DiffAdapt: Difficulty-Adaptive Reasoning for Token-Efficient LLM Inference
Authors: Xiang Liu
Xuming Hu
Xiaowen Chu
Eunsol Choi
Venue: ICLR 2026 Poster (2026)
Keywords: LLM reasoning
Adaptive Inference
Entropy Analysis
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recent reasoning Large Language Models (LLMs) demonstrate remarkable problem-solving abilities but often generate long thinking traces whose utility is unclear. We conduct a systematic analysis across models and datasets and discover a U-shaped entropy pattern: high entropy on simple problems despite high accuracy, low entropy on medium difficulty, and high entropy on hard problems reflecting uncertainty. The 22--25\% entropy reduction from simple to optimal regions reveals a fundamental inefficiency—an \emph{overthinking} phenomenon on easy instances. Building on these insights, we introduce \textbf{DiffAdapt}, a lightweight, deployment-ready framework that predicts problem difficulty from hidden states and selects among Easy/Normal/Hard reasoning strategies to allocate computation adaptively. DiffAdapt requires no retraining of the base LLM and is compatible with common inference optimizations. Across five models and eight benchmarks, DiffAdapt achieves comparable or improved accuracy while reducing token usage by up to 22.4\%, establishing a practical path toward compute-efficient reasoning.

Published as a conference paper at ICLR 2026
DIFPADAPT: DIFFICULTY-ADAPIVV REASONING FOR POKEN-EFFICIENS LLM INFERENCE
Xiang LIU
Xuming HU"
Xiaowen CHU
Eunsol Choi
H The Hong Kong University of Science and Technology (Guangzhou)
New York University
x1 #1iu836@connect. skust-gz.edu..
ABSTRACT
Recent reasoning Large Language Models (LLMs) demonstrate remarkable problem-solving abilities but often generate long thinking traces whose utility is unclear. Our work aims improve their efficiency, enabling them reach high performance without overthinking. First, we analyze the entropy of token probabilities in reasoning traces. Across three models, we observe consistent U- shaped entropy pattern: high entropy easy problems despite high accuracy, low entropy on problems with medium difficulty, and high entropy on hard problems reflecting uncertainty. Specifically, notice 22 22-25% entropy reduction from easy to medium difficulty regions, suggesting an overthinking phenomenon on easy instances. Building these insights, we introduce DiffAdapt, lightweight framework that selects Easy/Normal/Hard inference strategies per question based their difficulty and reasoning trace entropy. Each inference strategy consists of fixed prompt, temperature and maximum token length. In contrast to exist- ing efficiency optimization methods, our approach does not fine-tune base LLM but a small probe that classifies LLM's final hidden state, allowing inexpensive adaptation. We comprehensively evaluate our method on five models and eight benchmarks. Our method achieves comparable or improved accuracy while re- ducing token usage by up to 22.4%, establishing practical path toward compute- efficient reasoning.
INTRODUCTION
Large Language Models (LLMs) emerged as powerful tools for complex reasoning tasks, spanning
mathematical problem-solving (Lewkowycz al. 2022a Pan et al., 2024), code generation (Chen
et al. 2021), and logical deduction (Wei et al.) 2022 TANG et al. 20266] Tang et al. 20256). A key
ingredient in this success is intermediate reasoning steps, often referred to "chain of thought"
(CoT), before producing a final answer YYu et al. 2024 Li et al. 2025d Liu et al.] 2024 Kong
al. 2025). However, this capability comes at significant computational cost. Models typically generate a lengthy and claborate chain of thought for every problem, a process sometimes called test-time scaling (Muennighoff al. 2025 Chu al. 2025 Tang al. 2025a).
Always generating long traces is fundamentally inefficient. It squanders resources on simple prob- lems, while necessarily providing sufficient resources for truly complex tasks (Qu et al.) 2025
Sui et al. 2025 Li et al.) 2025b; Liu et al. 2025d с Zhang et al.) 2023 Dong et al.) 2025). Im this work, we introduce a framework to bridge this gap through systematic empirical analysis and adaptive inference strategy design. We first discover a U-shaped entropy pattern: high entropy on easy problems despite high accuracy, low entropy on problems with medium difficulty, and high entropy on hard problems reflecting uncertainty. Counterintuitively, models show high uncertainty on simple problems despite achieving high accuracy.
This motivates to design a different inference strategy per each of this three distinct difficulty gions. We develop three simple inference strategies, each equipped with different generation length (i.e.. max token length), prompt and decoding hyperparameters (c.g., sampling temperature). The prompt designed for "easy" questions encourages models to answer succinetly without overthinking, while the prompt for high difficulty questions instructs model to think carefully. We first conduct

