learning Representations, 2023b.
Weizhe Lin, Xing Li, Zhiyuan Yang, Xiaojin Fu, Hui-Ling Zhen, Yaoyuan Wang, Xianzhi Yu, Wulong Liu, Xiaosong Li, and Mingxuan Yuan. Trimr: Verifier-based training-free thinking compression for efficient test-time scaling. arXiv preprint 2.X.: 2025a.
Zhengkai Lin, Zhihang Fu, Ze Chen, Chao Chen, Liang Xie, Wenxiao Wang, Deng Cai, Zheng Wang, and Jieping Ye. Controlling thinking speed in reasoning models. arXiv preprint arXiv:2507.03704 2025b.
Chengzhi Liu, Zhongxing Xu, Qingyue Wei, Juncheng Wu, James Zou, Xin Eric Wang, Yuyin Zhou, and Sheng Liu. More thinking, less seeing? assessing amplified hallucination in multimodal reasoning models. arXiv preprint arXiv:2505.21523, 2025a.
Ruikang Liu, Yuxuan Sun, Manyi Zhang, Haoli Bai, Xianzhi Yu, Tiezheng Yu, Chun Yuan, and Lu Hou. Quantization hurts reasoning? empirical study on quantized reasoning models. arXiv preprint arXiv:2504.00 250.025504.0882,, 2025b.
Sheng Liu, Haotian Ye. Lei Xing, and James Zou. In-context vectors: Making in context learning more effective and controllable through latent space steering. arXiv preprint arXiv:2311. 0.22231100666 066668, 2023.
Sheng Liu, Haotian Ye, Lei Xing, and James Zou. Reducing hallucinations in vision-language models via latent space steering. arXiv preprint arXiv:2410.1577 2024.
Chenwei Lou, Zewei Sun, Xinnian Liang, Meng Qu, Wei Shen, Wengi Wang, Yuntao Li, Qing- ping Yang, and Shuangzhi Wu. Adacot: Pareto-optimal adaptive chain-of-thouge triggering via reinforcement learning. arXiv preprint arXiv:2505. /1866, 2025.
Yijia Luo, Yulin Song, Xingyao Zhang, Jiaheng Liu, Weixun Wang, GengRu Chen, Wenbo Su, and Bo Zheng. Deconstructing long chain-of-thought: A structured reasoning optimization framework for long cot distillation. arXiv preprint urXiv:2503.16385 2025.
Wenjie Ma, Jingxuan Hc, Charlic Snell, Tyler Griggs, Sewon Min, and Matei Zaharia. Reasoning models can be efffettve without thinking. arXiv preprint arXiv:2504. 09858, 2025a.
Wenjie Ma, Jingxuan He, Charlic Snell, Tyler Griggs, Sewon Min, and Matei Zaharia. Reasoning models can be effective without thinking. arXiv preprint arXiv:2504.0900 2025b.
Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannanch Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. sl: Simple test-time scaling. arXiv preprint (XXx:250111199) 2025.
13

---

Published as a conference paper at ICLR 2026
Sania Nayab, Giulio Rossolini, Marco Simoni, Andrea Saracino, Giorgio Buttazzo, Nicolamaria Manes, and Fabrizio Giacomelli. Concise thoughts: Impact of output length on Ilm reasoning and cost. arXiv preprint
2024. 2024.
OpenAL GPT-3.5 Turbo Models.
http:://paatttomm...ooaa.coccooa gpt 3-5 2023. Accessed: 2025-11-22
OpenCompass. Aime 2025, February 2025. URL httpss//aaogingggc..../tttatette
opencompass/ IME2025
Samuel
Paech.
Eq-bench creative writing benchmark v3. 2025.
https://github.cm com/
FQ-
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Di- rani, Julian Michael, and Samuel R Bowman. Gpqa: A graduate-level google-proof q&a bench- mark. In First Conference on Language Modeling, 2024.
Matthew Renze and Erhan Guven. The benefits of concise chain of thought problem-solving in large language models. In 2024 2nd International Conference on Foundation and Large Language Models (FLLM), pp. 476-483, IEEE, 2024.
Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi, Wenjing Zhang, Jiangze Yan, Ning Wang, Kai Wang, Zhaoxiang Liu, and Shiguo Lian. Dast: Difficulty-adaptive slow-thinking for large reason- ing models. arXiv preprint arXiv:2503.0444 2025.
Leheng Sheng, An Zhang, Zijian Wu, Weixiang Zhao, Changshuo Shen, Yi Zhang, Xiang Wang, and Tat-Seng Chua. On reasoning strength planning in large reasoning models. arXiv preprint arXiv:2506. 00332000000080, 08390, 2025.
Oscar Skean, Md Rifat Arefin, Dan Zhao, Niket Patel, Jalal Naghiyev, Yann LeCun, and Ravid Shwartz-Ziv. Layer by layer: Uncovering hidden representations in language models. arXiv preprint (.x.i:2552.020111 2025.
Charlie Victor Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling Ilm test-time compute optimally can be more effective than scaling parameters for reasoning. In The Thirteenth Inter- national Conference on Learning Representations, 2025.
Gaurav Srivastava, Shuxiang Cao, and Xuan Wang. Towards reasoning ability of small language models. arXiv preprint arXiv:2502. 1569, 2025.
Dilia Su, Hanlin Zhu, Yingchen Xu, Jiantao Jiao, Yuandong Tian, and Qinqing Zheng. Token assorted: Mixing latent and text tokens for improved language model reasoning. arXiv preprint arXiv:2502.03275. 2025a.
Jinyan Su and Claire Cardie. Thinking fast and right: Balancing accuracy and reasoning length with adaptive rewards. arXiv preprint arXiv:2505. 18298, 2025.
Jinyan Su, Jennifer Healey, Preslav Nakov, and Claire Cardie. Between underthinking and over- thinking: An empirical study of reasoning length and correctness in llms. arXiv preprint arXiv:2505.00112, 2025b.
Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen Zhong, Hanjie Chen, et Stop overthinking: survey on efficient reasoning for large language models. arXiv preprint 16441 2025.
Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang. Jiahao Qiu, Ming Yin, Mengdi Wang, Peter Bartlett, and Andrea Zanette. Fast best-of-n decoding via speculative rejection. Advances in Neural Information Processing Systems, 37:32630-3223 32652, 2024.
Zhongxiang Sun, Qipeng Wang, Haoyu Wang. Xiao Zhang, and Jun Xu. Detection and miti- gation of hallucination in large reasoning models: A mechanistic perspective. arXiv preprint arXiv:2505. 1 /2886, 2025.
14

---

Published as a conference paper at ICLR 2026
Qwen Team. Qwq-32b: Embracing the power of reinforcement learning, March 2025. URL htttps://qwenm.c.om github. httpaai.ooooooooog///oe/a/o
Chenlong Wang, Yuanning Feng, Dongping Chen, Zhaoyang Chu, Ranjay Krishna, and Tianyi Zhou, Wait, we don't need to" wait"! removing thinking tokens improves reasoning efficiency. arXiv preprint arXiv:2506.08343: 2025a.
Xuezhi Wang. Jason Wei, Dale Schuurmans, Quoe Le, Ed Chi, Sharan Narang, Aakanksha Chowdh- ery, and Denny Zhou, selficoonss improves chain of thought reasoning in language models. arXiv preprint arxiv:2203.111777 2022.
Yiming Wang, Pei Zhang, Siyuan Huang. Baosong Yang, Zhuosheng Zhang, Fei Huang, and Rui Wang. Sampling-effcient test-time scaling: Self-estimating the best-of-n sampling in early de- coding. arXiv preprint arXiv:2503.01422, 2025b.
Yue Wang, Qiuzhi Liu, Jiahao Xu, Tian Liang. Xingyu Chen, Zhiwei He, Linfeng Song. Dian Yu, Juntao Li, Zhuosheng Zhang, et al. Thoughts are all over the place: On the underthinking of ol-like llms. arXiv preprint arXiv:2501. 18585, 2025c.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. offain-of-though prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824 24837, 2022.
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. Huggingface's 's transformers: State-of-ther -he-thr-rttt natural language processing. arXiv preprint 1910:3791, 2019.
Heming Xia, Chak Tou Leong, Wenjie Wang, Yongqi Li, and Wenjie Li. Tokenskip: Controllable
thain-of-thought compression in lmss. arXiv preprint arXiv:2502. 12067, 2025.
Silei Xu, Wenhao Xie, Lingxiao Zhao, and Pengcheng He. Chain of draft: Thinking faster by writing less. arXiv preprint arXiv:2502. 2800000000.,0000, 2025a.
Yige Xu, Xu Guo, Zhiwei Zeng, and Chunyan Miao. Softcot: Soft chain-of-thouue of-thuught for efficient reasoning with 1lms. arXiv preprint 0rXiv:2502.12134 2025b.
Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report. arXiv preprint arXiv:2505.09388, 2025a.
Chenxu Yang, Qingyi Si, Yongjie Duan, Zheliang Zhu, Chenyu Zhu, Qiaowei Li, Zheng Lin, Li Cao, and Weiping Wang. Dynamic early exit in reasoning models. arXiv preprint arXiv:2504.1580 2025b.
Junjie Yang, Ke Lin, and Xing Yu. Think when you need: Self-adaptive chain-of-thought learning. arXiv preprint wrXiv:2504.032.55 2025c.
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. Ad- vances in neural information processing systems, 36:1 16:11809-10 1809-11822, 2023.
Ping Yu, Jing Xu, Jason Weston, and Ilia Kulikov. Distilling system 2 into system 1. arXiv preprint 20X1:22407.06023. 2024.
Hang Yuan, Bin Yu, Haotian Li, Shijun Yang, Christina Dan Wang. Zhou Yu, Xueyin Xu, Weizhen Qi, and Kai Chen. Not all tokens are what you need in thinking. arXiv preprint arXiv:2505.1782. 2025.
Weizhe Yuan, Ilia Kulikov, Ping Yu, Kyunghyun Cho, Sainbayar Sukhbaatar, Jason Weston, and Jing Xu. Following length constraints in instructions, 2024. URL https://axxiv. org/abs/2406.1777 17744, 2024.
Linan Yue, Yichao Du, Yizhi Wang, Weibo Gao, Fangzhou Yao, Li Wang, Ye Liu, Ziyu Xu, Qi Liu, Shimin Di, et al. Don't 't overthink it: A survey of efficient rsyst large reasoning models. arXiv preprint 2508 0200 /20 2025.
15

---

Published as a conference paper at ICLR 2026
Jinghan Zhang, Xiting Wang, Fengran Mo, Yeyang Zhou, Wanfu Gao, and Kunpeng Liu. Entropy- based exploration conduction for multi-step reasoning. arXiv preprint arXiv:2503. 2025a.
Nan Zhang, Yusen Zhang, Prasenjit Mitra, and Rui Zhang. When reasoning meets compression: Benchmarking compressed large reasoning models complex reasoning tasks. arXiv preprint arXiv:2504.02010, 2025b.
Shengjia Zhang, Junjie Wu, Jiawei Chen, Changwang Zhang, Xingyu Lou, Wangchunshu Zhou, Sheng Zhou, Can Wang, and Jun Wang. Othink-r1: Intrinsic fast/slow thinking mode switching for over-reasoning mitigation. arXiv preprint arXiv:2506. 02397, 2025c.
Shimao Zhang, Yu Bao, and Shujian Huang. Edt: Improving large language models' generation by entropy-based dynamic temperature sampling. arXiv preprint 14X1 2024.
Zhen Zhang, Xuchai He, Weixiang Yan, Ao Shen, Chenyang Zhao, Shuohang Wang, Yelong Shen, and Xin Eric Wang. Soft thinking: Unlocking the reasoning potential of Ilms in continuous concept space. arXiv preprint arXiv:2505.15775 2025d.
Yuqi Zhu, Jia Li, Ge Li, YunFei Zhao, Zhi Jin, and Hong Mei. Hot or cold? adaptive temperature sampling for code generation with large language models. Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 437-445, 2024.
16

---

Published as a conference paper at ICLR 2026
CONTENTS
Supplementary Motivation and Evidence
18
From Overthinking to Underthinking
18
A.2
Confidence-l.ength Association
18
A
Markov Persistence of Contidence States
19
A.4
Distributional Heterogeneity Model Confidence
21
Encoding Confidence Signals Latent Representations
A.6
Confidence as Evidence under Vocabulary Coverage Gaps
22
B Method Details
25
B.
I
Explicit Modeling of Overthinking and Underthining
25
Bostidence-Based Steering Vector Extraction
27
Model Behavior Dynamic Control Function
28
Additional Experimental Results and Ablations
30
Ablation on Individual Axes
30
Cross-Domand and Cross-Diffieuu Transferability
................................
31
Pass@Kk and Avg@k Performance Analysis
................................
32
Performance Variation under Different Confidence Distributions
.assd...........................
33
C.5
Semantic Change and Creativity Analysis
34
C.6
Confidence Characteristics of Overthinking and Underthinking
36
Performance comparison with Trimk and Flashthink
37
Balanced Thinking with Dynamic Temperature
38
Additional Prototype Construction Strategies
38
C.
10 Comparability with NPU Devices
40
D Details on Experimental Settings
40
E Details Benchmarks
41
Details Prompts
42
Details on Prompt-Based Approaches
43
Detailed Discussion of Related Works
44
Effictency Analysis
46
The Use of Large Language Models
Ethics Statement
47
Reproducibility Statement
47
M Case Study
48
17

---

Published as a conference paper at ICLR 2026
XOWAIT
NOTHENK
DYNAROR
Curru
D40
Minus
Rellocku Sough (HNIS))
Ratirmio Lmgrreeeett
Bekimmo lugg (wand)
Befrimee longh (werdo
Figure 6: Mitigating Overthinking Without Inducing Underthinking. Evaluation the MATH dataset with Each panel plots output length reference length (words), restricted reference I 300 and output S 8000. Top: correct examples. Bottom: incorrect examples. Competing methods reduce overthinking at the cost of underthinking, whereas Our Method mitigates overthinking without inflating underthinking.
A SUPPLEMENTARY MOTIVATION AND EVIDENCE
A. FROM OVERTHINKING TO UNDERTHINKING
Why do many anti-overthinking techniques backfire as underthinking? Empirically, the chain of thought (CoT) length and model performance are positively correlated, so aggressively truncating or penalizing long chains can excise necessary reasoning and degrade accuracy (Jin et al.] 2024). Token-complexity theory (Lee al. 2025) further posits intrinsic minimum token budget for success. Enforcing uniformly short budgets or early termination pushes more instances below this threshold, yielding concise but wrong outputs. Moreover, the reasoning-boundarl framework (Chen) et al. 2024a) shows that optimal CoT length and reasoning path selection are task-dependent, thus global length controls disregard this heterogeneity and may steer reasoning trajectories outside the feasible region for given task.
As shown in Fig. 6 on the MATH dataset with -IIPPSEE-RRR-IITLLL-OONN ods indeed curb overthinking but often induce underthinking, manifesting as collapse of error distributions toward short outputs. contrast, Our Method adaptively identifies and modulates the reasoning process, selectively shortening reasoning chains when appropriate while preserving longer explorations necessary for challenging instances. Consequently, our approach mitigates overthink- ing without inducing underthinking, as evidenced by error distributions that avoid collapsing into shorter outputs.
prior meth-
A.2 LONFIDENCE-LANGTH ASSOCIATION
Although confidence is commonly used as proxy for model's certainty, its connection to actual reasoning behavior remains ambiguous. To effectively utilize confidence for identifying suboptimal reasoning patterns or enabling adaptive control, it is essential to first establish a clear and quan- tifiable relationship between confidence and specific aspects of model behavior. Building upon the quantitative patterns presented in Fig. [bb,, which reveal distinct confidence signatures associated with overthinking and underthinking, we focus here on another key dimension of efficient reasoning: reasoning length.
Specifically, we measure how response length relates to step-level confidence on MATH-500, us- ing four models (DeepSeck-RI-DD) -iies--R--iissll--eeeet 1.5B, SrepSeeck-R--istill-oo QwQ-32B; each with n = 500 samples). Here, the length of a response is defined as the total num- ber of words in the reasoning text before the first </think>, and this count is aligned with the list of confidence values for each step. For every answer, we compute two key quantities: (i) the minimum seeel confidence, and (ii) the variance of step-level confidence within the answer.
Qwen3-14B,
Since confidence values produced by non-greedy decoding tend to be heavily skewed toward the upper end of the interval [0. 1] rather than following balanced and symmetric bell-shaped distribu-
18