---

Published as a conference paper at ICLR 2026
oracle experiments with these three templates. When allowed to choose optimal strategy per question, models achieve 50% token savings while improving the model accuracy by over 10%.
Based on this observation, introduce DiffAddat. three-stage framework that dynamically leets inference strategy rather than applying uniform reasoning budgets to all problems. Our frame- proxy model to generate training data by sampling responses and heuristically labeling them with difficulty-basee strategy assignments; (2) we train a lightweight probe on the model's hidden states to predict problem difficulty; and (3) during infer- ence, the probe dynamically selects the appropriate reasoning strategy TTasy/Normal/Hatte for each question. Compared the training-free baseline DEER (Yang et al., 2025b), DiffAdapt achieves superior performance with up to 62% token reduction and 18% performance improvement across eight mathematical reasoning benchmarks [ightman et al. (2023); Rein al. (2024); Wang et al.
work operates in three stages: (1) we
2024); He et al. (2024); Cobbe et al. (2021) over five models DeepSeek-Al et al. (2025 Yang et al. 2025a)
Liu al. (025a) Hou et al. (2025).
2 RELATED WORK
Training-Base Based Budget Control. Several methods incorporate budget control directly into the model's 's training phase. Huang et al. (2025); Shen et al. (2025); Liu al. (2025b) reinforcement learning (RL) with difficult-aware rewards to train model for adaptive budgeting. Similarly, Cheng al. (2025) employ dual-reward system based on Group-Policy Optimization (GRPO) to encourage conciseness. ThinkPrune (Hou et al. 2025) trains long-thinking LLMSs via RI. with token limits, using iterative pruning rounds achieve better performance-lengle tradeoffs. LCC-R Cheng et al. 2025) addresses "invalid thinking" through GRPO-based post-training with length and compress rewards, achieving significant sequence length reduction while maintaining performance. TL.DR Li et al., 2025c) is training-fres method that uses mixed systeml I and system2 data to control the reasoning process. AdaCoT (Lou al.) 2025) framed adaptive reasoning as Pareto optimization problem that seeks to balance model performance with the costs associated with CoT invocation. Thinkless (Fang et al. (025) is trained under a reinforcement learning paradigm and employs two control tokens, <short> for concise responses and <think> for detailed reasoning.
Inference- Timee Budget Control. Other methods operate purely at inference time without requiring training. Zhang al. (2025a) define "a moment" to switch from slow to fast thinking, while Zhang et al. (2025c) modify the sampling strategy to explore a continuous concept space. Yang et al. (2025b) monitor for specific transition tokens and model confidence to perform an early exit. Ma et al. (2025) propose a method to disable the reasoning process of LLMs. These training methods are flexible but often underperform leamed difficulty model.
Methods Requiring Auxiliary Models. Some approaches rely on external models to guide the
LLM's reasoning process. For instance, [i et al. (2025a) train separate BERT model to predict the remaining reasoning length and steer the generation process. ZZhang et al. (2025b) employ RI- 7B switcher model, using prompt engineering or supervised fine-tuning for strategy selection. Liang et al.) (2025) utilize an MLP-based switcher with group accuracy as training labels. While these methods can achieve good performance, they introduce the overhead of running additional non-trivial models during inference, increasing computational costs and deployment complexity. Our Diff framework, in contrast, very small classifier integrated directly with the I.LM's internal states, adding minimal latency.
CHARACTERIZING OVERTHINKING PHENOMENON
Recent work has identified that reasoning LLMs exhibit "overthinking" behavior (Ma et al. 2025 Sui et al.) 2025) Qu et al. 2025). ) where models generate exceedingly lengthy solution when they arrive correct solutions much more succinetly. Building upon this observation, analyze this phenomenon from an entropy perspective, revealing a counterintuitive pattern where models demonstrate high uncertainty.
2

---

Published as a conference paper at ICLR 2026
Revealing Ovarthinking: Why Simple Problems Show High Reasoning Uncertainty
Overthiiing Region)
Carta Segion
Cauaiiityy lime)
Uncertaltty
Shagpd
Menua Gentissting Pitters
6/12
Corretteess
(b) Nemotron-1.5B
64
62
Cenerator Drinay
60
Problom
(a) PeepSeek-R1-Qwen--2
(c) SaapSeeek-R1Qwen---1
Figure 1: Visualization of model accuracy (blue bar), generation entropy (red line) difficulty of question (X-axis), across three models (model name at the bottom of the graph). We observe consistent U-shaped entropy curve along the difficulty levels on multiple models.
3.1 EXPERIMENTAL SETTING
Dataset We use DeepMath- 103K dataset (He al., 2025). a large-scale, challenging, decontam- inated, and verifiable mathematical dataset designed for advancing reasoning capabilities. The dataset provides problems with difficulty ratings from 1-10 as evaluated by GPT-4o based on math- ematical complexity and covers wide range of mathematical topics with rigorous decontamination against numerous benchmarks. For our experiments, we created a balanced experimental set by sampling 300 questions per difficulty level, with 10 sampling iterations per problem at temperature 0.6, ensuring robust statistical analysis of entropy patterns.
Entropy Calculation We measure model uncertainty using generation entropy, calculated as the average entropy across all tokens in the generated sequence (Wang et al. (025). For each token position t, the entropy is computed as H; and pr.j is the probability of predicting the j-th token in the vocabulary given all preceding tokens Oct- The entropy H, corresponds to the uncertainty of the token generation distribution at position f, rather than an intrinsic property of the specific token oN sampled from that distribution.
Σ Pt.j log Pt.j. where V is the vocabulary size
Correctness Rate Evaluation For each difficulty rating bucket, we measure the correctness rate as the fraction of successful solutions across multiple sampling iterations. For each problem we generate responses (ri,); and evaluate their correctness against ground truth solutions. The correctness rate for problem z; is defined as:
C(x,)=
Σ Uris vil
where I[-] is the indicator function, Y4 denotes the ground truth solution for problem r, and 10 sampling iterations per problem our experiments.
3.2 RESULTS
Figure I (a) illustrates this U-shaped entropy curve across different difficulty levels using the DeepSeek-RRA RI-Distill-Qwen model on the DeepMath- 103K dataset (He et al. 2025). We observe consistent overthinking patterns across multiple model architectures (see Appendix for additional models).
The U-shaped entropy curve in Figure (a) reveals three distinct regions, where each region might benefit from different computational strategies:
Overthinking Region (Easy Problems): High correctness rates coupled with high entropy, in- dicating the model is uncertain about problems it can solve well. This counterintuitive pattern