---

Published as a conference paper at ICLR 2026
tion, the usual Pearson correlation is not suitable. Therefore, use the nonparametri Spearman rank correlation to capture the relationship more reliably.
Our correlation analysis results are shown in Tab. 5 Across all four models, word count shows clear negative association with the minimum step-level confidence (Spearman p values roughly be- tween -0.62 and -0.00), and clear positive association with the variance of step-level confidence within each answer (Spearman values roughly between 0.46 and 0.70). For all reported entries, the 95% bootstrap confidence intervals do not include zero. These results provide quantitative ev- idence that confidence trajectories encode meaningful signals about reasoning effort and stability, supporting their use as reliable indicators for dynamic reasoning control.
Words Min confidence
Words Confidence variance
Model
p
CI.
CI.hi
P
CL
CL.hi
DeepSeek-RI RRI-Distill-Qwee 1.5B
-0.733
-0.777
-0.684
0.602
0.542
0.655
SeprsSek-RI-Disiil---eeee QwQ-32B Qwen3-14B
00.624
-0.681
0.560
0.458
0.385
0.527
-0 -0.801
-0.835
- -0.765
0.698
0.643
0.746
-0.681
-0.730
- -0.228
0.623
0.561
0.682
Table 5: Spearman correlations (p) between word count length and confidence statisties MATH--50. 95% bootstrap percentile confidence intervals (CIlo hi, with 2000) are reported separate columns. All correlation coefficients are significant at p < 0.001.
The
A.3 MARKOV PERSISTENCE OF CONFIDENCE STATES
Effective dynamic control over model's reasoning trajectory often relies on the ability to recognize its current reasoning state. Intuitively, this requires examining contextual window of recently generated reasoning steps, i.e., a sliding window over the chain-of-thought trace. However, for complex problems such as those AIME (AI-MO) 2024a), reasoning traces can span thousands of tokens. While a larger window might seem necessary to capture sufficient context, it introduces significant computational overhead and may obscure fine-grained shifts in reasoning behavior.
In this section, we demonstrate that the model's confidence trajectory exhibits strong first-order Markov persistence. This finding reveals a key insight: the current reasoning state can be accurately inferred from just the immediately preceding step. Consequently, a minimal window of size two is sufficient and often preferable for capturing the essential dynamics of the reasoning process.
To formalize this, for each answer, we split the model outputs by double newlines ((n)n) (n) and align the resulting segments with the sentence-level confidences; we only consider adjacencies that oceur within complete, untruncated reasoning trajectories that contain the final answer. We then collect all adjacent confidence pairs (G-1,, ()) from the model's reasoning traces.
We convert each confidence value into either high state or low state using the model-wise median threshold T:
E (H, L). Here H (high) represents o 7 and (low) represents & т. If confidence value equals the threshold, that sentence is placed in the high state.
8; I[(, 2 T),
= median(ce over all sentences of the model].
For each model, we form two-by-two transition count matrix
N HH HL LH LL
where H is the number of transitions from high high and H is the number of transitions from high to low.
By normalizing each row, we obtain the corresponding transition probabilities:
HH
HL
P(H-H)- = HH + HL'
P(H+L) = HH + HL'
LH
LL
P(LHH) = LHH + LL'
P(L+L) L.H + LL.
19

---

Published as a conference paper at ICLR 2026
To measure how strongly state tends be followed by the same state, use the odds ratio
HH HH-LL OR HL LH
We evaluate statistical significance using two-sided Fisher exact applied to N. When all cells of the matrix are positive, we also report the approximate sinety-five percent confidence interval for the odds ratio (Woolf method):
log (R) 1.96
Clas% exp(-).
HH
HL
LI
For casier interpretation, we additionally provide the same state rate
HH + LL HH + HLL + LH LL
SameRate
All values reported in our results, including transition probabilities, odds ratios. confidence intervals, and significance levels, are computed using this median-based thresholding procedure.
From Tab. 66 all four models show clear evidence of strong like-to-like persistence in confidence when using the model-wise median threshold. The transition probabilities for remaining in the same state, P(H-H) and P(L+L), are both larger than the probabilities of switching to the opposite state. The overall same state rate satisfies SameRate 0.5, the Fisher exact tests give p < 0.001, and the diagonal odds ratios are consistently greater than one with ninety-five percent confidence intervals that do not include one. Taken together, these results provide strong support for the presence of first-order Markov persistence, which reflects clear tendency for the confidence state to remain stable from one step to the next.
Model
P(H-H
P(L-L)
SameRate
Cl.lo
CL.hi
Sig.
DeepSeek-RIe R1-Distill- Seell--wenn--5111 1.5B
0.666
0.665
0.666
3.96
3,83
4.10
SeepSeek-RI--istil---vvesss QwQ-32B
0.653
0.651
0.652
3.51
3.38
3.65
0.670
0.673
0.672
4.19
4.03
4.35
488
Qwen3-14h
0.657
0.659
0.658
3.70
3.56
3.86
#99
Table 6: Adjacent state persistence in step-level confidence using median threshold for binarization. Rows report the same state transition probabilities and diagonal odds ratios (OR) with ninety-five percent Woolf confidence intervals. Significance codes (Sig.): "p 0.05, ** p < 0.01, *** p < 0.001 (two sided Fisher exact test).
This observation directly guides the design of our sliding window. Based on this insight, we set the window size to w = 2 instead of larger value. window length two records each pair of adjacent states and therefore captures the transition patterns P(H-L)) and P(L+II) without any loss information. This choice keeps detection lag to a minimum and prevents brief reversals from being averaged away. When the model begins to drift away from its current reasoning direction, for example, when it moves into an overthinking regime, the adjacent transition window allows the intervention strength to increase immediately.
20

---

Published as a conference paper at ICLR 2026
A.4 DISTRIBUTIONAL HETEROGENEITY IN MODEL CONFIDENCE
In this study, our goal is to design a dynamic con- trol function that leverages stepwise confidence and its variance during the reasoning process to enable real-time, adaptive regulation of model behavior. We aim to make this control mechanism highly adaptable, i.., capable of fully realizing our proposed concept of balanced thinking and delivering accurate, smooth, and responsive control. However, greater adaptabil- ity inherently involves introducing additional param- eters, which raises concerns about the generalization ability of such control function across diverse mod- els. To address this, we analyze and compare confi- dence distributions across multiple models inves- tigate whether a universal hyperparameter configura- tion can be identified.
KDE (Corfidence) - Four Node's
Figure 7: KDE (Confidence) for QwQ-32B, Swen3-14B, and DeepScek-Ri -i-eck-----ttlll----e 7B and 1.5B MATH-500.
As shown in Fig. 7 we visualize confidence distributions across reasoning steps for multiple mod- els on the same dataset. The three models based on the QWEN2 family, namely QwQ-32B, DEEPSEEK-RI- --II--RRR-DISILL-WWENE
display broadly similar distributional patterns, although each model still exhibits its own characteristic shape. In contrast, the QWEN3- 14B model, built upon the QWEN3 family, exhibits a clearly different confi- dence profile compared with the QWEN2 family. These observations are consistent with previous findings that the LLAMA-3. WEEEOTTRNNNNNOO confidence regime throughout its reasoning process YYng et al.) 2025b).
and DEEPSEEK-RI-AEE L-DDDIILLL-WEN-LL...
model tends to operate in uniformly low
These distributional differences highlight the difficulty of designing single set of hyperparameters capable of effectively generalizing across various models. Consequently, this motivates our pro- posed model behavior-based dynamic control function fitting approach in Sec. 3.4 which automat- ically derives parameters tailored to the unique confidence behaviors of each model. By leveraging this behavior-aware strategy, method eliminates the need for manual hyperparameter tuning, thereby ensuring robust adaptability and broad applicability across diverse model architectures and confidence profiles.
A.5 ENCODING OF CONFIDENCE SIGNALS IN LATENT REPRESENTATIONS
To enable dynamic control over a model's reasoning behavior through confidence-aware steering, it is crucial understand how confidence manifests the model's internal representations. Since hidden states directly encode the evolving behavioral dynamics of a transformer during reasoning, they provide natural substrate for both amalyzing and manipulating the model's certainty. In this section, we demonstrate that confidence is not merely correlated with, but systematically and pre- dominantly linearly encoded in hidden states. This insight underpins an automated approach for identifying the most effective layers to target in steering interventions.
Confidence is discernible in hidden layers. From Eq. (T). we obtain stepwise confidence values. We also extract the hidden state H of the token following the delimiter \n This gives rise to direct mapping between hidden state and contidence:
- Ce.
Thus, the layer-i hidden state for sentence corresponds its confidence c.. As shown in Fig. we apply t-SNE to project the hidden states H. into two-dimensional space and color each point according to its corresponding confidence c.. Clear clusters emerge: high-confidence and low- confidence representations form visibly distinct regions, with this separation becoming even more pronounced in the mid-late layer embeddings. These observations indicate that confidence acts discernible signal in the hidden space, providing direct empirical support for our subsequent linear probing analysis.
Linear probing of the confidence signal. Building on the above observations, we employ a linear probing approach to examine how confidence is encoded in the hidden layers and to assess the
21

---

Published as a conference paper at ICLR 2026
strength of this linear relationship. Formally, the mapping is expressed as: Cs H + 66)
Here, w( and ((0) denote the parameters of the linear model.
Because hidden layer representations typically have several thousand dimensions, using a linear head directly may lead to overfitting and unstable estimation. To address this, we apply a standard dimensionality reduction method, PCA, to project the hidden states into a low-dimensional sub- space, and then study the relationship between the reduced representations and the corresponding confidence values. The detailed statistics before and after PCA are summarized Tab.
Co w (6) ZZ + 6(0)
Here, ~ denotes the low-dimensional representation obtained from the original hidden state H!!
The overall probe analysis pipeline is illustrated in Fig. 10 We We employ standard ridge regression approach to estimate the parameters (+() and b(4)
min
(0) 0 80))
+
w-(
After fitting the ridge-baut basee linear probe, we obtain the predicted confidence values as follows.
é w 6())
We assess how accurately confidence be predicted from the hidden representations by com- puting the coefficient of determination R² The formulation is given below. A higher R²² value, approaching indicates that confidence is more readily linearly decodable from the representations of the corresponding layer.
Rd = E, (C. -0)) E. (Ce 2)
Automated steering layer selection using R We evaluate the relationship between the confi- dence values e, and the hidden state ((()) across all layers of each model, shown Fig. A consistent pattern emerges: the coefficient of determination R2 typically reaches its maximum in the middle to late layers, indicating that c, is more easily linearly decodable from H in these layers. Since steering fundamentally operates through linear shifts in the hidden space, we select the layer with the highest R² the steering layer. The entire procedure fully automated, allowing the system to identify the optimal steering layer without manual intervention. In principle, choosing this layer minimizes the additional noise introduced by the steering operation.
Model
Original Dim
PCA Dim
Retained Var.
DeepSeek- RI - Distill-Qwen-ll Dii-sck--R---ittil---eeen QwQ-32B
1 536
64
0.889
3584
0.857
5 120
0.833
Qwen3-14RB
5 120
0.900
Table 7:
Cumulative explained variance retained by PCA (k = 64) across models. Higher retained variance indicates a stronger low- dimensional linear structure amenable to probing.
A.6 CONFIDENCE EVIDENCE UNDER VOCABULARY COVERAGE GAPS
A growing number of efficient reasoning methods mitigate overthinking by relying on predefined keyword vocabularies. Representative strategies include targeted suppression of specific tokens (e.g., NOWAIT (WAGG ET AL.., 2025A)), latent-space guidance that steers the model away from
22

---

Published as a conference paper at ICLR 2026
aa)
(b)
THE
(d)
Figure 8: t-SNE projections of hidden states sampled immediately after \ colors encode sentence-level confidence (0- 1). (a) DeepSeek-RR RI-Distill- Qwen- 1,5 layers 5, 20, 28. DeepSeek- -i-ekk--I-Dsslll Distill-QwennP7 layers 20, 35. (d) QwQ-32B layers 20,
layers 5, 20, (c) Qwen3-14B
states prone to emitting certain tokens (e.g., SEAL (Chen et al. 2025b). MANIFOLD STEER- ING (Huang al., 2025b)), and cue-word-drive early stopping, which treats designated trigger terms as checkpoints to terminate the reasoning process when appropriate (e.g.. FLASHTHINK- ING (JTANG ET AL.. 2025), TRIMR (LIN AL.. 2025A). DEER YYANG ET AL.. 20258),
23

---

Published as a conference paper at ICLR 2026
(8)
(b)
(c)
(d)
Figure 9: Layer-wise linear probe decodability (R²) of confidence. Mid-to-late layers achieve the highest
scores, motivating the steering-layer choice.
Ground
output the token-level probability P(G Xxx at each step
Head
Hidden Vectors
Next-Sentence Confidence
DecoderLayer
Hn
PCA
Linear Predictor
DecoderLayer
Layor-wise R² for confidence decodability.
DecoderLayer
H2
DecoderLayer
H1
Tokenizer
<think>Aright, so have...nivFFrret..cca written p-qeithinko>
Figure 10: Linear-confidence probe with PCA. Given an input sequence, we freeze the language model (I.M) and extract layer-wise hidden states. The representations are projected by PCA and passed to a linear predictor to estimate step-level confidence. We repeat this procedure across layers to obtain layer-wise test-set It² scores.
DYNASOR- COAAOOR-COTTA (FU ET AL., 2025)). However, the fundamental reason why such lexical inter- ventions improve reasoning behavior remains poorly understood. In this section, aim to uncover the mechanism behind their effectiveness by aggregating keyword vocabularies from representative methods and analyzing their relationship with model confidence. Our key finding is that vocabulary- based strategies are, in essence, incomplete approximations of confidence based control: they cap- ture only the most frequent lexical manifestations of low-confidence reasoning, while missing broader spectrum of uncertainty signals.
Category
Vocabulary
NoWait (suppress)
wait, alternativelly hmm, but, however, alternative, another, check, double-check, oh, maybe, verify, other, again, now, ah, any al alternatively, think differently, another way, another appreach, another method, another solution, another strategy, another technique
Transition
SEAL Reflection SEAL-Reflectio
wait, verify, make sure, hold on, think again, 's correct, incorrect, let me check, seems right
Table 8: Unified vocabularies for NoWait (keyword suppression) and SEAL transition/reflectionn cues.
To investigate this, we first compile the keyword sets used by NOWAIT and SEAL as illustrative examples (see Tab. S). Notably, these lexical items function surface markers of the model's epistemic uncertainty. Using DEEPSEEK- DI--SIILLL-WEN-T7 sentence-level confidence at each reasoning step and project it all words appearing in that
on MATH-500, we compute
24

---

Published as a conference paper at ICLR 2026
sentence. As summarized in Tab. the vast majority of these words associated with confi- dences below the model's overall mean (c = 0.8162). This pattern suggests a straightforward interpretation: vocabulary- interventions such NOWAIT and SEAL primarily suppress low- confidence modes of the model's reasoning, rather than targeting particular semantics per se.
As illustrated in Fig. 11 both SEAL and NoWait reliably elevate the model's confidence along the reasoning trajectory. This empirical pattern corroborates our analysis: NOWAIT achieves the effect by suppressing the emission of high-frequency lexical markers associated with low confi- dence, whereas SEAL steers the hidden representations away from states that tend to produce low- confidence, figh-frequency sentences. In essence, both methods act by attenuating the model's low-confidence modes.
However, vocabulary-driven heuristics do not, by themselves, capture the model's low-confidence modes. In practice, such methods identify only sub- set of high-frequency lexical correlates of low con- fidence, leaving a substantial long-tail of equally in- formative cues outside the predefined lists and thus
Confidence Distribution (KDE) DeepSeek 81 Distil Owen
Derityy (022
unmeasured. As illustrated in Tab. 9 we enumer- ate several representative omissions that most exist- ing approaches fail to account for. Consequently, confidence-based approaches systematically surface the model's low-confidence modes irrespeciiee of their lexical realization.
0.0
0.4
1.
Confideace
Figure 11: KDE (Confidence) NoWait vs SEAL.
Origin vs
This comprehensive extraction provides principled basis for subsequent research to diagnose and mitigate overthinking, enabling more complete coverage than vocabulary-drive ing forward, a fruitful research agenda is pursue a confidence-based line of work. One direction is to treat low-confidence states as actionable checkpoints for early exit, developing calibrated cri- teria and adaptive halting policies to further improve the accuracy-effiiinny trade-off of early-exit models. Another is to analyze the relationship between semantic (meaning-level) uncertainty and model-internal confidence estimates, thereby deepening our understanding of and ultimately miti- gating both overthinking and underthinking behaviors.
heuristics. Look-
B METHOD DETAILS
In this section, we provide more detailed introduction to the technical details of REBALANCE presented in Sec. 3 First, in Appendix [B.]] we formally define these reasoning modes and propose an explicit, confidence-based modeling paradigm quantitatively distinguish between redundant reasoning and premature conclusion. Building upon this foundation, Appendix B B.2
presents the Confidence-Basse Steering Vector Extraction, where we leverage hidden-state representations to derive directional steering vectors that guide LRMss reasoning trajectory towards optimal decision- making boundaries. Finally, Appendix B.3 details our Model Behavior-Baue Based Dynamic Control Function, which dynamically modulates steering strength according to real-time real-time confidence and variance metrics. By integrating these three sequential components, our framework achieves a robust and adaptive control mechanism, effectively balancing exploration and commitment in the reasoning processes of LRMs.
B.1 EXPLICIT MODELING OF OVERTHINKING AND UNDERTHINKING
Formal Definition. Let the reasoning trajectory inside <thi <think>... ... </thinka be split into steps S S.m... by the double newline delimiter \n),n introduced in Sec. 2.1 Let rgs denote the partial reasoning up step s. If the model is forced to stop after step and produce a conclusion from r<., it induces a distribution over answers which we denote by π.. Let d, arg max n, be the predicted conclusion under fixed decoding rule. Define the stability index
min {s de d, for all s' 2 8 and d, is correct).
25

---

Published as a conference paper at ICLR 2026
Count
209
3,012
491
60
77
384
2,223
1,728
288
1,734
5
5
6
5
18
6
11
5
15
27
10
31
10
30
12
8
45
Confidence
0.6549
0.6740
0.6881
0.6950
0.7040
0.7120
0.7223
0.7325
0.7710
0.7821
0.8182
0.5977
0.5342
0.5461
0.5602
0.5739
0.5809
0.5819
0.5871
0.5937
0.6038
0.6072
0.6098
0.6150
0.6164
0.6378
0.6380
0.6401
0.6413
0.64
0.6427
0.6433
0.6442
0.6445
Block D
another approach"
overcomplicating
Table 9: Low- confidence lexicon (items from the curated vocabulary are marked with and colored red).
Word/Phrase
muy beeybbe
hold on
suspect
I'm not sure
probably
Let me check"
hmm
incorret"
perspective
systematically
reason
annual
crucial
haven
homogeneous
altered
verification
schedule
unfolding
mentally
arrive
effectively
offset
handshake
paper
interpreted
wait"
now
neat
redo
may
mix
Count
115
101
1,857
729
101
154
734
13
5
75
10
6
8
10
10
42
5
13
11
10
7
13
Confidence
0.6537
0.6690
0.6844
0.6940
0.7115
0.7120
0.7180
0.7347
0.7490
0.7820
0.7900
0.5948
0.5093
0.5447
0.6460
0.5702
0.5809
0.5818
0.5869
0.5914
0.6033
0.6067
0.6082
0.6148
0.6162
0.6371
0.6379
0.6399
0.6407
0.6416
0.6426
0.6433
0.6442
0.6444
0.6451
Block C
Word/Phrase
think again"
confused
alternatively
in summary
any
seemss
not sure
again
possibly
wow
phew
casiest
overlooked
layer
separate
generating
systems
shake
discrininants
misupplicd
obvious
theory
translation lation
shapes
necewary
tang led
allows
solidify
precisely
mixing
extensions
abstract
shaking
safe
crossing
Count
13
71
417
866
339
43
339
776
10
288
92
10
11
7
21
15
11
12
8
8
I
13
38
16
44
6
Confidence
0.6375
0.6620
0,6821
0.6920
0.6988
0,7120
0.7161
0.7290
0.7357
0.7821
0.7919
0.5948
0.5066
0.5389
0.5470
0.5649
0.5797
0.5817
0.5866
0.5899
0.6028
0.6055
0,6076
0.6123
0.6162
0.5537
0.6126
0.6699
0.7318
0.6416
0,6422
0.6432
0.6442
0.6444
0.64488
Block B
another solution'
Word/Phrase
alternative"
confusing
another way"
double-check
correct
seems right
mandatory
partially
summed
misinterpret
diametrically
consuming
periodicity
absolutely
designed
uncertain
surprising
verifying
complicate
eighteen
assumptions
think
verify"
guess
other"
rely
consist
careful
accept
hard
relief
unsure
clarify
cell
Count
49
1
78
68
354
647
6,866
1,093
6
11
17
40
14
5
8
29
5
7
11
7
1
7
7
6
Confidence
0.0067
0.5990
0.6781
0.6914
0.6971
0,7050
0.7092
0.7228
0.7270
0.7700
0.7830
0.8187
0.4997
0.5359
0.5468
0.5612
0.5763
0.5811
0.5859
0.5895
0.5979
0.6046
0.6073
0.6100
0.6159
0.4954
0.6059
0.6559
0.7315
0.8295
0.6420
0.6430
0.6435
0.6444
0.6446
Block A
think differently"
another method"
verify (variants))
Word/Phrase
sorry
however"
let think
check"
anyway
believe
it looks like
summarizing
uses
uniquely
trick
substitutions
despite
clear
quickly
extends
worried
interpreting
elsew here
interpolation lattoon
concerning
surprised
doubt
concerned
nervous
triplets
version
nature
exploit
pyramid
but'
oh
26

---

Published as a conference paper at ICLR 2026
A trajectory exhibits overthinking if it continues generating steps after s*. Conversely, A trajectory exhibits underthinking if it stops at step with an incorrect d, while there exists >> s such that de would be conrect. These definitions capture redundant reasoning beyond the earliest stable correct decision and premature commitment before sufficient exploration.
Explicit Modeling with Confidence. We instantiate the above definition using the sequence of stepwise confidence {c₂) and confidence variance {r,} defined in Sec. [2.1 where (ar(c,; W.) and W, is sliding window. We can determine two-sided quantile thresholds (Hynd-)
man & Fan 1996) from small-scale seen dataset. Let Qe(g) and Qg))) be the empirical q-quantile of Cs and Us)-- Choose < qL < 9u < 1 and define
= Qc(an), Te Qo(un),
+. - Qulrr;
-e Qu(gn))
A step is tagged low-confidence if c₂ S Th and high-confidence if 2 T!" Similarly, A step is tagged as high-variance if v, 2 T!! and low-variance if U, S T. We then define the sets
o f Th and 2 + u+ {s 2 H and S T
As observed in Fig. Bb), high variance reflects frequent switching across reasoning paths and often co-occurs with low confidence; thus, we treat O as a proxy for overthinking. Persistently high confidence with low variance indicates stable yet potentially premature commitment, making U proxy for underthinking. Steps that fall outside both sets are considered to reflect a normal state and excluded from subsequent analyses.
B.2 CONFIDENCE- -AAEE STEERING VECTOR EXTRACTION
Building upon the explicit modeling paradigm, propose deriving steering vectors from deep-layer hidden representations to guide LRMs away from undesirable reasoning modes. These vectors are efficiently obtained via a one-pass collection performed only once per model prior deployment, eliminating additional computation during actual use.
One-pass prototype extraction. We prepare a small-scale seen dataset Dween and run the model per prompt. When the model generates delimiter \n), the next token marks the first token of a new step. At this token, we save deep-layer hidden states h,a) for step index and select layers f, chosen via probing method maximizing confidence separability on single dataset but shared across all datasets (see Appendix (..)). The first token of a step serves as a compact representation of the step mode for two reasons. First, it typically encodes the intent that sets the direction of the et al., 2025b), and due to the causal mask, all later tokens in the step condition on it. Second, deep layers show stronger distinguishability between the two reasoning modes in our empirical study, consistent with Gekhman et al. (2025); Skean et al.) (2025).
step (e.g., wait or alternatively)
Using the tags from sets O and U, we form latent distributions for the overthinking and underthink- ing modes. Then, we can obtain mode prototypes by averaging the extracted hidden states
h
1
Σ
m
O
SEO
Steering vector construction.
The difference between the two prototypes defines a steering vector
4 0 4 v
v
1 n 112
This vector encodes the transition direction in latent space from underthinking toward overthinking, with its negation representing the reverse.
During inference, we inject the stecring vector solely at each step's first token. Specifically, we modify the deep hidden state by
27

---

Published as a conference paper at ICLR 2026
h 1: = h, + Cta V.
Let e!!)) be its position and let as be scalar steering weight that controls strength X and direction ò, at step s.
0x ro ds:
200 d, (+1,-11)
Setting 8, +1 pushes the state away from underthinking and increases exploration. Setting 5g pushes the state away from overthinking and facilitates the model to coverage reason- able reasoning path. The values of X and 6, will be determined by the dynamic control function introduced the later section. Conceptually, the two prototypes act as boundaries of the model's reasoning process. Our goal is to keep the stepwise state between these boundaries so that the model reasons efficiently with balanced thinking.
B.3 MODEL. BEHAVIOR SIHAVIOR--BAEDD DYNAMIC CONTROL FUNCTION
Inputs differ difficulty, and the model's reasoning state evolves over time. To keep the trajectory between the overthinking and underthinking boundaries, we set the steering weight a online as continuous function of the current state. The function takes the stepwise confidence C, and the confidence variance as inputs, and outputs steering weight a that determines both direction 8 and magnitude The weight pushes the state away from the closer boundary and grows as the state approaches that boundary.
From a confidence curve to a control surface. To derive this three-dimensional surface, we first construct simplified two-dimensional curve f(c,) based solely on stepwise confidence ca. From the previous analysis, the steering weight a, needs to transition smoothly from a minimum negative value (away from overthinking) maximum positive value (away from underthinking) as confidence C2 increases. Many functions satisfy this requirement. Here, we adopt the widely used sigmoid as an illustration.
For ease of spatial transformation, we express the sigmoid function in terms of the hyperbolic tan- gent:
(cc.) = 1 2 + tanh
1
Co 2
Our goal is to spatially transform this sigmoid to align precisely with the overthinking and under- thinking boundaries. After transformation, the function becomes:
(cc, = a + lanh(k(c, + m));
where o, and m represent spatial transformation parameters, which can be obtained by fitting.
However, as detailed in Appendix A.4 confidence distributions vary significantly across models, making it difficult find universally applicable parameters. Thus, we propose model behavior- based fitting method. This method adaptively determines these parameters based on model-specific behavior, using the previously collected stepwise confidence es and confidence variance v, from one-pass prototype extraction without additional computational cost.
Specifically, after the one-pass extraction, we obtain hidden-state distributions and corresponding prototypes for overthinking and underthinking, from which derive steering vector v. Since the steering vector connects prototypes that represent their respective hidden-state distributions, the steering strength can be interpreted the displacement of these distributions along the vec- tor direction. Therefore, adjusting the magnitude this displacement enables us to capture specific behavioral characteristics of the model, allowing tailored data point generation.
To illustrate this, consider first the alleviation overthinking. Suppose the hidden-state distribution of overthinking is bounded. The aggressive displacement is defined the minimal distance required
28

---

Published as a conference paper at ICLR 2026
to shift all points within this distribution beyond its boundary. A more moderate displacement, how- ever, moves only the overthinking prototype outside the boundary, defining the moderate distance. We explicitly anchor these through specific behavioral criteria:
Anchor Al: At C2 Th the steering should yield negative moderate displacement, effectively guiding the state away from overthinking.
Anchor A2: At Gs 7 the steering weight is set to zero, as the state is considered to lie within the normal confidence region, and thus no additional steering is applied.
In contrast, mitigating underthinking poses unique challenges. Experimental observations indicate that LRMs may also consistently exhibit high confidence during normal reasoning, making direct numerical quantification of overconfidence, defining characteristic of underthinking tendency, dif- ficult. Therefore, we adopt a conservative mitigation approach. Recognizing that greater disper- sion in confidence distributions corresponds to larger distances between prototypes measured by the norm the steering vector, we select aggressive and moderate displacements for underthinking as small proportional fractions of this norm. This proportional strategy reduces underthinking with- out adversely affecting normal reasoning and ensures scalability across diverse LRMs' confidence distributions. This approach is anchored by:
Anchor A3: At c, the maximum normalized confidence, the steering provides posi- tive moderate displacement to mitigate excessive confidence and counteract underthinking.
We obtain these moderate targets from model behavior in latent space. Let prototype distance d = |e 1l and let s, v h, where ,ng denotes the hidden state of the first token generated reasoning step s, encoding the reasoning state that step. Thus, 9, represents the coordinate of the step's s reasoning state projected onto the steering vector v. Define separating threshold along by 2 T (p + p ") The moderate distance overthinking
d.m v 0
which moves the overthinking prototype to the boundary. The aggressive distance for overthinking is
d
max (52) t, SEO
which moves all overthinking steps past the boundary. For underthinking, we adopt a conservative rule since normal reasoning can also show sustained high confidence. We set
d.m P Pror
U.a
pù droo
with constants 0 < <P < Pb that are fixed and identical across all models. These distances scale with the separation between prototypes and thus naturally adapt different models. Because v has unit norm, a displacement of size along corresponds to steering magnitude d in the hidden space. We therefore fit f so that
f(+ -
-
f(1= UU..
We solve for b and & by least squares on these anchors. This yields smooth curve that produces negative weights at low confidence and positive weights high confidence, with zero at the center.
Lifting to variance aware surface. We denote by B the mode boundary that determines the maximal steering amplitude. Three distinct mode boundaries are employed. Bn, is the moderate boundary used outside the high-risk regions and is determined by the moderate fitting targets of f. B, and B., are the boundaries for overthinking and underthinking regions. Because the aggressive distances already encode how far the risky states extend from the boundary along v, we set
B, 0,a
Bi U.a
29

---

Published as a conference paper at ICLR 2026
Using aggressive distances boundaries provides a worst-case correction inside each high-risk gion. For overthinking, d is defined from the most extreme detected overthinking step, so steering with this magnitude is sufficient to push all detected overthinking states back to the boundary. For underthinking, we use the conservative construction of dU,a to reduce premature commitment while limiting disruption normal high-confidence reasoning.
We now incorporate variance to obtain a two-input control g(c,, v2). The idea to keep the sign and basic shape from f(c,) while increasing the magnitude near the two high-risk regions. The overthinking region is Cs S Te with vs 2 T The underthinking region is Cs 2 T with e S T. However, abrupt steering strength changes at region boundaries are undesirable. To mitigate this, we define smooth gates that approach one inside each region and decay to zero outside
Wo vss) ( (To Cs
(
Ux
(u((,, Us) = Cx
TC
Tv
Us
a
Tcc
77
where (z)) = 1/(1 + e ") The parameters 1)c
and ne > 0 control the width of the transition
and smooth the change region boundaries.
We then interpolate between the moderate boundary and the mode-specific boundarics using the smooth gates:
Bic,, U.) = Bm + (Bo - Bm) coo v.) ) + (Bu - Bm) eou (us
The final control surface is
g(Ca, U&) = sign 7 Blc,, v.) tanh 1a--Tg
(1c--1=")
For fixed Us. the map is monotone in cs- For fixed cs. the magnitude increases smoothly v, enters either high-risk region. The sign follows the confidence side so that the steering pushes away from the nearer mode boundary.
Online steering.
At step we set
a, u.C,
>=
5, sign
These values plug into the injection rule and separate direction and magnitude defined earlier. The procedure is training-free and uses only statistics that are already computed online. It adapts across models through the behavior-based distances and across inputs through the gates (c,, v. The result is a continuous controller that keeps the trajectory between the two mode boundaries and allocates more steering when the state drifts toward either boundary.
Function Visualization. We visualize the fitted mapping g(c,, v.) for DREPSEEK- -DDISTILLL QWEN-1.5B. Darker gaggioss-charaaterrieeee by high variance and low monfidenc-iinicann where the overthinking penalty is strongest. Conversely, the lighter, positive region low variance and high confidence marks where the underthinking penalty is strongest.
C ADDITIONAL EXPERIMENTAL RESULTS AND ABLATIONS
C.I ABLATION ON INDIVIDUAL AXES
We study univariate effects via axis-wise ablations of g(c., U.): define ge(cs) = 7) and
(v2) == g(č, Uz). where E and denote the means confidence variance v and stepwise con- fidence estimated the extraction set. As shown in the first three rows of Tab. I0] both univariate variants degrade REBALANCE performance, indicating that the bivariate form g(c, v) provides finer- grained and more effective control than single-variable schemes.
30