---

Published as a conference paper at ICLR 2026
Table 1: Our three inference strategy configurations. |ax] refers the predefined maximum token budget for generation. Full prompts and detailed configurations can be found in the Appendix C
Strategy
Temperature
Max Tokens
Simplified Prompt Template
Easy
0.5
0.4
Max
Direct solving with verification
Normal
0.8
1.0
Max
Step-by-step methodical approach
Hard
0.4
0.5
Max
Resource-aware strategic reasoning
Performance vs. Token Consumption for Different Strategy Selections
GSMBK
MATH
AIME24
AIME25
102.
*
Performance (15
63-
64-
52-
40
87.5
42
Alliiormal
32-
All Hard
82.5
81.0
23-
200
OlympladBanch
GPQA
Minerva
MMLU-Pro
69
Perfomance (%6
*
70.
83-
60-
55
63
50
1.5K
1:3K
1.3K
2/16
2,16 Average Tokens Problem
1.2% 1.4K
1.3K
1
Average Tokens Problem
Average Tokens per Problem
Average Tokens per Problem
Plots summarizing task performance (y-axis) and efficiency (x-axis) on Qwen3-4B model with various inference strategies. In all datasets, we find oracle strategy (i.e., the strategy that achieves the correct answer while incurring minimal tokens the strategy incurring minimal to- kens if no strategy leads to correct answer) significantly outperforms any uniform setting.
Table 2:
suggests unnecessary computational overhead and motivates a Easy reasoning strulegy, which allocates minimal computational resources.
Certainty Region (Normal Problems): Low entropy with optimal performance, representing the sweet spot where the model's uncertainty aligns with task complexity. These problems benefit from Normal reasoning strategy, which allocates standard computational resources.
Capability Limit Region (Hard Problems): High entropy with low accuracy, indicating genuine difficulty where the model struggles. These problems may benefit from Hard reasoning strategy, which allocates less computational resourees and prevents getting stuck in reasoning.
This analysis naturally leads to three-tier adaptive strategy: Easy, Normal, and Hard reasoning modes, each tailored to the computational needs revealed by the overthinking phenomenon. We develop such three inference strategies and present oracle experiments with them in the next section.
ORACLE EXPERIMENTS WITH THREE INFERENCE STRATEGIES
Inference Strategy Set We design the inference strategies to address the challenges revealed by our overthinking analysis. For Easy problems, we use lower temperature and reduced tokens to prevent the model from exploring unnecessary solution paths. Normal problems receive standard temperature with full token budget to enable comprehensive reasoning in the optimal region where the model's ss uncertainty appropriately matches task complexity. For Hard problems, we implement a "Fail Fast" mechanism with strict token limits. Since our analysis indicates that these problems typically exceed the model's capabilities regardless of generation length, this strategy prioritizes cutting computational losses on likely-to-fal queries to reallocate resources, rather than engaging in potentially unproductive reasoning. Table Tubble[summe summarizes the specific configurations for each reason- ing strategy. Notably, these hyperparameters were empirically determined through a systematic grid search optimization rather than selected heuristically. Further details provided in Appendix C
4

---

Published as a conference paper at ICLR 2026
(1 Data Generation
Unlabeled Dataset
Entropy Correct Rate
Heuristics- Label Assignment
Q1:Caiculate line intagral
Q1:Colculate the line integral... ...
65%
>
C
0
Qrc Using only 3 paice, paise...
Q2: Liiing only 3 paise. paise
0.1
69%
(ii) Probe Training
(il) Infercnce with Probe
QT:Colculate integral... .oe
Calculiaa ino Integ ak.
Hidden
Hidden State
Pronpp Tempessturd
Final
Streney Lebel
Probe
Adaptive Strategy Execution
Figure 2: Overview of the DiffAdapt framework. Top: (i) Data Generation: We sample multiple responses from the proxy model, and compute statistics to heurstically assign inference strategy. This process yields training dataset for the probe; (ii) Probe Training: We train lightweight probe which takes model's 's hidden states after processing the query and predict its inference strategy label; and (iii) Inference with Probe: where the trained probe dynamically selects appropriate inference strategies (Fasy/Norma//Hard).
Experimental Setting To establish the theoretical upper bound of our approach, we conduct an oracle experiment using Qwen3-4B across eight reasoning benchmarks. We evaluate on five math- ematical reasoning tasks: GSM8K (Cobbe al. 202I), MATH500 (Lightman et al.) 2023). AIME 2024 & 2025, and OlympiadBench (He et al. 2024), along with three out-of-domain benchmarks: Minerva (Lewkowyez al. 20226), GPQA (Rein et al. 2024), and MMLU-Pro (Wang al. 2024). For each input, we generates outputs from each of the three inference strategies, Easy. Normal, and Hard. The Oracle strategy is determined through two-step process: first, we identify all strategies that yield correct answer, and from this set, we then select the one with the minimal token con- sumption. The parameter configurations are set as the same as the fixed strategies in Table T and the max token limit is 32K. More visualization details can be found in Appendix G
Results Figure showw that the oracle consistently dominates fixed strategies across all benchmarks, yielding 7.2% average accuracy gain over the best fixed baseline. Token allocation adapts with problem difficulty, ranging from 198 tokens on GSM8K to 4.675 on AIME25, highlighting the need for adaptive compute budgeting. These results substantiate the overthinking hypothesis and quantify the benefits of difficulty-aware reasoning in both accuracy and efficiency. The oracle's Pareto-optimal frontier across benchmarks sets actionable performance targets for DiffAdapt.
THE DIFFADAPT FRAMEWORK
To enable I.LMs to adapt their inference process hased on question difficulty, we introduce three- stage framework that leverages the model's internal representations to adapt inference to question difficulty. Figure preparation to deployment.
2 illustrates our overall approach, consisting three sequential stages from data
5.1 STAGE DATA GENERATION WITH PROXY MODEL SAMPLING
The first stage generates training data for the difficulty classifier through self-supervised approach. Starting from an unlabeled dataset, we use a proxy Moddel-typically the same LLM to sample responses and compute its entropy and correctness.
For each problem I, we prompt the model to generate complete reasoning steps and final results with maximum length of 32K tokens. We then compute the model's uncertainty using the same genera-
5

---

Published as a conference paper at ICLR 2026
tion entropy calculation described in Section 3 We perform this process with 10 sampling iterations per problem to ensure robust entropy estimates. This generation entropy serves as proxy for task complexity. Grounded in the U-shaped entropy pattern in Section and the Pareto-optimal Oracle analysis (Appendix D.3). we adopt the following heuristic labeling rule: Normal if correctness 2 a 7; Easy otherwise (typically the low-difficulty anomaly with moderate correctness but unexpectedly high uncertainty). The thresholds a, 3, 7 are set per model using a simple heuristic informed by the observed entropy-correetness distributions; we op- tionally perform a light sanity check on small validation split to ensure label stability. Per-model values are reported in Appendix
and entropy [3; Hard if correctness
5.2 STAGE PROBE TRAINING ON HIDDEN STATES
The second stage trains a lightweight probe using the generated labeled data. As shown in the bottom-left of Figure we extract hidden state representations h2 from the last layer after prefilling the question.
small probe C parameterized by 0 learns to predict difficulty levels from these hidden states. The probe is implemented simple multi-layer perceptron (MLP):
softmax ReLU(W1h2 b1) b2)
where 0
{W1, W2, 61, b2) are the learnable parameters, and d represents the predicted difficulty distribution over the three classes (Easy/Normal/Harl),
The classifier parameters optimized by minimizing cross-entropy loss:
N
((0)
1
Σ
log P(d, = y:
N
We keep the base LLM weights frozen, requiring only the training of a small probe network.
5.3 STAGE INFERENCE WITH ADAPTIVE STRATEGY EXECUTION
During the inference, the trained probe dynamically selects reasoning strategy based pre- dicted difficulty, as shown in the bottom-right of Figure 2 The process consists of: Difficulty Prediction: Extract hidden states after prefilling the question and predict difficulty using the trained probe. Strategy Selection: Map predicted difficulty to corresponding reasoning strategy (Easy/Normal/Hard), Adaptive Execution: Apply the selected strategy with appropriate computa- tional budget allocation.
This difficulty prediction step does not interfere with the model's prefilling and decoding processes, making it compatible with most inference optimization techniques including batching, KV cache, prefix cache, and others (Kwon al. 2023 Zheng al. 2024).
EXPERIMENTAL RESULTS
6.1 EXPERIMENTAL SETUP
Models. We evaluate our framework on five models: three reasoning LLMSs (Qwen3-4B (Yang et al. 2025a). DeepSeck-RI1- R1-Qwen--B (DeepSeek-Al et al. 2025), DeepSeek-RR-LLama--8t (DeepSeek-
AT et al. 2025)) and three models trained with length control RL training (Nemotron-1.5B (Liu al. 2025a ThinkPrune-7BB (Hou et al. 2025), The probe uses a simple MLP structure, trained For 100 epochs using the Adam W optimizer with learning rate le-3.
Training Datasets. The probe training dataset consists of subset of the DeepMath-103K dataset, with 300 problems sampled per difficulty level. Data labeling is performed through Stage I of our DiffAdapt framework to generate difficulty-based strategy assignment.
Baselines. We compare against fixed-strategy baselines that apply exclusively Easy, Normal, or Hard all problems, as well as the dynamic early-exit method DEER (Yang et al. 2025b). For
http:://ttgginngaccccoooiivaaaae Rww.eeeearccrcccsnnnnn-OOOO DeeassoinngQwwen-1..5

---

100 Published as a conference paper at ICLR 2026
Qwen3-4B
DeepSeek-Ri Ri-eee----
DeepSeek-RR-
Detidept
Nemmal
Alld
Augg3 Parformance
DEER
40
25-
Ldd 1544
30
Percontage of Maximum Token Limit (%)
Percentage of Maximum Token Limit (%)
Parcentage of Maximum Taken Limit (86
(a) In-domain performance across reasoning LLMs model architectures. Diff'Adapt consistently outperforms fixed strategies across computational budgets.
Qwen3-48
DeepSeek BrmpSeek-R1-Oae R1-Qwen--7
DeepSeek BeapSeek-R1-Lameee R1-Llama--8
50
Difesast
All Easy
All horma All Hard
Nuge inrfrmma (%6)
DEER
14
15-
20 93
1.30
20
90
60
20
60
110
Percentage of Maximum Token Limit (%)
Percentage of Maximum Token Limit (%)
Percentage of Maximum Token Limit (%S)
(b) Out-of-domall performance on Minerva, MMLU-Pro, and GPQA. DiffAdapt maintains effectiveness under domain shift for reasoning L.MSs.
Figure 3: Performance across reasoning LLMs model architectures and domains. The x-axis represents different maximum token limit constraints as a percentage of the full token budget, demonstrating how different strategies perform under varying computational budgets. (a) In-domain: DiffAdapt consistently outperforms fixed strategies. (b) Out-of-domain: Effectiveness maintained under domain shift.
DEER, we align its maximum generation length with other methods and use the default think thresh- old of 0.9. This setup highlights the benefit of the proposed DiffAdapt framework over both static and training- free dynamic approaches.
Evaluation Benchmarks. We evaluate five mathematical reasoning tasks: GSM8K (Cobbe ctt al.
2021). MATH500 (Lightman et al. 2023). AIME 2024 & 2025, and OlympiadBench (He
2024). along with three out-of-domain benchmarks: Minerva (Lewkowycz et al. 2022b), GPQA (Rein et al. 2024), and MMLU-Pro (Wang et al. 2024).
al.
Experimental Design. To demonstrate the probe's ability to perform dffficultt-adaptive reason- ing, we mix benchmarks different difficulty levels. We present averaged results across bench- marks with varying domains, in-domain (GSM8K, MATH500, AIME24&255. OlympiadBench) and out-of-domain (Minerva, GPQA, MMLU-Pro) to show adaptive performance across the difficulty spectrum. Each experiment is run three times: we report the mean across runs. For evaluation, we follow reliability protocols from Ye et al. 2025 2 Chen et al. (2025 Max token limits: for each pair, we first allow generous cap (e.g., 32K) to observe the model's longest output and then set nearby rounded value as the per-benchmark Appendix Table for the finalized values and Appendix
max_tokens used in plots; see Appendix[D.6ffol D for details.
6.2 REASONING LLMS RESULTS
To validate the generalizability of our DiffAdapt framework, conducted comprehensive periments across different reasoning LLMs model architectures and scales. Figure presents the
bhtts:/gittbb.com///AAAAA