---

Published as a conference paper at ICLR 2026
13
-55
-55
15
-20
-35
-10
-30
0.00
0031.
0.8
0000
1.0
Figure 12: Left: heatmap of g(c,, va) with on log scale; Right: 3D surface of g(e., v.). Dashed lines mark Gis. fis and 425 97s-
Math500
GSM8K
Olympiad
Method
Pass
#Tokens
@
#Tokens
Pass@1
#Tokens
Full [o(c,
83.0
3474
78.3
765
43.9
7235
g(c) g(v)
82.6 +
3387 4
78. 4
8901
41.7 J
6612 I
76.6 I
3596 f
78.0
658
39.6
6876 |
Table 10: Axis-wise ablations the RI-1.5B backbone across difficulty levels. mance changes three math benchmarks with varying difficulty: Math500 (medium), GSM8K (easy). and Olympiad (hard). Metrics are Pass@1 accuracy (%) and generated token numbers. Arrows indicate change relative to Full g((, v) }; Acc. 1 increase, decrease; Tokens | decrease, increase.
We analyze perfor-
C.2 CROSS- DOOSS-DOMAN AND CROSS- DIFFICULTY TRANSFERABILITY
Cross-domain transferability. To validate the cross-domain transferability of ReBalance, conduct experiments on DeepSeek-R1-D -iias-----itttl---eeee... 1.5B, Specifically, we extract steering vectors and confidence statistics from coding (LiveCodeBencc) and commonsense (StrategyQA) tasks and transfer them to the mathematics domain (MATH-500). We also report the quartiles of the confi- dence distribution (q25c, 975c) and variance distribution (q25v, 975v) derived from each extraction dataset. For readability, confidence quartiles are scaled by 100 and variance quartiles by 1000.
As observed in Tab. II although the confidence statistics differ to some extent across domains, ReBalance achieves strong performance in all cases. Notably, extractions from Strategy QA can even achieve higher Pass @1 scores compared those from MATH within the same domain.
Cross-difticulty transferability. We categorize commonly used mathematical reasoning datasets by difficulty level: easy (GSM8K, AMC23), medium (MATH/MATH-500), and hard (AIME24, AIME25, Olympiad). Using the medium-difficull datasets as an intermediary, we examine two transfer directions, easy-to-medium and hard-to mard-to-medium, to explore how changes in task difficulty affect the transfer performance ReBalance. The experimental results on DeepSeek-PP -I--skiil- Qwen-1.50 are shown in Tab. 11
However, data distributions vary across different datasets, which may introduce confounding factors beyond just difficulty. Moreover, it is challenging to precisely quantify the difficulty gaps between datasets, posing signíficant challenges to the analysis. Therefore, we leverage the ground-truth difficulty grading within the MATH dataset and conduct analysis based on QwQ-32B (Tab. I1, performing an in-distribution, fine- mne-grained difficulty classification from Level to Level 5 within the same dataset.
Based on the experimental results, we have the following observations. Firstly, the higher the dif- ficulty of the extraction dataset, the lower the confidence and the higher the variance. This reflects
31

---

Published as a conference paper at ICLR 2026
Confidence Distribution
MATH-500
Dataset
q25c
77cc
g25v
q75v
Pass @
#Tokens
QwQ-32B
MATH
70.6
92.0
0.4
7.3
95.2
3662
Level-1
74.8
92.8
0.3
5.2
94.8
3447
Level-2
72.4
92.7
0.3
6.9
94.8
3623
Level-3
92.1
0.4
6.9
3678
Level-4
69.6
92.0
0.4
7.4
95.0
3696
Level-5
69.2
91.1
0.4
7.7
95.4
3720
Peepeek--l--Disiin wwwpssiiill-----..BB5
MATH
93.9
0.5
11.0
83.0
3474
GSM8K
76,9
94.4
0.3
6.7
80.6
3221
AMC23
61.8
94.2
0.4
10.4
82.8
3277
AIME24
63.2
92.1
0.5
9.8
83.2
3568
AIME25
90.2
0.4
3796
Olympiad
58.1
84.2
0.5
9.5
83.6
3862
GPQA
56.5
82.8
0.5
9.5
81.4
4260
LiveCodeBench
62.4
91.2
0.5
6.2
82.0
3482
StrategyQA
61.2
85.8
0.4
8.3
83.4
3667
Table 11: Performance Variation under Difficulty-Conditioned Control Surfaces. We investigate per- formance shifts induced by control surfaces derived from datasets of varying difficulty. For each difficulty tier, extract the steering vectors and fit the associated control surface, and subsequently evaluate them on DeepSeek-R SeepSeek-RI-Distil--e R--Distill-Qwen--1.5t and QwQ-32B. The results indicate systematic differences in Rebalance be- havior, suggesting dataset difficulty plays non trivial role shaping the resulting control dynamics.
the model's broader and more frequent exploration of reasoning paths, aligning with the decision rule we proposed in Eq. (5). where confidence serves as an indicator. Secondly, Extraction datasets of easy difficulty prioritize token reduction, while those of hard difficulty prioritize accuracy im- provement. We believe this occurs because easy extraction datasets exhibit higher confidence and lower variance; thus, according to Eq. (5), the range classified overthinking is wider, and the derthinking range is narrower. This results in more frequent triggering of the overthinking criterion, causing the dynamic control function to preferentially suppress redundant reasoning. The opposite occurs for harder datasets.
Therefore, we suggest using extraction datasets of medium difficulty (e.g., MATH) in practical ap- plications to achieve an optimal accuracy-efficiency trade-off.
C.3 P K AND YVG@K K PERFORMANCE ANALYSIS
The core issue addressed by ReBalance balanced thinking, i.e., mitigating overthinking while simultaneously preventing underthinking. Here, "underthinking" refers cases where the model is inherently capable of solving problem but produces incorrect answer due to insufficient reasoning. According the definition of Pass@k. this metric effectively grants the model multiple reasoning attempts and expanded exploration space, counting a question as correct if any one of the sampled solutions successful. Therefore, theoretically, for problems that meet the underthinking criterion, which are those that the model is truly capable of solving, the likelihood of obtaining a correct answer approaches certainty as the number of samples increases, eventually converging toward the model's capability ceiling (Karan & Du 2025). At this point, the impact of underthinking on accuracy becomes negligible, and ReBalance primarily serves to alleviate overthinking.
To validate the above analysis, evaluate Pass 22 over 20 samples and measure the average token length of generated sequences on the few-example datasets AMC23, AIME24, and AIME25. The results shown in Tab. 12
As can seen, consistent with our analysis, ReBalance significantly reduces reasoning length with- out any degradation in model accuracy. This demonstrates that our proposed confidence indicator accurately characterizes overthinking and underthinking. Thanks to this precise characterization, ReBalance prunes only genuinely redundant reasoning steps rather than essential effective ones,
32

---

Published as a conference paper at ICLR 2026
AMC23
AIME2024
AIME2025
Method
Pass @ 20
#Tokens
Pass @ 20
#Tokens
Pass @ 20
#Tokens
DeepScck-R-Distill Qwen-1.5B
Baseline
97.5
7430
63.3
10645
433.3
10447
ReBalance
97.5
4832
63.3
9243
46.7
8652
Baseline
100.0 100.0
6021
76.7
11249
66.7
11379
ReBalance
5264
76.7
8563
66.7
9019
Qwen3-14B
Baseline ReBalance
100.0 100.0
7331
90.0
11367
12717
5124
90.0
9247
80.0
10381
QwQ-32B
Baseline ReBalance
100.0 100.0
7034
14121 9853
13386 11586
5651
86.7
83.3
Table 12: Pass@20 Performance and Token Efficiency. ReBalance preserves accuracy while consistently reducing the token usage of baseline models.
Model
Avg@1 1
Tok
Avg@4 + Std
Tok@4 + Std
79.6
4516
79.6 0.004
4620 + 100.9
w/ ReBalance
83.0
3474
83.3 + 0.007
3553 55.5
DeepSeck-RI I-Dis-ill- tarill-Qwen-7BB
89.8
3699
89.4 0.007
3703 + 56.4
ReBalance
92.6
2903
92.6 0.001
2894 1 38.1
Qwen3-14B
93.8
4470
93.8 0.001
4550 1 38.8
w/ ReBalance
94.0
3641
94.1 1 0.001
3674 44.6
Table 13: Multi-sample evaluation MATH-500.
enabling lossless and efficient sequence compression even when the model operates near its capa- bility ceiling.
Following prior work (Jacch et al., 2024 Guo 2025), report Avg@4 on the large-scale MATH-500 dataset. For the faw-lampl benchmarks AMC23, AIME24, and AIME25, we also include Avg@ 16 together with Avg @4 evaluate the model's average case reasoning performance under both low-sampling settings and medium-sampling settings. As shown in Tab. 13 and 14 have the following observations:
Inter-dataset variability in randomness. As reflected by the standard deviations of Avg@k and Tok @k, datasets with very few samples exhibit substantially higher vari- ance-up order of magnitude larger than that of large-sample datasets such as MATH- 500. This affects both accuracy and generated sequence length.
Performance stability on large-sample datasets. On datasets with sufficient sample size, multi-sample evaluation has minimal impact on the relative performance between ReBal- ance and the baseline. The performance gap remains largely consistent across different sampling counts.
Performance stability few-sample datasets. Although both accuracy and sequence length fluctuate more significantly with increased sampling on few-sample datasets, the relative improvement of ReBalance over the baseline remains stable. Notably, under 16- sample evaluation, ReBalance yields even larger performance gains compared to the orig- inal single-sample setting across multiple datasets and models, further demonstrating its effectiveness and robustness.
C.4 PERFORMANCE VARIATION UNDER DIFFERENT CONFIDENCE DISTRIBUTIONS
33

---

Published as a conference paper at ICLR 2026
Model
Arg@1
Tok
Avg Std
Tok@4 Std
Arg 16 Std
Tok 16 Std
ATME34
SeegSeel-RI-Dissil---0
Baseline
23.3
12596
21.7 0.02
12897 + 830.7
19.6 = 0.03
12931 + 830.7 9179 602.6
ReBalance
36.7
9040
34.2 0.04
9179 718.4
35.6 1: 0.05
DeepSeek-RI-Disee
BenSSell-R--Distil---eeenn
Baseline
40.0
13994
41.7 0.03
13636 + 434.2
41.3 = 0.04
13764 + 418.2
ReBalance
9012
55.0 0.02
57.9
Qwen3-14B
Baseline
188.1
11174
ReBulance
73.3
9464
71.7 0.02
9627 115.3
73.1 0.02
9613 112.1
AIME25
DeepSeek-RR --iidd-RR--iiilil-nnn----.
Baseline
16.7
14556
15.0 + 0.02
14107 = 354.3
15.4 = 0.02
14589 + 416.3
ReBalance
30.0
8140
27.5 0.03
8447 447.0
27.5 0.03
8723 626.9
MeepSeel-RI-Distill R
Raseline
26.7
13778
28.3 0.04
12340 +: 308.3
27.3 + 0.04
12192 + 308.3
ReBalance
40.0
9227
40.0 0.02
8813 609.3
40.0 0.02
9241 463.2
Qwen3-14B
Baseline
56.7
13125
55.0 0.05
12900 + 241.7
54.4 0.06
12457 298.7
ReBalance
56.7
11057
57.5 0.06
11023 + 213.1
$7.3 = 0.07
11013 = 224.3
AMC23
DeepSeet-R1-Diiiee R.-Diittill--Qne--.S5
Raseline
55.0
8990
53.8 + 0.01
8616 + 302.6
54.1 = 0.02
8637 + 538.8
ReBalance
80.0
5216
80.6 0.04
4730 + 255.3
80.0 0.04
4729 433.5
Sonsseel-I--Disil--O
Buseline
75.0
6898
74.9 0.02
6297 520.2
74.8 1 0.02
6297 335,1
ReRalance
95.0
4767
93.8 0.02
4115 423,4
92.2 + 0.04
4226 394.4
Qwen3-14B
Raseline
95.0
6985
ReBalance
100.0
5230
98.8 + 0.02
5120 + 98.4
97.7 0.02
4848 190.2
Table 14: Multi-sample evaluation on AMC23, AIME24, and AIME25.
Confidence Distribution
Baseline
Rebalance
Temperature
925
92s
475
Pass @ (%)
Token
Pass @ I (%)
Token
0.2
0
0
68.4
6248
77.0
4335
0.4
0.9602
0.0006
72.6
5694
79.6
3906
0.6
0.8507
0.0039
79.4
4584
81.6
3689
0.7156
0.9845
0.0002
0.0098
81.0
4529
3485
1.0
0.5516
0.9449
0.0006
0.0164
82.4
4660
82.4
3488
Table 15: Performance of Rebalance under different temperature settings DeepSeek- -1-bekkk---iitllll Qwen-1.5B. Rebalance consistently provides higher accuracy and shorter reasoning traces from low- high- temperature settings, demonstrating stable generalization under varying confidence distributions.
To examine the generalizability of Rebalance across distinct confidence regimes within the same model, we keep both the control surface and evaluation samples fixed while varying the model's decoding temperature. This setup allows formance gains under different confidence distributions. As shown in Tab. [5] Rebalance behaves adaptively across decoding temperatures. At low temperatures, the model exhibits persistently high confidence, which frequently triggers the underthinking detector; Rebalance then encourages more diverse reasoning trajectories. Conversely, higher temperatures, the model produces higher vari- ance and lower confidence, activating the overthinking detector, in this regime, Rebalance effectively narrows the search space and accelerates convergence. As illustrated Fig. [3| the results provide detailed view of how Rebalance adjusts the model's behavior across temperature regimes. At lower temperatures, Rebalance effectively suppresses overconfident predictions, thereby reducing underthinking and substantially improving the model's accuracy.
assess whether Rebalance consistently yields per-
C.5 SEMANTIC CHANGE AND CREATIVITY ANALYSIS
Even though Appendix A.6 examines the relationship between the keyword vocabulary and con- fidence, it remains necessary to analyze the semantics of the model's generated outputs. To this
34

---

Published as a conference paper at ICLR 2026
(
hb)
(C)
Figure 13: (a) (d) show the mean confidence distributions of the Bascline and Rebalance models temper- atures 0.2, 0.4, 0.8, and 1.0, respectively. The trend reveals a clear iuperature-ddpendeen effeet: lower temperatures, Rebalance systematically reduces model's overconfident predictions, whereas at higher tem- peratures, it shifts the distribution upward, counteracting the underconfidence introduced by increased sampling randomness.
Reflection
Transition
Performance Change
Method
Word Count
TF
TE-IDF
Word Count
TF
TF-IDF
APass @
Tokens
DeepSeek- RI- Distill-Qwen-
Baseline
9.7
12.1
2.4
No'Thinking
1.7
4.6
-649%
NoWait
1.6
1.7
3.5
0.1
1.9
10
7.7 x 10
-1.6
-41.4%
Rebalance
10.5
6.4
9.3
1.7
1.0
2.3
+3.4
-23.1%
DeepSeek-RI
RR--iisill-Qwenme
Baseline
19.2
8.1
10.3
5.2
2.3
3.7
NoThinning
1.8
1.2
3.1
0.1
9.6
10
4.5
10
-9.2
-77.4%
NoWait
1.2
1.8
0.03
10 2.8
1.8
10
-3.0
-32,9%
Rebalance
12.8
6.7
9.2
2.7
1.4
2.8
+2.8
-21.5%
Qwen3-14B
Baseline
24.0
9.2
10.8
8.7
3.3
4.4
NoThinking
10.4
5.5
9.4
2.8
1.0
2.4
0.0
-0.55
NoWait
9.4
10
10
-1.0
-27.9%
Rebalance
13.2
+0,2
-18.5%
QwQ-32B
Baseline
13.3
NoThinking
26.0
11.5
13.7
3.8
0.0
-13.7%
NoWait
2.5
2.4
4.1
0.1
2.2
10
8.1
10
-1.0
-27.9%
Rebalance
22.1
10.8
10.1
5.4
2.5
3.8
+0.4
-19.3%
Table 16: Semantic statistics of Transition and Reflection vocabularies across different methods and mod- els, and corresponding performance changes. For each method each model, we report the total word count, frequency (TF), and TF-IDF score for both vocabularies, well the change in accuracy and tokens relative to the Baseline the same model.
end, we conduct systematic semantic analysis of the -aabbeek-R----ssiil---aann -waeill---ee---.BBBB reasoning traces using the Transition and Reflection vocabularies introduced in SEAL Chen al.) (20256). As shown in Tab. [6 we quantify the semantic changes of the model under NoWait NoThinking, and our method before and after steering. For readability, TF and TF-IDF scores are scaled by factor of 1000. We observe that both reflection and transition patterns decrease to varying degrees across all methods. However, the first two baselines suffer from noticeable accuracy drops. con- trast, our method preserves nog-negli amount of these reasoning patterns, which substantially contributes to maintaining, and in some cases even improving, the model's accuracy. Retaining re- flection and transition patterns appears to be a key factor for preserving reasoning correctness in large language models.
To evaluate whether applying Rebalance imposes any unintended drawbacks on creativity and the naturalness of the expressions, we further assess the models using the Creative Writing v3 bench- mark Paech (2025). The experimental results are summarized in Tab. 17
We evaluate four models using Sonude--oonet-4555 Anthropic (2024) as the judge model. For each model, we report three metrics: (1) Rubric Score, aggregate quality score across multiple writing dimensions; (2) Elo Score, a relative triting-qualite ranking calibrated with SBPT-3.5TuurboOpeene 2023) and DeepSeek-PP R1-Distill--Qeal 1.5B fixed anchors; and (3) Fine-grained Ability Scores,
35

---

Published as a conference paper at ICLR 2026
Score
Ability
Method
Rubric
Img
Pac
Dia
DeepSeck-R1-Disiie -ii-----iiiill-eeeee 1.5B
1.9
1.8
Rebalance
15.2
57.5
1,5
1,9
2.7
2.2
5.6
1.4
3,0
4.3
2.5
3.3
1.2
11.2
9,4
1.7
DenSeell-R--isiil----eeen
Baseline
22.9
308.9
4.8
4.8
2.7
9.9
9.6
Rebalance
23.4
349.7
4.3
4.5
4.7
5.1
6.7
3.0
4.6
6.6
4.1
5.4
2.9
9.9
9.9
3.5
Qwen3-14B
7.7
Rebalance
50.5
1368.0
12.2
7.2
11.6
11.3
9.5
12.1
12.3
8.2
9,9
6.9
8.0
9.4
12.6
9.0
10.3
QwQ-32R
Baseline
52.7
1438.2
12.0
11.7
9.8
13.6
12.6
8.5
9.8
9.6
10.6
Rehalance
52.8
1442.7
12.7
7.9
12.3
11.8
9.9
12.6
12.2
8.6
8.0
9.8
12.2
9.5
10.7
Owen2.5-78Innsrruct
Baseline
7.2
7.6
6.9
9.5
GPT3,5-Turbe
Bascline
52.8
1500.0
13.2
7.1
11.5
12.2
10.5
12.7
13.3
8.1
11.0
7.2
8.9
9.1
11.5
10.4
10.8
Table 17: Creative-writing performance on Creative Writing v3 benchmark. Ability abbreviations: Coh
Coherent; Crt = Creativity; Img = Descriptive Imagery; Pac = Pacing; Ele Elegant Prose: Inst = Instruction
Following: Voi Consistent Voice & Tone; Dia Strong Dialogue: Flo Sentence Flow; SDT Show-Don't
Tell; Ama Avoids Amateurish Prose; Emo Emotional Depth; Pos Avoids Positivity Bias; Pur Avoids
Purple Prose; Ch Believable Characters.
covering fifteen creative-writing competencies: coherent (logical consistency and structural clar- ity), creativity (originality and non-templated expression), descriptive imagery (vivid, sensory-rich description), pacing (appropriate narrative flow), elegant prose (fluency and stylistic refinement), instruction following (accurate adherence to instructions), consistent voice & tone (stable narrative voice), strong dialogue (natural, character-appprorriate dialogue), sentence flow (smooth transitions between sentences), show-don's t-tell (conveying meaning through scene and action rather than ex- position), avoids amateurish prose (avoidance clichés or novice patterns), emotional depth (nu- anced emotional expression), avoids positivity bias (avoidance of forced optimism), avoids purple prose (restraint from overly ornate language), believable characters (psychological plausibility and consistent character voices).
can be observed that after applying ReBalance, the models generally maintain and even improve their performance in creativity and the naturalness of expressions. The percentage of metrics that are higher than or equal to those of the original models is notably high: 71% for DeepSeck-RI- Distill-Qwen-1.5B 88% for QwQ-32B. Notably, Qwen3- 14B achieves significant improvements across all metrics after apply- ing ReBalance. We posit that this improvement stems from ReBalance's ability continuously and gently guide the reasoning process, achieving a balance between overthinking and underthinking, thereby maintaining the model within effective reasoning boundaries. Consequently, the models exhibit measurable improvements in creative- writing performance, indicating that ReBalance accel- erates convergence without compromising the model's capacity for innovative or divergent thinking.
100% for Qwen3-14B, and 65% for
We additionally evaluate Qwen2.5-2 (eeen2.577B-- 5-7B-Instruct and find that its creativity scores are substantially higher than those of the distilled DeepSeek-R1 R1-kssill -ii--iiill--Qwe--7B, further supporting our observa- tions. Our analysis reveals that distillation systematically reduces linguistic diversity, property that is closely tied to creativity. Likewise, task-specific fine-tuning may limit creative expression by reinforcing existing patterns at the expense of novel exploration.
In contrast, Rebalance induces form of cognitive restructuring the model's internal reasoning process, which partially restores and enhances creative expressiveness. These findings highlight an important direction for future work: developing methods that improve reasoning stability without suppressing linguistic diversity creative generation.
C.6 CONFIDENCE CHARACTERISTICS OF OVERTHINKING AND UNDERTHINKING
Fig. b) in the main text highlights& a core contribution of our work: confidence serves as a con- tinuous and reliable indicator for explicitly modeling overthinking and underthinking. In Fig. 2b),
36

---

Published as a conference paper at ICLR 2026
Nurmal (O-base)
Overthinking
Normal (U-base)
Underthinking
Model
Conf.
Var.
Lon.
Conf.
Var.
Lon.
Conf.
Var.
Len.
Conf.
Var.
Len.
DeepSeek- RI--Distill- Qwen- Distill-Qwen- DeepSock-RR Bit--RiID--iQwaasseee Qwen3-14B
18.5 1357
78.6
21.3
85.1
89.7
1726
82.4
12.1
QwQ-32B
84.0 16.1
3712
75.4
22.1
6377
76.2
18.5
4573
78.9
1.76
3080
Table 18: Comparison of Confidence, Variance, and Output Length Across Thinking Modes. For cach model, we report statistics for Normal responses paired with Overthinking (Normal (O-base)) and Underthink- ing (Normal (U-base)), together with the corresponding Overthinking and Underthinking stales.
DeepSeek-RI CrepSeek-RI-Disee R-----sill----------- 1.5B MATH-500 for visualization. To demonstrate the gen- erality of this observation, we extend the experimental setup to include four model sizes (1.5B to 32B) across three distinct model families. Tab. T8 presents comprehensive quantitative analysis of stepwise confidence and confidence variance across these models. For clarity, confidence values are scaled by 100 and variance by 1000.
We can observe that, across all models shown in Tab. 18 the results consistently align with those of Fig. bb: Firstly, overthinking samples exhibit lower confidence and higher variance, suggesting hesitant and repeated switching between reasoning paths, often leading to redundant reasoning; Sec- ondly, underthinking samples show higher confidence and lower variance, and this persistently high confidence often causes the model to prematurely commit to an incorrect reasoning path, hinder- ing thorough exploration. This observation further confirms that confidence can serve as general indicator, broadly applicable across various large reasoning models.
C.7 PERFORMANCE COMPARISON WITH TRIMR AND FLASITHINK
Since the official implementations of TrimR (Lin et al. 2025a) and FlashThink (Jiang et al. are not publicly available, we reproduce both methods for fair comparison.
2025)
Reproduction of TrimR. We adopt the reflection tokens given the paper to split reasoning into fixed-interval sub-thoughts and feed them into a verifier model for answer detection. We use streaming inference pipeline that monitors generation through -ccoppplopeaebble endpoints. Since the step size is unspecified, set it to 100 tokens, consistent with the original Fig. 8. Both the overthinking woo.kiiiin-ommmssssoon module (via answer convergence) and the module (via budget monitoring) are enabled accordingly. In the original study, TrimR was exe- cuted on Ascend NPUs with the NPU-native Pangu-7B model serving as the verifier. Since we do not have access Ascend NPUs, experiments instead conducted NVIDIA RTX PRO 6000 GPUs and adopt Qwen2.5-7BBIInttruc (Yang et al. 2025a) as the verifier, following the other configuration outlined in their paper.
In TrimR, R denotes the underthinking threshold defined in the original paper. Specifically, the reasoning process is terminated once reaches R% of the maximum sequence length, with 0.5 used by default. Since TrimR is originally validated only on models of 32B parameters or larger, we conduct our comparison using QwQ-32B to ensure a fair assessment of its strengths. Despite TrimR's use of an additional 7B auxiliary model, an extra inference stage, and post-hoc optimization via repetition truncation, ReBalance still achieves significantly better performance. Moreover, our analysis of the R parameter reveals that increasing R leads to notable accuracy gains for TrimR on challenging benchmarks such as AIME24 and AIME25. This observation further supports the hypothesis that existing approaches designed to mitigate overthinking inadvertently induce underthinking, highlighting a critical trade-off in current reasoning frameworks.
Reproduction of FlashThink. For FlashThink, we follow the paper's core procedure, i.e., seg- menting the chain-of-thour using delimiter tokens and invoking verifier model for early exit. Like TrimR, we employ a streaming inference pipeline that monitors generation via OpenAI- compatible endpoints. Each detected segment is forwarded to the verifier (Qwen2.5-7B-Bl) I-Istrttt using the original prompt template. The main results for TrimR and FlashThink are reported in Tab. I with more detailed comparisons provided in Tab. T9
37

---

Published as a conference paper at ICLR 2026
QwQ-32B
MATIL 500
AIMECI
AIML2S
GSMRK
AMC23
GPQA D
P
STokens
Passi81
VTokens
Pasei1
#Tokeus
Passih
#Tikens.
Pae011
Pasci
#Tiskess
Trimut
threchold=0..5
threshold-0.75
94.8
4048
70,0
10235
53.5
L1397
96.7
1410
87.5
6778
69.2
7312
Bacshold=]
11726
95.9
6890
Flashthink
mor Inkemanl 6000
94.8
56.7
6157
9613
96.3
90.0
5200
64.7
tnkems-32000
94.6
2834
66.7
11390
11218
96.7
5596
66.2
5962
Rellalance(ours)
700
10350
11575
96.8
6064
Table 19: Performance comparison with TrimR and Flashthink on QwQ-32B, All experiments are run with the same sampling parameters.
AMC23
AIME24
AIME25
Olympiad
Mothod
Pass@1 1
#Tokens
Pass @
#Tokens
@sss@
#Tokens
Pass @
#Tokens
DeepSeek-RIe --iisill-Qween-B Siilll--een--.5P
Baseline
55
8990
23.3
12596
16.7
14556
41.2
8785
Dynamic Temperature
75
7344
36.7
12054
23.3
11659
44.7
8538
Dynamic Steering
80
5216
36.7
9040
30.0
8140
43,9
7253
DeepSeek RI RI-Distill- Seenill-Qeen-7B
Baseline
75
6898
40.0
13994
26.7
13778
$6.1
7590
Dynamic Temperature
82.5
5856
53.3
10593
36.7
10740
57.5
7498
Dynamic Steering
95.0
4767
56.7
9012
40.0
9227
57.0
6321
QvQQ33BB
Baseline
87.5
7021
66.7
14342
46.7
13350
66.7
8219
Dynamic Temperature
92.5
672.
66.7
11202
53.3
12134
67.6
8160
Dynamic Stecring
95.0
6064
70.0
10350
63.3
11575
68.6
7422
Table 20: Performance comparison across three model sizes under different inference-time control methods.
Overall, while FlashThink and TrimR effectively reduce token usage, they incur significant inference-time overhead. Frequent verification interrupts disrupt the decoding process, stall the pipeline, and degrade VV-cache efficiency; moreover, deploying a separate verifier increases sys- tem complexity. In contrast, ReBalance introduces minimal overhead, maintains uninterrupted de- coding, and simultaneously reduces token consumption and latency, offering a more practical and self-containee solution.
C.8 BALANCED THINKING WITH DYNAMIC TEMPERATURE
In this work, use steering as an illustrative example to practically implement our idea of balanc- ing overthinking and underthinking. Notably, the concept itself is generalizable and applicable to variety of approaches aiming promoting balanced thinking. To support this argument, inspired by Zhang et al. (2024) and et al. (2024), we replace the dynamic steering component in ReBalance with much simpler approach: adjusting the temperature parameter. Specifically, when overthinking is detected during inference, we reduce the temperature to avoid excessively divergent exploration leading to redundant reasoning. Conversely, when underthinking is detected, we increase the tem- perature to broaden reasoning. Due to constraints in time and computational resources, conduct only preliminary experiments using a discrete, binary hyperparameter setting without further tuning. the temperature 1.2 upon detecting underthinking.
Still using confidence the indicator, and reduce it to 0.7 upon detecting overthinking.
The comparative results summarized in Tab. 20 Even with such simple configuration, balanced thinking approach achieves significant performance improvements and reductions in rea- soning length. Therefore, we believe the performance can be further enhanced by introducing model behavior-basee dynamic function fitting method similar to ReBalance, which naturally en- ables adaptive continuous regulation without manual hyperparameter tuning.
C.9 ADDITIONAL PROTOTYPE CONSTRUCTION STRATEGIES
38

---

Published as a conference paper at ICLR 2026
In Sec. 3.2 of the main text, we provide two definitions for overthinking and underthinking in Eq. (3) and Eq. (5) respectively, and select Eq. (5) as ReBalance's 's explicit modeling approach. To address potential concerns, present a comparative analysis and experimental validation of these two definitions.
Comparative analysis. Although Eq. appears more concise and intuitive, it serves only as a theoretical definition of overthinking and underthinking. Its purpose is to provide a conceptual distinction between the two phenomena through a formalized expression. This definition operates at the trajectory level, classifying an entire reasoning trajectory as overthinking or underthinking by comparing it against an idealized stability index that acts as a decision boundary. Consequently, Eq. (3) were directly used as the indicator for steering vector extraction, it would inevitably lead to the following issues.
Mismatch in operational granularity. Our method aims to adaptively adjust the model's behavior based on the real-time reasoning state at each step. This requires a step-level, fine-grained indicator to detect tendencies toward overthinking or underthinking. To avoid a mismatch in operational granularity, we therefore maintain the same step-level resolu- tion during the steering vector extraction. However, Eq. 3 only supports trajectory- classification of reasoning modes, and this granularity mismatch may lead to suboptimal performance.
Limited applicability. The stability index requires access to ground truth, which intro- duces an additional dependency on labeled data. Although one could follow Lin et al. (2025a) using external verifier to approximate ground truth by assessing the existence and equivalence of answers across consecutive sub-thoughts, this strategy still relies on the verifier's capability, prompt design, and hyperparameters for determining answer equiv- alence. Consequently, it may incur extra engineering overhead and introduce potential performance bottlenecks.
Difficulty in capturing complex reasoning dynamics. Eq. defines the stability in- dex only when all subsequent reasoning steps yield identical answers that exactly match the ground truth. However, existing studies have shown that the accuracy of reasoning models is not positively correlated with reasoning length (Chen et al. 2024b); on the con- trary, longer reasoning sequences may introduce more hallucinations (Liu et al. (025a). Therefore, while Eq. (3) offers convenient and intuitive way to conceptually distinguish overthinking from underthinking, its rigid requirement makes it unsuitable practical indicator in yeal-world scenarios, given the inherent complexity and variability of actual reasoning dynamics.
Experimental validation. To quantitatively validate the above analysis, we perform steering vec- tor extraction using Equation 3 as follows. First, consistent with existing methods, we randomly select 500 seen samples from the MATH training set and feed them into DeepSeek- -1-cc------itill RI-Distill-Qwen- 1.5B. We collect the generated output sequences and record the model's confidence each reasoning step. To identify steps corresponding to overthinking and underthinking. following [in et al. (202a) Bwen2.5-7B-Inntruue
and al. (025), we step contains the ground truth (see Appendix G for the prompt template). Once a step containing the ground truth is detected, is designated as the stability index: all preceding steps are classified as underthinking, and all subsequent steps as overthinking. The labeling procedure produces 29,701 701 overthinking samples and 24,710 underthinking samples, showing that our partitioning strategy is reasonable and results in a well-balance dataset.
(Yang et al.) 2025a) to determine whether each
To analyze the relative positional distribution of stability indices within the entire thinking sequence, we divide the range of relative positions (0-1) into 10 equal intervals (bins). We then count the num- ber of occurrences of stability indices falling into each interval, as shown in Tab. 21 Notably, an interesting observation emerges here: the stability indices exhibit bimodal distribution along the thinking sequence, with pronounced concentrations around Interval I and 10. These two Intervals correspond to the underthinking and overthinking behaviors of reasoning models, respectively, fur- ther providing strong empirical support for the necessity of balanced thinking.
The subsequent steps, including steering vector extraction, fitting of the dynamic control function, and dynamic steering during inference, are kept identical to those in ReBalance, The experimental results, along with the comparison of steering vector norms || 2, are shown in Tab. 22
39