---

Published as a conference paper at ICLR 2026
Nemotron-1.58 (ID)
ThinkPrune-78 (ID)
Nemotron-1.58 (OOD)
ThinkPrune-78 (OOD)
Avges Performance (50)
Eary
Mermal
93
25
40
25 06 30 Percentigge Token (S)
20
43 56 60 Parcentage Mao. Limit
56 100
83 15 30 90 750 196)
20 Percentige Mox.
00 50. 239 65
Porcentage
Figure 4: DiffAdapt orthogonality with Length Control RL methods. Performance analysis across three LC-RL trained models on both ID and OOD datasets.
performance-efficienncy trade-offs for three representative models both in-domain (ID) and out- of-domain (OOD) evaluation datasets.
Performance Analysis DiffAdapt consistently outperforms fixed strategies across all model archi- tectures and domains. On in-domain tasks (Figure 3a), Qwen3-4B shows the largest improvements, particularly at higher token budgets. DeepSeek-RR -iieek-RR--weeee Ri-Qwen-7B demonstrates stable gains, while DeepSeck-RI-Llamae
exhibits significant benefits across model families. On out-of-domain eval- uation (Figure 3b). the framework remains effective: DiffAdapt delivers consistent improvements across models and domains, with gains becoming more pronounced as the maximum token bud- get increases. By contrast, the training-free dynamic baseline DEER performs comparably to the strongest fixed strategy in-domain datasets but still lags behind Diff'dapt; under distribution shift (Minerva, MMLU-Pro, GPQA), DEER exhibits limited generalization and larger performance degradation. These results confirm DiffAdapt's 's robustness across different architectures, scales, and domains.
Key Findings The results reveal distinet performance patterns for different strategies. Easy strate- gies achieve high performance with minimal tokens but plateau quickly. Normal and Hard strate- gies improve continuously with increased computational budgets. This validates averaging across benchmarks of varying difficulty, as it captures the distinct computational requirements of different problem complexities. DiffAdapt exploits these patterns through adaptive strategy selection, con- sistently outperforming fixed approaches across all token limits. Compared with DEER, DiffAdapt delivers larger and more stable gains across token budgets and domains, indicating that difficulty- aware strategy selection generalizes better than confidence- inter-strategy performance differences show greater DiffAdapt improvements, confirming that adaptive selection benefits from strategy diversity.
early exit. Models with larger
6.3 ORTHOGONALITY WITH LENGTH CONTROL RL METHODS
We evaluate DiffAdapt models trained with Length Control RL (LC-RL) to demonstrate orthog- onality with existing training- based approaches. Figure shows results across two LC-RL models: Nemotron-1.5B. ThinkPrune-7B on both in-domain and out-of-domain datasets.
Key Findings. Observing the Easy, Normal, and Hard strategy curves reveals that Easy strategies achieve superior performance in most settings, LC-RI. training adapts these models to solve problems efficiently with low computational cost. DiffAdapt shows slightly lower performance than Easy strategies under low token limits but achieves state-of-ther art results under high token budgets. This aligns with our design philosophy, enabling LLMS to maintain high performance and efficiency across problems of varying difficulty and different token budgets.
This analysis establishes that DiffAdapt can be effectively combined with existing training- based op- timization methods, offering readily integrable solution that enhances reasoning efficiency without requiring modifications to the underlying training paradigm.
6.4 COMPUTATIONAL EFFICIENCY AOTICAAAAAII ANALYSIS
We quantify computational efficiency in terms of tokens consumed and latency. For token consump- tion, measure the average relative token reduction of DiffAdapt compared to fixed Normal
8

---

Published as a conference paper at ICLR 2026
Table 3: Comparing token savings (%) of our method and baseline (DEER). Negative means more tokens than the baseline.
Table 4: End-to-end inference time comparison. All methods use vLLM backend.
Model
DiffAdapt
DEER
Method
Time (minutes)
DS-RI DS-RI-Qwen-7B
9.7%
-53.3%
vLLM Baseline
64
Qwen3-4B
22.4 22.4%
-27.5%
+ DEER
57
ThinkPrune-7B
10.1%
+ DiffAddpt
10
strategy across eight benchmarks B:
1 B
Tokens
To MMtthod
Token Savings
X 100%.
TokenSi, Nommal
BE
A higher value for Token Savings indicates greater efficiency, meaning more tokens are saved com- pared the Normal strategy. Conversely, negative value signifies that the method consumed more tokens than the baseline.
Token cost From Table we observe substantial efficiency gains from DiffAdapt across models: Qwen3-4B achieves 22.4% token savings, while DS-RI JS-RR-Qwen-7BB and ThinkPrune-7B reduce usage by around 10%. In contrast, DEER increases token usage relative to Medium (e.g.. -27.5% on Qwen3-4B and - 53.3% on DS-R1-Qwen-775 The reason is mechanistic: DEER operates under a fixed token budget and chooses continuation length primarily by confidence/probability which frequently drives generations to the maximum token cap rather than allocating tokens adaptively by problem difficulty.
Latency As shown in Table Dapf reduces end-to-end wall-clock time by 6x vLLM base- line and 5x vs DEER (both using vLLM backend) under identical settings (Qwen3-4B; first 40 OlympiadBench problems; batch size 10; single A800 GPU; max token limit 32K; temperature 0.6; DEER think threshold 0.9), corroborating that token savings translate into practical runtime speedups.
These efficiency gains translate directly into lower inference cost at comparable accuracy levels.
7 ABLATION STUDIES AND ROBUSTNESS ANALYSIS
To rigorously validate the design choices and robustness of DiffAdapt, we conducted extensive ablation studies regarding hyperparameter sensitivity, probe architecture, and reasoning integrity.
7.1 ROBUSTNESS AND DESIGN CHOICES
whether the method requires man- ual tuning per model: (2) Probe Architecture: whether a non-linear MLP is necessary; and (3) Data Efficiency: performance impact of reducing training data. Our evaluation protocol aligns with Section [ utilizing in-domain benchmarks including GSM8K, MATH500, AIME 24&25, and OlympiadBench.
We investigate three key dimensions: (1) Threshold Sensitivity:
Sensitivity to Thresholds (a,3377 7) A key concern for deployment is whether the difficulty thresh- olds require fine-grained calibration. To test this, we replaced the optimized thresholds of Qwen3-4B with a configuration directly transferred from the DeepSeek-RI model family (a = 0.85, 8 = 0.35, 0.60). As shown in Table 5 ("Transferred Config"), ) the performance difference is neg- ligible (avg. difference 0.3%). This confirms that DiffAdapt is highly robust to hyperparameter variations, enabling "plug-and-plaa Probe Architecture and Data Scale Table 5 also highlights the impact of probe design. Replacing our 2-layer MLP with a simple Linear Head Teads to a consistent accuracy drop (>...2%), justifying the need for lightweight non-linear classifier. Conversely, reducing the training data to only 30% results in minimal degradation, demonstrating that DiffAdapt is extremely data- tata-efficient compared to Ra-ba methods that typically require large-scale rollout data.
deployment without per-model re-calibration.