---

Published as a conference paper at ICLR 2026
Interval
6
10
Relative Position Range
[0.0, 0.1)
(0.1,0.2)
[0.2, 0.3)
[0.3, 0.4)
[0.4, 0.5)
[0.5, 0.6)
[0.6, 0.7)
[0.7, 0.8)
[0.8, 0.9)
[0.9. 1.0]
Count of Stability Indices
27.41
16.45
10.96
9.87
6.58
3.29
2,85
2.19
3,95
16.45
Table Distribution of stability indices over relative position intervals.
||
MATII ()
MATI1-500 (FTokens)
AIME24 (Pass@P
AIME24 (MTokens)
Baseline
79.6
4516
23.3
12596
Vector Extraction w/ Stability Index
11.4
81.8
4715
20.0
12563
Vector Extraction Confidence
62.4
83.0
9040
Table 22: Comparison of steering vector extraction variants.
We observe that the experimental results obtained using the steering vector extracted via Eq. ( sig- nificantly outperform those of Eq. (3) in both accuracy and efficiency, strongly validating our anal- ysis above. Moreover, the difference in ||S'|2 reveals that when switching to stability index-based extraction, the norm of the steering vector becomes substantially smaller. This indicates blurring of the boundary between overthinking and underthinking, further explaining the performance gap between the two methods.
C.10 COMPARABILITY WITH NPU DEVICES
To further validate the generalization of REBALANCE, conduct experiments on the Ascend 910B NPU plat- form using the NPU-native OpenPangu-Emmeedeeeee
A1ME2025
GSM8K
Method
Pass@1 1
#Tokens
Pass@1 1
#Tokeus
V1.1 (Chen al. 2025a) model in its slow-thinking mode. Our evaluation covers both simple (GSM8K) and challenging (AIME25) problems. Compared to the baseline, REBALANCE consistently improves reasoning accuracy and efficiency, as shown in Tab. 23 Method adaptation details available in GitHub repository.
Baseline Ours
73.3
14552
94.5
76.7
9417
94.3
1363
Table 23: Evaluation Ascend 910B SaennPann--Emeededd
slow-
thinking mode); REBALANCE improves Pass@ while reducing token usage.
D DETAILS ON EXPERIMENTAL SETTINGS
Benchmarks. Evaluation is conducted on mathematics reasoning datasets: MATH-500 (Light- man et al. 2023b), AIME24 (AI-MO) 2024a), AIME25 (OpenCompass) 2025), AMC23 (AI-MO) 2024b), GSMSK (Cobbe al. 2021). and OLYMPIADBENCH (He et al. 2024); scientific rea- soning dataset, GPQA DIAMOND (Rein et al. 2024 commonsense reasoning dataset, STRATE- GYQA GGea et al., (2021): and code reasoning dataset, LIVECODEBENCH (Jain et al., 2024).
Evaluation metrics. We implement REBALANCE in both Hugging Face Transformers Wolf at 2019) and vLLM (Kwon et al. 2023b). We evaluate using Pass@ 1 (t) and the average number of generated tokens Tok (I). Unless otherwise specified, results are reported with the Transformers implementation.
Backbone reasoning models. We conduct experiments on open-source large language mod- els used backbones: DEEPSEEK-R1H R1-DISTILL--QW QWEN (1.5B and 7B) (Guo et al. 2025). QWEN3-14B (Yang et al. 2025a), QwQ-32B (Team) 2025), and OPEN PPPNAANGUEEMEEDDEDD-- V1.1 (Chen al. 2025a Together, they span 4 model architectures and 4 parameter scales, en- abling controlled comparisons across model families and sizes.
Steering extraction and dynamic function fitting. From 500 randomly sampled MATH (Hendrycks et al., 202I) problems, we estimate steering vector and a control sur- face for each backbone once and hold them fixed across all benchmarks.
40

---

Published as a conference paper at ICLR 2026
Baseline methods. We compare REBALANCE against representative training-free freee methods for efficient inference that do not rely on auxiliary models. ii) PROMPT-BASED methods:
CoD (Xu et al.) (025a) and NOTHINKING (Ma et al,, 2025b); (ii OUTPUT-BASED methods:
NOWAIT (Wang et al. 2025a); (iii) DYNAMIC EARLY-EXIT methods: DYNASOR-COO (Fu al. 2025). DEER (Yang al. 2025b). FLASHTHINK Tiang al. (2025), and TRIMR Lin et al.
(2025a) (iv) STEERING methods: SEAL (Chen et al. 2025b) and MANIFOLD STEERING (Huang
al. 2025b). This set covers the major design paradigms (prompting, output-time control, early exiting, and latent steering) under a training-free setting. Further details on additional PROMPT- BASED baselines are provided in Appendix
Hardware. All experiments were conducted on a single server with 8x NVIDIA RTX PRO 6000
(Blackwell Server Edition) GPUs.
Decoding settings.
Unless otherwise noted, we use nucleus sampling with
temperature 0.7, top_p 0.95, max.generated_toke 16000.
The same decoding configuration is applied across both the Transformers and vLLM backends.
E DETAILS ON BENCHMARKS
MATH-500 (moderate; 500 problems). Comprises 500 problems spanning arithmetic, algebra, geometry, and calculus, with varying difficulty levels. It evaluates a model's ability in complex mathematical formalism, equation solving, and structured reasoning. (Lightman al. 2023b)
AIME24 (hard; 30 problems). An Olympiad-style set assessing logical deduction and advanced problem-solving skills; includes official AIME problems from the 2024 cycle. (AI-MO) 2024a
AIME25 (hard; 30 problems). An updated set from the same AIME competition as AIME24, continuing target high-level deductive and multi-step mathematical reasoning. (OpenCompass) 2025
GPQA DIAMOND (hard; 198 problems). A challenging graduate-level subset with multiple- choice questions authored by domain experts biology, physics, and chemistry. (Rein et al.) (0024)
AMC23 (simple; 40 problems). An aggregated 40-problem set based on AMC 12 (2023 A/B). Items are multiple choice and span the standard high-school curriculum (calculus excluded), posi- tioned below AlME-level difficulty. (AI-MO) 2024b)
GSM8K (simple; 1319 problems). Grade-school and middle-school word problems emphasizing short chain-of-thougee arithmetic reasoning; commonly used split is ~7.5k train / ~~1 test. (Cobbe)
et al. 2021
OLYMPIADBENCH (hard; 675 problems). A bilingual math+physics Olympiad-style benchmark sourced from international/nationaal olympiads and Gaokao, substantially more challenging than standard competition datasets. (He et al. 2024)
STRATEGY QA (simple; 2,780 problems). Yes/No questions requiring implicit multi-hop com- monsense reasoning; each example provides a decomposition and supporting evidence passages. (Geva et al. 2021)
LIVECODEBENCH (hard; 400 problems, vl). A contamination-awwrr coding benchmark con- structed from
problems (e.g.. LeetCode, AtCoder, Codeforces). Tasks require generating runnable programs that are judged by execution-basee based unit tests, emphasizing algorithmic reasoning, data-structure design, and implementation fidelity. We use version v1 with 400 problems. (Jain et al. 2024
41

---

Published as a conference paper at ICLR 2026
F DETAILS ON PROMPTS
Math-(MATI (MATH-500 AIME 2024, AIME 2025, AMC23, GSM8K, Olympiad).
System Please reason step by step, and place the final answer inside \boxed
< </Ueer|> Iquestion]
Science- GPQAA
<I Systemm2 Please reason step by step, and place the final answer inside \boxedi
<| User|>> [question]
Answer with the choice letter only, in (boxed(). not include option text.
Commonsense StrategvQA.
System|> You answer binary commonsense questions, Think step by step, then output exactly one final line: \boxed(Yes) or box)d(No). < User|> [question]
Answer with \boxed(Yes) or \boxed(No) only.
Code LiveCodeBench.
<serr|>
AAA Instruction: You will be given question (problem specification and will generate correct Python program that matches the specification and passes all tests. You will NOT return anything except for the program.
Question:
[problem]
Ensure that when the pythen program runs, it reads the inputs, runs the algorithm and writes output to STDOUT. python # YOUR CODE HERE
## | be>s|im_starrr stassstttnttthiie
Prompt Used for Steering Vector Extraction in Eq.
42

---

Published as a conference paper at ICLR 2026
You Answer Detector for reasoning blocks of large language model.
I will provide a gold answer and ONE sentence from the model's internal thinking process.
Your task: Determine whether THIS thinking sentence already clearly contains the correct answer.
Gold answer: (gold_answer) Thinking sentence: (thinking_sentence))
Respond ONLY with a JSON object, in this exact format:
(found: <yes/no>}
Rules:
1. Output yes if this thinking sentence: explicitly states the gold answer, OR
gives sethhmmttiaalllyssennttiii
equivalent expression,
CR
computes a value that uniquely determines the gold answer. 2. Ot Otherwise output ne.
3. No explanations, no extra fields, no additicnal text.
4. Only judge THIS sentence. Ignore all context.
5. Ignore notation differences, equivalent arithretic, and paraphrases.
G DETAILS ON PROMPT-BASER APPROACHES
From the main experimental table, we observe that prompt-based approaches can, to a certain extent, mitigate redundant reasoning. This represents one of the simplest methods for altering model behav- ior. The specific details of the currently popular "Magic Prompts" are summarized the (Table 24
Method
Prompt Modification Please reason step by step.
CoT
Wei et al.
2022
CoD
Xu
al.
Think step by step, but only keep minimum draft for each thinking step, with 5 words at most.
2025a
CCoT
Renze &
Think step by step. Be concise.
Guven
2024)
CCo1-2-45
Nayab
Let's think bit step by step and limit the answer length to 45 words.
2024
BTC
Ding et al.
Rapidly evaluate and use the most effective reasoning shortcut to answer ques- tion.
2024
NoThinking
Ma
<think> Okay, have finished thinking. </think>
2025a
Table 24: Prompt modifications used in different reasoning strategies.
Modifying prompts is not in conflict with our proposed method. In fact, we integrate our approach with prompt-based techniques our experiments and observe that QwQ-32B achieves further per- formance improvements on several datasets. For instance, the MATH-500 dataset, replacing prompt reduced the number generated tokens from 3662 to 3064 without compromising accu- racy, demonstrating the complementary benefits of prompt refinement. Notably, existing research has shown that even with the same prompt content, different positioning strategies can significantly affect both the accuracy and efficiency of large language models (Cobbina & Zhou 2025). However,
43

---

Published as a conference paper at ICLR 2026
main experimental results also indicate that prompt-based methods not always effective, as the model does not consistently follow instructions. Regarding the broader issue of model con- trollability, substantial body of research has already explored the use of reinforcement learning (RL. techniques to achieve more reliable control over large reasoning models (I (LRMs) (Aggarwal & [Wlleck 2025) Yuan al. (0024). Building upon these insights, exploring how to effectively com- bine multiple training-free freee strategies with advanced control methods remains an important direction for future work.
H DETAILED DISCUSSION OF RELATED WORKS
Chain-of- Thoough (CoT). CoT prompting elicits intermediate rationales and markedly improves multi-step reasoning (Wei et al., 2022;; self-consistency further aggregates diverse chains (Wang) et al., (022). Beyond a single chain, search/verification variants under the -heeughh um- brella include Tree-of-Thoughts (Yao al. 2023), Stream-of-Search (Gandhi et al.. 2024), Graph- ThoThugghss (Besta et al., 2024). Process Reward Models (Lightman et al. 2023a), and RL-based Self- Self-Correction (Kumar et al.) (202). A converging view is that judiciously increasing test-time compute via multiple paths, search, or verification can rival or surpass pure parameter scaling for reasoning (Snell et al. 2025).
Latent Reasoning. Latent reasoning shifts the thain-of-thought from discrete tokens continu- ous hidden representations, reducing tokenized traces and sampling while preserving intermediate signals- thus improving test-time efficiency. Representative approaches include soft thinking with gated hidden-state signals (Zhang et al. (025d), training models to reason directly in continuous latent space via internal activations (Hao et al. (024), compressing long CoT into dense vectors (Cheng & Van Durme, 2024), and ceiistant-guidee soft CoT that maintains multi-step structure in latent form (Xu et al. 2025b).
Post-Traine Training Methods for Efficient Reasoning. Post-training approaches reduce test-time cost by shaping models' use of chain-of-thought (CoT) after pretraining. We group them into SFT-based and reinforcement fine-tuning (RFT)-based
SFT-based. Supervised fine-tuning can induce conditional brevity: paired supervision under matched conditions (e.g., long vs. short) teaches when concise reasoning suffices (Kang et al. 2025). A complementary line supervises compressed rationales with an explicit compression control at
ference; more principled compression schemes further improve faithfulness and controllability (Xia Yuan et al., 2025).
al. 2025
RFT-based. Reinforcement fine-tuning directly optimizes the accuracy-efficiency trade-off via re- ward shaping and preference learning. Examples include difficulty-aware ranking to form preference data followed by SimPO optimization (Shen et al., 2025); ; fixed accuracy-length rewards with PPO
Arora & Zanette (205);; self-adaptive CoT learning with GRPO (Yang al. (025cc;; and dynami-
cally weighted rewards that balance accuracy and length during PPO training (Su & Cardic 2025). Control-token policies decide when to think using GRPO, and explicit reinforcement of how long to
reason enables length control (Aggarwal & Welleck 2025).
Overall, these methods teach models reason when necessary and remain concise otherwise, im- proving the seuurac-llaeecy/tonn trade-off without increasing parameter count.
Steering- Besering-Based Methods for Efficient Reasoning. Recent work explores steering mechanisms that intervene directly in a model's latent states improve reasoning efficiency without retraining the backbone. Early work has already examined steering in the prompt space to elicit more con- trollable model outputs (Liu al. 2023). and has also leveraged steering-baser methods to mitigate hallucinations (Liu et al. 2024). Recent work has extensively explored using steering- based methods to enable more efficient reasoning. SEAL (Chen et al., 2025b) partitions model thoughts into exe- cution, reflection, and transition phases, and uses small amount of training data to bias the model toward the execution mode. MANIFOLD STEERING (Huang et al. 2025b) constructs redundant ver- concise datasets based on response length and keyword density, derives steering vector from them, and applics it to all tokens across all layers. REASONING STRENGTH PLANNING (Sheng et al. 2025) further introduces pre-allocated direction vector injected into the activation corre-
44