---

Published as a conference paper at ICLR 2026
Table 5: Ablation study on Qwen3-4B, We compare the Default DiffAddat configuration against: (a) Transferred Thresholds from DeepSeek-RI (to test robustness), (b) Linear Probe (to test architecture necessity), and (c) 30% Training Data (to test data efficiency).
Baseline
Robustness Check
Probe Design Ablation
Token Budget
Diff'dapt (Default)
Transferred (DeepSeek-R))
Linear Head
30% % Data
33.3%
59.3
59.9
56.4
64.5
50.0%
67.2
66.8
64.1
67.9
66.7%
72.4
72.7
68.7
69.3
83.3%
75.8
76.0
72.2
69.8
100%
76.8
76.6
74.0
70.1
Average
70.9
71.2
67.7
68.5
Table Pairwise comparison of reasoning qual- ity (N=50). A blind Judge (Qwen3-30B) com- pared DiffAdapt vs. Baseline.
Table 7: Failure analysis of the cases where Baseline won. "Truncation Error" indicates ac- tual logic failure.
Outcome
Count
Percentage
Reason for Loss
Frequency
DiffAdapt Wins
38
76%
Subjective Preference
10% (5/50)
Baseline Wins
6
12%
Truncation Error
2% (1/50)
Tie
12%
7.2 REASONING INTEGRITY ANALYSIS
To address the concern that aggressive token reduction might compromise the logical completeness of reasoning chains (e.g., causing early truncation), we conducted blind, pairwise L.L-- Judge study, more details can be found in Appendix
We sampled 50 queries from GSM8K and used Dwen3-330--A- as an impartial judge to evaluate anonymized outputs from Diff'dapt and the Baseline (Normal strategy). The judge was explicitly instructed to penalize logical gaps. As shown in T DiffAdapt was preferred in 76% of cases, with the judge often citing that DiffAdapt produced "more concise and direct" reasoning without redundancy. Notably, catastrophic failure due to early truncation occurred in only 2% of cases (Table 7 refuting the concern that efficiency comes the cost of reasoning integrity.
CONCLUSION
We characterize a overthinking phenomenon in LLMs, U-shaped entropy patterns across multiple architectures. This counterintuitive finding challenges the assumption that more computation always improves reasoning.
Our study offers three takeaways: (1) empirical characterization of overthinking, with consis- tent 22-25% entropy reduction from simple to optimal regions that reveals systematic inefficiency; (2) Oracle analysis suggesting large potential for difficulty-wware inference strategy selection; and (3) DiffAdapt, lightweight framework that predicts difficulty from hidden states and se- lects Fasy/Normal/Hard strategies, matching or improving accuracy while reducing tokens by up to 22.4% across five models and eight benchmarks.
DiffAdapt requires no LLM retraining, is compatible with common inference optimizations (e.g., batching, KV/prefix caching), and is orthogonal to length-control RL methods. We presents a sim- ple, lightweight solution to allow adaptive computation allocation for LLM reasoning.
REPRODUCIBILITY STATEMENT
We aim for full reproducibility. Upon publication, we will release code, prompts, evaluation scripts, and configuration files to reproduce all tables and figures. We specify all random seeds, sampling settings (lemperaturc//opppnnnml p/uubberreeeeeeeeeeeeee of samples =10)), and the base token limit We will provide instructions for dataset preparation (e.g., Deep 103K splits and filtering). model versions used (Qwen3-4B, DeepSeek-RP R1-QWen-7B, DeepSeek-R SeepSeee--R-LLlmma---5. 1-L-lam--88, Nemotron-1.55 1..5B, ThinkPrune-779
10

---

Published as a conference paper at ICLR 2026
and hardware/software environments needed to replicate results. All plots in the paper generated by released scripts.
ETHICS STATEMENT
This work studies compute-efficient reasoning strategies for LLMs on public, decontaminated rea- soning datasets. No personally identifiable sensitive data used. We discuss potential risks of misinterpretation and over-reliance on automatic reasoning systems and recommend careful hu- man oversight in high-stakes scenarios. We will document evaluation limitations and known failure modes, and we avoid claims beyond the evaluated settings.
ACKNOWLEDGEMENTS
We thank the members of NYU ML for their helpful feedback. This work was supported in part through the NYU IT High Performance Computing resources, services, and staff expertise. The work is partially funded by NSF CAREER award 2443271 I and NSF award RI-2521091; the National Natural Science Foundation of China (Grant No. 62506318 and 62272122); the Guangdong Provin- cial Department of Education Project (Grant No. 2024K (NC0028;; the Scientific Research Projects for the Higher-eduuational Institutions (Grant No. 2024312096) and the Guangzhou- Boangzzou-HKKSTCOG HKUST(GZ)) Joint Funding Program (Grant No. 2025A03/3957) from the Education Bureau of Guangzhou Mu- nicipality; and Hong Kong CRF grants under Grant No. C7004-22G and C6015-23G.
REFERENCES
Ding Chen, Qingchen Yu, Pengyuan Wang, Wentao Zhang, Bo Tang, Feiyu Xiong, Xinchi Li, Minchuan Yang, and Zhiyu Li. xverify: Efficient answer verifier for reasoning model evalua- tions. arXiv preprint arXiv:2504.1042 2025.
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.033: 2021.
Zhengxiang Cheng, Dongping Chen, Mingyang Fu, and Tianyi Zhou. Optimizing length compres- sion in large reasoning models, 2025. URI. https: / raxii www.wwwwwww...a.......... abs 2506. 14755
Yuanlin Chu, Bo Wang, Xiang Liu, Hong Chen, Aiwei Liu, and Xuming Hu. Ssr: Speculative parallel scaling reasoning test-time. arXiv preprint arXiv:2505. 15340, 2025.
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reichhro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems, 2021. URL https://arxiv.
orq/ /trg/ab/// 2110.1111. 14168
abs/
DeepSeck-AL, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang Xingkai Yu, Yu Wu, F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai. Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Honghui Ding, Huajian Xin, Huazuo Gao. Hui Qu, Hui Li, Jianzhong Guo, Jiashi Li, Jiawei Wang, Jingchang Chen. Jingyang Yuan, Junjie Qiu, Junlong Li, J. L. Cai, Jiaqi Ni, Jian Liang, Jin Chen, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang. Lecong Zhang, Liang Zhao, Litong Wang. Liyue Zhang, Lei Xu, Leyi Xia, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Meng Li, Miaojun Wang, Mingming Li, Ning Tian, Panpan Huang, Peng Zhang, Qiancheng Wang, Qinyu Chen, Qiushi Du, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, R. Chen, R. L. Jin, Ruyi Chen, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shengfeng Ye, Shiyu Wang, Shuiping Yu, Shunfeng Zhou, Shuting Pan, S. S. Li, Shuang Zhou, Shaoqing Wu, Shengfeng Ye, Tao Yun, Tian Pei. Tianyu Sun, T. Wang, Wangding Zeng, Wanjia Zhao, Wen Liu, Wenfeng Liang, Wenjun Gao, Wenqin Yu, Wentao Zhang, W. L. Xiao, Wei An, Xiaodong