---

Published as a conference paper at ICLR 2026
sponding to the <think> token, whose magnitude encodes the desired reasoning strength in terms of the target number of reasoning tokens, with steering consistently applied at each layer for every generated token. Unlike the static steering methods above, CONTROLLING THINKING Sin et at. 2025h) introduces a dynamic variant. They construct steering vectors by pairing long and short correct responses and extracting the hidden states of the last token in the first two reasoning steps at chosen layer. A sliding-window controller then adjusts the steering strength based on token- level difficulty, measured via the Jensen-Shannon divergence between shallow- and deep-layer logit distributions, decreasing or increasing the magnitude according to a window-specific threshold.
However, the adjustment mechanism remains fundamentally oieediretiooaal in how steering mag- nitude is updated. Moreover, the steering- based methods above focus primarily on mitigating over- thinking. yet overlook a complementary issue: alleviating overthinking often introduces or amplifies underthinking (Wang et al. 2025c). REBALANCE addresses this gap through a bidirectional dynamic steering mechanism that uses real-time confidence during reasoning as an indicator to assess the ten- dency toward overthinking or underthinking, dynamically adjusting both the direction and intensity of steering to achieve efficient reasoning with balanced thinking.
Early-Exit Methods for Efficient Reasoning. A prominent line of work toward efficient reason- ing involves enabling models to exit early from their reasoning process once sufficient evidence for an answer has been gathered. Representative approaches such as TRIMR (Lin et al.. 2025a) and FLASHTHINK (Jiang et al., 2025) employ an external instruction followiin model to monitor the target model's 's ofann-of-thouhtt when the monitor deems further reasoning unnecessary. it halts the process and triggers answer generation. Other methods, including DEER (Yang et al. 2025b) and DYNASOR- -OTT (Fu al. 2025), instead rely internal signals, such as the model's confidence or entropy over candidate answers, to determine an appropriate exit point. While these strategies effectively reduce token consumption by forcibly terminating redundant reasoning, they all share fundamental limitation: the decision to terminate is binary and coarse- coarse-grained, typically applied at the level of the entire reasoning path (e.g., sub-thoughts in TRIMR or reasoning chunks in FLASH- THINK). This rigid binary selection risks discarding potentially valuable reasoning steps, thereby inducing additional underthinking (Wang et al. 2025c). Although TRIMR includes certain mecha- nisms addressing underthinking, it primarily opts for abandoning reasoning upon detecting under- thinking, which leans towards engincering-driie -driven token length optimization rather than genuinely addressing underthinking itself. Moreover, both TRIMR and FLASHTHINK depend on handcrafted keyword triggers to identify termination points, limiting their adaptability across models and tasks.
In contrast, REBALANCE departs fundamentally from this paradigm. Rather than merely mitigating overthinking through early exit, ReBalance is explicitly designed to simultancously mitigate over- thinking and prevent underthinking. It achieves this not by discarding reasoning paths, but by lever- aging confidence as an indicator to dynamically detect the model's tendency toward overthinking or underthinking in real-time. Based on this detection, it adaptively adjusts the strength and direc- tion of steering. thereby dynamically controlling the model's 's behavior to keep its reasoning state consistently within the reasoning boundary. This enables balanced thinking without requiring any additional verifiers or inference stages, making ReBalance an efficient, dynamic, and fine-grained reasoning acceleration approach.
Acceleration for Efficient Reasoning. Beyond training-time efficiency, complementary line of work targets inference-tine acceleration-ree ceeeiiinnneddennnggneeeee latency and improving throughput under fixed hardware budgets. One class of methods exploits speculative decoding, drafting tokens with lightweight proposer and verifying them with the target model to amortize compute (Leiiahha et al,, 2023). A second class reduces memory and scheduling overhead via paged K VVcacche management and continuous batching, enabling high-utilization serving at scale (Kwon et al. 2023a). A third line optimizes the sampling process itself: Monte Carlo tree or search-style strategies for data synthesis
(Li et al. 2025). best-of-n reasoning accelerated by speculative rejection (Sun et al. 2024), and carly-decoding schemes that self-estimate the necessary to balance quality and cost (Wang al. 2025b).
Small Language Models (SLMs) for Efficient Reasoning. A complementary line of work pur- sues efficient inference by compressing or transferring reasoning ability into smaller backbones. First, quantization can degrade multi-step reasoning if applied naively, calling for calibration-awan
45

---

Published as a conference paper at ICLR 2026
Method
TPS
TPR (8)
Additional GPU Memory (GB) #Tokens
DeepSieas-R--Dsitill--ewa
Bascline
80.2
174.6
13994
NoThinking
80.1
55.2
0.0
4427
Col
80.2
145.5
11663
FlashThink
73.8
135.9
18.3
10034
TrimR
48.8
147.7
15.4
7213
SEAL
78.8
128.3
0.0
10112
ReBalance (Ours)
78.5
114.8
0.0
9012
QwQ-32B
Baseline
20.5
698.6
14342
NoThinking
20.6
509.6
0,0
10507
Col
20.7
552.0
0.0
11438
FlashThink
14.9
673.4
18.3
10034
TrimR
6.5
1289.8
15.7
8345
SEAL
508.8
10344
ReBalance (Ours)
20.5
504.4
0.0
10350
Table 25: Efficiency analysis on SeepSeck-R--Disilll-weess and QwQ-32B.
schemes and mixed- -eeeddppecision designs (Liu et al. 2025b). Second, distillation transfers long-horizon reasoning into compact policies by (i) shortening and regularizing chain-of-thouge toouugttttttt traces and (ii)
internalizing deliberate reasoning into fast feedforward behavior (Luo et al. 2025 Yu et al. 2024).
Third, pruning/compression benchmarks sparsity and related compression knobs complec rea- soning tasks, revealing sensitivity to where and how compression applied (Zhang et al. 2025b Finally, recent assessment reports the combined effects of distillation and pruning SLMMs, high- lighting regimes where small models recover strong reasoning at fraction of the compute (Srivas- lava al. 2025).
EFFICIENCY ANALYSIS
We conduct comprehensive efficiency evaluation of ReBalance by comparing its inference over- head against both the baseline and other efficient reasoning methods. Specifically, we report four key metries: tokens per second (TPS), time per request (TPR), and additional GPU memory sumption relative to the baseline.
Specifically, to ensure thorough comparison, include prompt-based methods (NoThinking, CoD), carly-exit methods Think, TrimR), and latent steering methods (SEAL). Different from existing methods, ReBalance preserves effective thinking process by promoting exploration when necessary to avoid underthinking, particularly when the model faces difficult reasoning problems. As result, it more likely incur efficiency overheads challenging datasets. In the following. we adopt AIME24 for the efficiency evaluation, as shown in Tab. 25
Token generation efficiency: By observing TPS, it be that since the confidence utilized by ReBalance can be directly obtained from the log probability of each token' 's decoded output, and the dynamic function introduced in this method is very lightweight, the proposed dynamic adjustment logic has negligible impact on the single-token generation time compared the baseline.
Reasoning time acceleration: As shown TPS and #Tokens, ReBalance significantly shortens reasoning length without compromising token generation efficiency, yielding 1.5x and 1.4x TPR speedups over DeepSeek-RI/ -iiitiii--Qweree and QwQ-32B, respectively.
Additional GPU memory usage: Although ReBalance requires the of extracted steer- ing vectors during reasoning, their GPU memory footprint is minimal (e.g., the vector size for QwQ-32B is only 22 KB). In contrast, early-exit methods, such as FlashThink and TrimR, require additional verifiers (usually at least 7B), introducing extra GPU memory usage and communication load.
46

---

Published as a conference paper at ICLR 2026
In conclusion, ReBalance achieves outstanding performance in tokens per second, time per request, additional GPU memory usage, and generated sequence length.
J THE USE OF LARGE LANGUAGE MODELS
In this work, large language models are used exclusively for polishing the writing and checking grammar. They not involved in research idcation, experimental design, data analysis, or the formulation of conclusions. All substantive intellectual contributions are made by the authors.
K ETHICS STATEMENT
We confirm that this research adheres to the ICLR Code of Ethics. Our method is purely algorithmic and theoretical in nature, involving human subjects, personal data, real-world deployment that could lead to harmful, biased, or discriminatory outcomes. We have carefully considered poten- tial ethical cmplications-inntueeee the proposed approach poses no significant ethical concerns. All experiments were conducted on publicly available datasets.
fairness, transparency, and societal impact-and conclude that
L REPRODUCIBILITY STATEMENT
To facilitate replication, we fix the random seed 42 for all sampling and evaluation. All exper- iments are run on the same hardware (8x NVIDIA RTX PRO 6000 GPUs) with unified decoding configurations (temperature=0,
top-p=0.95. max www.geeerrae....mm woweesettteeeeee000 vectors and control surfaces are extracted once from random sample of 500 MATH problems and then reused across all benchmarks. Together with standardized evaluation metries (Pass and average token length), these settings ensure that our results can be reproduced reliably.
Steering
47

---

Published as a conference paper at ICLR 2026
M
CASE STUDY
Question:
For what real values of Sx$ is $-4xxx
(2) (4)-<<x <215 satisfied?
Answer[-iisgr(33, (3])
Express your answer in interval notation.
have this inequality solve: -4 <x* + 4xe 21. Hmm, okay. looks like quartic inequality hecause of the x term. I remember that to solve such inequalities, can break it down into two separate inequalitiss and salve each xxx and solve x* 4xx* 21. After that,
Generated Responses Aringnnittttnnnknnniiingggg
part separately. That first both parts to get the final answer ininl et me start by rewriting the inequality:nlmi4 < x* + 4x < 21, 21/nnSS,, I'll split this into two 41n2. x 4x** 21aniSttrrting with the inequality: x > 4.innHimm x is always non-negative since any real number raised to an even power is positive or zero. Similarly, 4x is also non-negative for the same reason. So, adding them logether, 4x" is always greater than or equal to zero. to be greater than -4 -47ininWell, since x + 4x* is always at least zero, and zero is greater than -4, that inequality x + 4x* > -4 la always true all real numbers x. So, the part doesn't impose any restrictions on x. That's a relief; I don't have to werry about excluding any from the solution set because the left inequality is always Inlnsow moving on to the second inequality: xt 4x* < 21, Thin one more challengingg Let's try simplify rearrange Maybe can set y xx to make easier handle. Since x² is always non-negative, y 0. Subsiituting, the inequality becomes y 4y < 21. St.in let me write that outininy* 4y 21 < O.ininow. can this quadratic inequality terms of y. Firat, let's find the roots of the equation ya 4y 4y-21 D Diminnaing the quadratic
the intersection the solutions from
parts//nnn1
here, the right side is which is negalive, So, possible for 4x"
formula: = [-b a ((b? 4ac]] / 2a. ininHere, a =1 b,b 4, c= 21 ininnnugg in, we get:ininy = 144 + ((16 4-1"(-) 2in= [-4 <(16 84)] 2in= [-4 v100)/ 20 [-4 a 10) 2 2tninSo the roots are/niny (-4 1022- 6/2 3, (-4 10)2 (-14)2 -7.mBButt since y = x and x can't be negative, y can't be -7, So, the only relevant root is y 3 Now, to solve the inequality y + 4y 4y-21 21 < intervals determined by the roots. Since the quadratic opens upwards (because
coefficient y posilive). the quadratic will be below zero between is two roots. However, one of the roots is negative (y = =-7), which we can ignore because y negalive, we only consider from to 3.wnn the inequelity y + 4y 4y-21 21 < holds when y ls between 0 and 3, Le.. < < 3.nnnBut remember. y X 80 subsiitutigg back, we x*> that means x * 0. But we have to consider x² 3 as well Snin x² <3 implies that [x] 133, which means is in the interval (-13.
we can analyze
is non-negalive. So,
relevant interval from y = -7 to y= S. But since y can't
gettininn x 3.iiinThs inequality implies xf between 0 and To find we
square roots. conts.nniFFirst
second inequality xf 4x+x on. No, actually, since x² > 0, the inequality x² < holds for all in (-13, (3) except x n 0. But actually, when x 0, + 4x² - D, which 4x'
i3j.innnPuttiing all together,
satisfled when x between 333 and v3, excluding zero? Wait hold
less than 21. So, actually, x= l mellued//nnnWW found that must be between and 3. Since y= x5, that translates to x between O and 3. So, x² 3, which is equivalent to |X| < v3, zero because when zero,
let's tink again. When transformed
Into y + 4y- 22
meaning x is in (-13, 13). So. actually, Interval is from -33 to v3, including zero WnnWalt, but in the first inequality x 4xx 4 is always true. So, combining both inequalites is in (-13, +3). aigtt/ but when is zero included. So, the solution as all real numbers x such tha is between -13 and v3 Nnnnt let me double-check this. Let's test a and 21. So, valid. WninWhat about = Y3? Then, x" + 4x* = (13)44 + 4"(y3/22 - (9) + 4"(3) 9 + 12 21, which is equal to the upper limit. So, = v3 la not included because the iequuaiity strict 21). Similarly, -13 also not includad includad///inWhar Whhnt about = 2? Then, x 4xx
0. which indeed less than So, the
solution is
such
4x' is zero, which is than so it's
value inside the interval, say x= 1. Then, x 4x*= =1 4 5, which between
16 32, which greater than 21, so it's excluded. So, indeed, the solution is between 333 and v3, not including the endpcints. WninWart but let me check
0.0625- 4*(0.25) 0.0625- so it's valid, IntaWhat about x= 1.5 which still less than 13 (since 13= = 1.732). x + 4x² = (1.5 (1.5)4 + 41 5.0625 + 44(2.25) = earlier, 4x
0.5. which inside interval. x* 4x+= (0.5)% 4"(0.5)2
= 1.0625, which between and 21,
5.0625 14,0625,
than 21, so It's valid Hnn about x V3- 1.7323
exactly 21, which isn't included, Similarly x 13 gives 21 as minSo, putting it all together, the solution is all real numbers x such that -13 is greater than -4, so the first inequaliyy doesn't restrict x at all. So, the only restriction comes from the second inequality Inin combining both incqualities, the solution x (-v3, v3) InlnBBu just make sure, this interval that satisly both inequalites. For example, X = 2. which is outside, 8s we saw. gives x + 4x* 32, which is greater than 2 gives the same result. What about x = v3, which is included IninSo, yeah, seems that the only x that satisfy both inequalilies are those in the interval (-13, v3). Therefore, in interval notation, that would be (-13, WaitttWWai, just double-check there any way xf 4x be less than or equal -4? Since x + 4x is always non-negative, it can't be less than -4 So, the only time when it's less than 21 is when it's between 0 and 21. and
which B3.tn/nBu wail, let me think again about the first inequality. Since x + 4x" always greater than equal wnaplityyinnnneeeeooooo Therefore,
consider there
any points outside of
excluded. Similarly,
saw, gives 21, which excluded. x 0. gives
increases or decreases, grows. So, when does reach 21? AiX = 213, as we Thw.rfnnherrforre the solution is indeed x E (-13, v3). So, in interval notation, that's written as (-x3, Y3) Inl wail, just to make sure, let me consider between So, we had y + 4y 21 < with y x 2 We found that y is between and S, which corresponds -v3 and 13,ininYes, that seoms consistent IninSo, think can confidently say that the solution is all real numbers x such that x is "nThe solution is Whoxed[[--asppe (isgr(())) insthink........... Therefore, the solution the
But 4x" is 0
the quadratic
between v/3 and " inequality W-4 < x*4 + 4xx2 < 21l) (einii/mlboowed((teartt (3)
Answer, But answer very long. (1 Loken)))
Figure 14: A ---iieei------iiiiI---------- w.Se inference case shows that, although the model arrives at the correct solution, its reasoning is rife with redundancy and repetition. Beyond the necessary boundary checks, it inspects numerous inconsequential points, incurring substantial token overhead.
48