---

Published as a conference paper at ICLR 2026
Liu, Xiaohan Wang, Xiaokang Chen, Xiaotao Nie, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xinyu Yang, Xinyuan Li, Xuecheng Su, Xuheng Lin, X. Q. Li, Xiangyue Jin, Xiaojin Shen, Xi- aosha Chen, Xiaowen Sun, Xiaoxiang Wang, Xinnan Song, Xinyi Zhou, Xianzu Wang, Xinxia Shan, Y. K. Li, Y. Q. Wang. Y. X. Wei, Yang Zhang, Yanhong Xu, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yi Yu, Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Yishi Piao, Yisong Wang, Yixuan Tan, Yiyang Ma, Yiyuan Liu, Yongqiang Guo, Yuan Ou, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yunfan Xiong, Yuxiang Luo. Yuxiang You, Yuxuan Liu, Yuyang Zhou, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yaohui Li, Yi Zheng, Yuchen Zhu, Yunxian Ma, Ying Tang. Yukun Zha, Yuting Yan, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhicheng Ma, Zhigang Yan, Zhiyu Wu, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Zizheng Pan, Zhen Huang, Zhipeng Xu, Zhongyu Zhang, and Zhen Zhang. Deepseek-rl: Incentivizing reasoning capability in Ilms via reinforce- ment learning, 2025. URL Btthttp:: /ttttps:://rxiii arxiv. org/ abs/2501..... 12948
Peijie Dong, Zhenheng Tang, Xiang Liu, Lujun Li, Xiaowen Chu, and Bo Li. Can compressed LLMs truly act? an empirical evaluation of agentic capabilities in LLM compression. In Forty- second International Conference Machine Learning, 2025. hRthttss: http:: //tttree///ppereveeee. net//orum? id= rKWXYSDKsc
Gongfan Fang, Xinyin Ma, and Xinchao Wang. Thinkless: Llm learns when to think. arXiv preprint 1.X1::5505.1339. 2025.
Chaoqun He, Renjie Luo. Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, and Maosong Sun. Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual mul- timodal scientific problems, 2024. URL
org/ abs 24000 14008
Zhiwei He, Tian Liang, Jiahao Xu, Qiuzhi Liu, Xingyu Chen, Yue Wang, Linfeng Song, Dian Yu, Zhenwen Liang, Wenxuan Wang, et al. Deepmath-103k, 1033: A large-scale, challenging, de- contaminated, and verifiable mathematical dataset for advancing reasoning. arXiv preprint arXiv:2504. 11456, 2025.
Bairu Hou, Yang Zhang, Jiabao Ji, Yujian Liu, Kaizhi Qian, Jacob Andreas, and Shiyu Chang- Thinkprune: Pruning long chain-of- thought of ilms via reinforcement learning. arXiv preprint arXiv:2504.01290 2025.
Shijue Huang. Hongru Wang. Wanjun Zhong, Zhaochen Su, Jiazhan Feng, Bowen Cao, and Yi R. Fung. Adactrl: Towards adaptive and controllable reasoning via difficulty-wwarr budgeting, 2025.
URL pesttss://axivee org/ abs /ta.b///5000 2505. 18822
Zhenglun Kong, Yize Li, Fanhu Zeng, Lei Xin, Shvat Messica, Xue Lin, Pu Zhao Manolis Kel- lis, Hao Tang, and Marinka Zitnik. Token reduction should go beyond efficiency in generative models-from vision, language to multimodality. arXiv preprint arXiv:2505. 18227, 2025.
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu. Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023.
Aitor Lewkowyez, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ra- masesh, Ambrase Slone, Cem Anil, Imanol Schlag, Theo Gutman- el. Solving quantitative reasoning problems with language models. Advances in neural information processing systems, 35:3843-3857, 2022a.
Aitor Lewkowyez, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ra- masesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman et al. Solving quantitative reasoning problems with language models. Advances in neural information processing systems, 35:3843-3857, 2022b.
Junyan Li, Wenshuo Zhao, Yang Zhang, and Chuang Gan. Steering Ilm thinking with budget guid- ance, 2025a. URL httpthttt///t ptttppa:/arriiin http://wxxwv.oo////bb/s////////s httpgg/bb///0000 13752
12