---

Published as a conference paper at ICLR 2026
Question:
For what real values of Sx5 is (44-x"(44++4X" (2) 4215 satistied?
Answar((-lagnt(3), (s]rt(3))
Express your answer in interval notation.
Generated Responses: "Okay, so have this inequality to solve: 44 +x²+ Hmm, need to find all real numbers that satisfy this. Let me think about bow to approach this, inniFrst, I notice that the expression is a quartic polynomial. but it's only in terms of xt. Maybe I can make a substitttion lo simplify it. Lei me sel y x². Then, inequality becomes handle because a quadratic in terms of ynn'nSo now have two inequalities solve/ninl. y 4y 4'm2. y 4y 21' tackle the first one: y 4y
y" 4y 21. That seems easier lo
can rewrite this as ye + 4y + 4- D. Hmm, that looks like a perfect square. Let me check: (y ( 22 + 4y + +. Yes, that's correct. So, the incquality becomes (y + 2P > Whenn is a square greater than zoro? Well, squares are always non-nogativee so (y+-yy+2y +2 is greater than zero when y + is not equal to zero. That means # -2, But wait, y was defined as x², and is always non-negative. So, y can't he negative. Therefore, y -2 is not possible because can't be negative. So, the inequality always true except when - -2, which isn't in our domain. So, the first inequality is always satisfied for all real except when -2, hut since y can't be negative, this inequality doesa't restrict all. So, the first part doeen't impose any restrictionsnnnnNNGG
2y
moviing the second incquality: y² 4y <21. Led
To solve this quadratic incquality. need to dy 21 I can the quadratic formula: y- [-b sqri(b' - 4ac]] (2s). Here, a- I, b,b- 4,
this as 4y-
find the roots of the equation
-21 21.nWnnaaluuaaiig the discriminant: 67 4ac - 16- ((14((2)2)) - 16 84 100, That's perfect square, which is nice. So, the roots are two roots'nnall. (-44+ 10)2 6/2 y (-4 (-4 10y2 (-14y2-
[-4 [onn((0)]//// 2 [-4 = 10] /2///2//mnnt /aannlallug
-7urnSoo, the are y= and -7. Since y and cnn't negative, can't -7. Therefore, relevant root is
3 3.nninNow, the quadratic y 4y factors as (y- 3)y 7). But since y can't be negative, only consider y So, the quadratic is negative between its roots. But since one mot is negative and the other positive, the quadratic is negative between y- 7 and y- 33. However, since y can't
02 relevant interval y between and Thherfhrr.... the incquality y 4y-
holds that not zero,
when O < < Since y x², this translates s0 x # 0. The incquality x² < implics that -3qi((3) <x sqn(3). Combining these, we get (gri((3) <x < and 0 0xxx x sqrt(3). So, in interval notation. that's (-sgri(3), 0) union (0, sqr((3)) /nnnnBtt wait, lot me double-check. The original incquality was xt 4x* 21. trans formed to y* 4y seems correct in n'nBut just to he thorough me test some values to make sure. innr. lat' test - 1. Then, xt 4x 1 +4+ m Is between 44 and 21? Yas, it Inin What about x= sqrt(3)? Then, x 4g² (sqrt(3)) + (*(qqr(3))22 = (9) 4*)3) = 9 + 12= 21. So, 21 the upper bound. but inequality is striet, so x sort(3) not included. Similarly, x -sgm(3) is not included 'n' about included? in//tinkk--nnn
xxx3 Now.nNow, let's solve 0 <. The inequality
which gave
y 3,
g 3, which indoed -sgr((3)
syr((3) excluding x-0,
solution involves substituting y x", tramsforming the inequality into a Auadratic, and solving for The resulting interval for is y < which translates to being in (ssgi(3), 0) union (0, sqn((3))/ninn in Answer:* The solution in interval notation is Whoxed (-Sogrt (3). 0) Reup (0, (3)).
o? Then, x 4x-
0, whiclyss between
21.
UnderThinking, Should Double-Check
Answer short, incorrect (8
Figure 15: DeepSeek-RI -i-cck--I-DDstlllll Qwen- 1.5B inference case, applying existing overthinking mitiigatoonn techniques reduces token usage relative to the baseline; however, the absence of verification steps results in an incorrect answer.
49

---

Published as a conference paper at ICLR 2026
Question: what real values of Sx5 5-4<x> (4) (215 satisfied?
Ansgtrt-llggt(3,, (3qrt(3))
Express your in interval notation.
Generated Responses:" "Okay, so have this incqualiit to solve: x4xxx* +xxxx 21. Hmm, it's quartic incquality because of the tern. remember that for inequalities with polynomials, especially even degree ones, the graphs tend to have U-shape on both ends. So, the function fix) = x** + 4x² will go infinity as x approaches both positive and negative infinity. That means function will have a minimum somewhere in the middle ''nFirst, maybe should try to solve the incquality step by step. It's like seporate inoqualities: x + 4x" > 44 and x² 1 4x" < 21. Bul since x' and x' are always non-negative (because any real number squared is non negativveee, and raising the fourth power is also non non-negative), their sum will also be non non- negative, So, xt 4x" always greater than or equal to always true because non-negative number always greater than -4. So, don't have to worry about that part, it docsn't restrict the solution set no, the real work solving the second inoquality: xt - 4x--xx < 21. Let mo rewrite that as x² - 4x'--1 21 < O. Maybe I can factor this expression or find its roots to figure out where it's negative. innnLet me try to factor x² + 4x. 21. Hmm, noticing that it's quadeatic terms x can let x², so the equation becomes y + 4y 21 0. Let sqri(
zero. That means first inequality, x++ 4x's
sqr[[1 84)] /2- - (-4 10)2 6/2 = 3, and y (4- 1012 =- -14/2 = -7. So, y = or = -7.niiuut y = x2, which is always non-negative, so -7 docan't make sense in this context. So, the only relevant solution is = Therefore, x² sox sqri(3) or x -agrt (3 AninSo, that zero x -gri(3) and -xgrt(3)) Since quartic function opening upwards (because the sqri(3) and -sgrt(3), and will be below the x-axis between these twa rosts, In'nSo, the inequality xf 1 4x" 21 < holds when x is between ssqr(() and sqr((3). Therefore, the solution to the original is the some as x 4x2 < 21, which is when between -qri( and sqr(3) /nnnBuu wait, should check
solve for Usinn the quadrutic formula: y
sqr((100)) [100]] /2 2- [.4 10j/2/n'nSa. the solutions
incans the quartic function
coefficient of is positive), the graph will touch the x-axis
incquality - x** + 4x² there any other intervals or points where the inoquality might hold. Since quartic is always greater than equal zero, and we're looking for it to be less than 21, which positive number, it's only between the two roots where it dips below 21, An'nSo, that means x must satisfy =xgr((3) x surt(3). Let me verify that with some test points.in'nFor For x 0: 0* 4"00)² O. which between -4 16
and 21. so that's gn x
5, which between 44 and
that's nn
32, which greater for
than 21, so that's ousside the solution inin
sorr(3); approx 1.732, function equals 21, that's the boundary/''nSimmiiii = -3)r((3): same ceault/n'aSe the interval froth -sqr(() to sqr((3 is where the function is less than 21, and since the other incquality is always true, the solution is just that interval Tithherrfoo,,
interval nolation, the solution is (-sgrt(3), syri(3) laia" " "Fiul
Answer The solution liboxee noting that bip14 + 2%) is always non-negarive because it is sum of squares. Therefore, the incquality Vyxh4 + 4xx2 > 441) is always true. Next,ext, we focus on the inequility 90x"4 + 4x"2 221)) We rewrite this as '0''4 4x 21 < 09). Letting My = 2-2)), we transform the equation into a quadratic; Myn2 + 4y- 21 = 06)). Solving this quadratic equation using the quadratic formula, we find the mools:/n/niiium
(3 (s)
solve the incquality 10-4 xnd 4x*2 218), we start by
S-racc-4 (-4 ipm Vaqrt 16 8433 (2) Afrae (-4 fpm 10) (2) (2]a'jaan gives buyy 39) and My 71)). Since (y - x*2 must be non negative, we discard Myy - -79). Thus, 1 - 31)) gives Vix Vasrr (3)9) or Vix - 3 (hth fanction lixx44 4x 211) is quartic that opens upwards and is zero at Wix (3gr1(339) and Mfx (3)9) It negative between these roots. Therefore, the incquality 10004 4x 219 holds for Woll) in the interval 19(6 Rsgrt(3), (egr( (30)) the solution to the original incquality W < x + 4x < 211) is//n/ www.a///llbbbboedddoooo (- (3)r
Elficienlly Check the Boudary.
Answer short and Correct (875tokens)
Figure 16: In DeepSeek-RI 1iiit-R---DD-------- ---iisill--Qeen-1.51 inference example, the incorporation of judicious verification steps yields correct and succinct response.
50


Reviews:
[{"review_id": "DZqhzqzxLw", "reviewer": "Reviewer_RNpb", "summary": "This paper addresses a key challenge in LRMs: the trade-off between overthinking and underthinking. The authors propose ReBalance, a training-free and plug-and-play framework that dynamically controls reasoning behavior via internal confidence signals. Experiments on 4 models and 9 reasoning benchmarks show that ReBalance reduces reasoning token length significantly while improving or maintaining accuracy.", "questions": "see the weakness above", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. An important problem that is of strong interest to the LRM community.\n\n2. The paper uses stepwise confidence and confidence variance to detect reasoning modes, with a motivation that is well-explained and intuitively convincing.\n\n3. The method achieves superior results across a wide range of reasoning benchmarks.", "weaknesses": "1. The main text lacks a concise introduction to the baselines, which makes the paper more difficult for readers to follow.\n\n2. Novelty of this paper is limited. Steering vector has been used before. How does ReBalance differ from SEAL? Both of them leverage the steering vector. I want to see an apple-to-apple comparison. \n\n3. ReBalance is similar in spirit to adaptive halting or early-exit reasoning models, such as TrimR [1] and FlashThink [2]. I noticed that the authors cited these two papers, but neither compared them as baselines nor discussed them in the Related Work section of the appendix or other places.\n\n[1] Weizhe Lin et al. TrimR: Verifier-based training-free thinking compression for efficient test-time scaling. arXiv preprint arXiv:2505.17155, 2025.\n\n[2] Guochao Jiang et al. FlashThink: An early exit method for efficient reasoning. arXiv preprint arXiv:2505.13949, 2025. (edited)", "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": "JfaJhF63Fb", "reviewer": "Reviewer_HMxB", "summary": "This paper addresses the issues of \"Overthinking\" (redundant computational steps) and \"Underthinking\" (insufficient reasoning paths) in Large Reasoning Models. The authors propose REBALANCE, a training-free framework that aims to achieve efficient and \"balanced thinking.\" The core idea is to use model confidence and confidence variance as real-time indicators to dynamically steer the model's hidden states. The framework uses a pre-computed \"steering vector,\" derived from reasoning mode prototypes extracted in an offline pass, to guide the model's reasoning trajectory. A dynamic control function modulates this vector's strength and direction, pruning redundancy during overthinking and promoting exploration during underthinking. Experimental results show that the proposed method outperforms existing baselines and achieve better efficiency.", "questions": "1. The intervention method is specifically developed to target the paper's definitions of Overthinking (low-confidence/high-variance) and Underthinking (high-confidence/low-variance). However, could this direct manipulation of internal reasoning states have other unintended side effects on reasoning, beyond just mitigating these two defined issues? For instance, could it negatively impact creativity or the naturalness of the expressions?\n\n2. While the quantitative results are strong, have the authors analyzed the semantic changes in the model's reasoning process post-intervention? For example, did the REBALANCE framework lead to a measurable change in the frequency of keywords associated with reflection or hesitation (e.g., \"wait,\" \"alternatively,\" \"let me check\")?", "limitations": "", "rating": 10, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. Clear Optimization Objective: The paper defines Overthinking and Underthinking based on numerical features of internal states (stepwise confidence and confidence variance) derived from empirical observations. This semantic-agnostic definition, independent of specific keywords, provides a very clear and quantifiable optimization objective.\n\n2. Extensive Experimentation: The paper provides comprehensive experiments and ablation studies. The REBALANCE framework is validated across multiple models of varying scales and tested on diverse domains beyond mathematics, demonstrating strong generalization. Furthermore, thorough ablation studies are conducted for key design choices, offering justifications for the method's components.\n\n3. Demonstrated Effectiveness: The experimental results demonstrate that the proposed approach can successfully balance the trade-off between Overthinking and Underthinking. It outperforms previous baselines in terms of task performance and token efficiency.", "weaknesses": "1. Missing Citation to Key Related Work: The paper focuses significantly on the problem of \"Underthinking\". However, it fails to cite an important and highly relevant recent study on this specific phenomenon [1]. This omission is a significant gap in the related work section.\nReference:\n[1] Wang Y, Liu Q, Xu J, et al. Thoughts are all over the place: On the underthinking of o1-like llms[J]. arXiv preprint arXiv:2501.18585, 2025.\n\n2. Practical Application Burden: Although the method is presented as \"training-free,\" it remains highly dependent on hyperparameter tuning. Furthermore, it requires an additional \"seen dataset\" and a model-specific calibration step (to extract steering vectors and fit the control function). This introduces an extra implementation and tuning burden for each new backbone model, which may complicate its practical application.", "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": "a3xjRAccOO", "reviewer": "Reviewer_4rhm", "summary": "This paper proposes a training-free solution that balances overthinking and underthinking in reasoning models.\nBased on the key observation that confidence values and their variances help distinguish these two reasoning modes, the authors introduce a representation steering method that dynamically adjusts the model’s reasoning behavior.\nExperimental results demonstrate that the proposed method improves performance while reducing reasoning length.", "questions": "**[Q1] Extraction of the Steering Vector** \nAs I understand, the authors extract the steering vector using confidence-defined sets in Eq. (5). However, directly using the definition in Eq. (3) appears simpler. Could the authors report the performance gap between these two methods?\n\n**[Q2] Observation Across Models** \nWhat type of model is used in Figure 2 (b)? I am curious whether confidence values and variances serve as general indicators across different models.\n\n**[Q3] Inference Overheads** Appendix I provides only a brief latency analysis. Could the authors include a quantitative throughput evaluation (e.g., tokens per second) comparing their method with the original model?\n\n**[Q4] Number of Samplings** How many samplings are used per test sample? Since challenge datasets such as AIME and AMC contain relatively few examples (< 100), multiple samplings may be required for more reliable evaluation.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The motivation is clear, and the addressed problem is crucial in the reasoning field.\n- The paper is well-written and easy to follow.\n- The experimental results, from initial observations to ablation studies, strongly support the proposed method.\n- The method is simple, easy to implement, and can be plug-and-play across various reasoning models.", "weaknesses": "No major weaknesses are observed, but clarification on the following questions would strengthen the paper and may affect my final score.", "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": "w7ZEA7wXVC", "reviewer": "Reviewer_4rRS", "summary": "This paper proposes REBALANCE, a training-free framework that dynamically balances overthinking and underthinking in reasoning models by steering hidden representations based on confidence and variance signals. The method constructs a steering vector from a small calibration set (e.g., 500 samples) and adaptively adjusts the model’s internal states during inference. Experiments across multiple reasoning domains show improved efficiency (i.e., reducing the number of reasoning tokens) and good accuracy without additional parameter updates.", "questions": "I think it is good to add the exact throughput analysis and memory usage analysis for the efficiency claim.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "**[S1]** Using confidence to address overthinking and underthinking is interesting. Although the overall framework is complex, the method works surprisingly well in practice, which makes the idea conceptually appealing.\n\n**[S2]** The fact that the approach operates without any parameter updates and can be instantiated with only a few hundred samples (e.g., 500) is impressive. It suggests that the proposed mechanism captures a general behavioral signal of the model rather than relying on large-scale retraining.\n\n**[S3]** The method is applied not only to mathematical reasoning tasks but also to multiple domains, which demonstrates a degree of robustness and general applicability beyond a single task family.\n\n**[S4]** The paper is overall well written and clearly structured, with extensive empirical validation and thoughtful analysis. The presentation is careful, and the experiments are relatively thorough.", "weaknesses": "**[W1]** While Figure 5 presents a reasonable ablation on the number of samples used for mean and variance estimation, the transferability of these statistics remains questionable. The current analysis is limited to sampling from MATH and evaluating within the similar domain. It would be more convincing to test more rigorously (than the current analysis in Figure 5) whether the mean/variance computed from easier datasets (e.g., GSM8K) transfer to harder domains (e.g., AIME), or whether math-derived statistics generalize to different modalities such as code reasoning. Without this, the generalization claim remains somewhat weak.\n\n**[W2]** The method feels overly complex due to the number of hyperparameters involved. Decisions such as which layer to select, how strongly to apply steering, or what window size to use are not clearly motivated. It remains unclear whether there exists a single configuration that works consistently across datasets or domains, or whether each model and domain effectively requires its own tuning. This undermines the claim of universality and adds to the engineering-heavy impression.\n\n**[W3]** The paper is somewhat engineering-driven rather than conceptually driven. Most design choices (confidence thresholds, gating, control surfaces) are empirically fitted, and it is not clear why or when the method should work beyond the tested settings. Thus, the approach lacks principled understanding of why such steering in hidden space improves reasoning. The steering direction is defined by empirical prototypes, but the underlying mechanism or geometry of reasoning states remains speculative. From my experience with steering-based approaches, this line of work is still at a very early exploratory stage. The paper demonstrates that something “can” work, but not really why it works. While the results are decent, the contribution feels somewhat ad-hoc and might not generalize to models or tasks with different internal confidence landscapes.\n\n**[Overall]** I don’t find a strong reason to reject this paper. However, it also doesn’t give strong conceptual insights; it reads as an engineering effort that happens to work well rather than a principled step forward in understanding reasoning control. I would lean toward a weak accept, mostly for its empirical completeness and potential to inspire future, more principled research in steering-based reasoning.", "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)