---

Published as a conference paper at ICLR 2026
Qi Li, Junpan Wu, Xiang Liu, Yuxin Wang, Zeyu Li, Zhenheng Tang, Yuhan Chen, Shaohuai Shi, and Xiaowen Chu. Reasoning language model inference serving unveiled: An empirical study. arXiv preprint arXiv:2510.1866 2025b.
Zhong-Zhi Li, Xiao Liang, Zihao Tang, Lei Ji, Peijie Wang, Haotian Xu, Haizhen Huang, Weiwei Deng, Ying Nian Wu, Yeyun Gong, et al. TI; dr: Too long, do re-weighting for effcient Ilm reasoning compression. arXiv preprint arXiv:2506.0m 02678, 2025c.
Zhong-Zh Li, Duzhen Zhang, Ming-Liang Zhang, Jiaxin Zhang. Zengyan Liu, Yuxuan Yao, Haotian Xu, Junhao Zheng, Pei-Jie Wang, Xiuyi Chen, et al. From system system 2: A survey of reasoning large language models. arXiv preprint arXiv:2502. 17419, 2025d.
Guosheng Liang, Longguang Zhong, Ziyi Yang, and Xiaojun Quan. Thinkswitcher: When think hard, when to think fast. arXiv preprint arXiv:2505.14/22 2025.
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, llya Sutskever, and Karl Cobbe. Let's verify step by step, 2023. URL
https: //axxvv// org/abs 2305 20050
Mingjie Liu, Shizhe Diao, Ximing Lu, Jian Hu, Xin Dong, Yejin Choi, Jan Kautz, and Yi Dong. Prorl: Prolonged reinforcement learning expands reasoning boundaries in large language models,
2025a. httthtts:: //arii...iivvviaa org/ abs/2505 24864
Shih-Yang Liu, Xin Dong, Ximing Lu, Shizhe Diao, Mingjie Liu, Min-Hung Chen, Hongxu Yin, Yu-Chiang Frank Wang. Kwang-Ting Cheng, Yejin Choi, et al. Dler: Doing length penalty ingettinceniiiiiing more intelligence per token via reinforcement learning, arXiv preprint 2xXi:2510011110, 2025b.
Xiang Liu, Peijie Dong, Xuming Hu, and Xiaowen Chu. LongGenBench: Long-context genera- tion benchmark. Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (eds.), Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 865-883, Miami, Florida, USA, November 2024, Association for Computational Linguistics. doi: 10.18653/v1/2024, findings-emmlp.48. URL https://aclatthol.e http://lllattooooyyoro//22/22
org/2024. findings emllp.
48/
Xiang Liu, Zhenheng Tang, Hong Chen, Peijie Dong, Zeyu Li, Xiuze Zhou, Bo Li, Xuming Hu, and Xiaowen Chu. Can lims maintain fundamental abilities under kv cache compression? arXiv preprint arXiv:2502.0199 01941, 2025c.
Xiang Liu, Zhenheng Tang, Peijie Dong, Zeyu Li, Liuyue, Bo Li, Xuming Hu, and Xiaowen Chu. ChunkKV: Semantic- Semantic-preserving KV cache compression for efficient long-context LLM inference. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025d. URL https: http::////peeeevieeee net met.t//orommmdd-20Dnaa....
Chenwei Lou, Zewei Sun, Xinnian Liang Meng Qu, Wei Shen, Wenqi Wang, Yuntao Li, Qing- ping Yang, and Shuangzhi Wu. Adacot: Pareto-optimal adaptive chain-of-thought triggering via reinforcement learning. arXiv preprint arXiv:2505.1 /1896 2025.
Wenjie Ma, Jingxuan He, Charlie Snell, Tyler Griggs, Sewon Min, and Matei Zaharia. Reasoning models can be effective without thinking, 2025. URL inttps://arxiin wrg. wttp:/www.r///b//550... 25504 09858
Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. sl: Simple test-time scaling. arXiv preprint 2rXiv:2501.19393 2025.
Rui Pan, Shuo Xing, Shizhe Diao, Wenhe Sun, Xiang Liu, KaShun Shum, Jipeng Zhang, Renjie Pi, and Tong Zhang. Plum: Prompt learning using metaheuristics. In Lun-Wei Ku, Andre Mar- tins, and Vivek Srikumar (eds.), Findings of the Association for Computational Linguistics: ACL 2024, pp. 2177-2197, Bangkok, Thailand, August 2024. Association for Computational Linguis tics. doi: 10. 10.18653/v1/2022 1/2.......//....nniiii....... 129. URL ht tps::p: / www.aaaclannto...oooo orgp220000.0//222,,, findi pordings-acl..o 1297
13

---

Published as a conference paper at ICLR 2026
Xiaoye Qu, Yafu Li, Zhaochen Su, Weigao Sun, Jianhao Yan, Dongrui Liu, Ganqu Cui, Daizong Liu, Shuxian Liang. Junxian He, et al. A survey of efficient reasoning for large reasoning models: Language, multimodalityy and beyond. arXiv preprint arXiv:2503.21614, 2025.
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.
Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi, Wenjing Zhang, Jiangze Yan, Ning Wang, Kai Wang, Zhaoxiang Liu, and Shiguo Lian. Dast: Difficult--daaptiv slow-thinking for large reason- org/
ing models, 2025. URL htt.::ttt///aaate
/aborr/////////ooee///eee/eeaaee aww./b//..... 04472
Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen Zhong, Na Zou, et al. Stop overthinking: A survey on efficient reasoning for large language models. arXiv preprint arXiv:2503. 2025.
Zhenheng Tang, Xiang Liu, Qian Wang, Peijie Dong, Bingsheng He, Xiaowen Chu, and Bo Li. The lottery LLM hypothesis, rethinking what abilities should LLM compression preserve? In The Fourth Blogpost Track at ICLR 2025, 2025a.
Zhenheng Tang, Zichen Tang, Junlin Huang, Xinglin Pan, Rudan Yan, Yuxin Wang, Amelic Chi Zhou, Shaohuai Shi, Xiaowen Chu, and Bo Li. Dreamddp: Accelerating data parallel distributed Ilm training with layer-wo wise scheduled partial synchronization. arXiv preprint arXiv:2502. 1058,, 2025b.
Zichen TANG, Zhenheng Tang, Gaoning Pan, Buhua Liu, Xin He, Kunfeng Lai, Xiaowen Chu, and Bo Li. Ghost in the cloud: Your geo-distributed large language models training is easily manipulated. In The Fourteenth International Conference on Learning Representations, 2026. www.:ooodmddddongaooiii
URL https: httph:ttttppseeeeeeee http::///ppeeeeeee.eeeee
Shenzhi Wang, Le Yu, Chang Gao, Chujic Zheng. Shixuan Liu, Rui Lu, Kai Dang. Xionghui Chen, Jianxin Yang, Zhenru Zhang, Yuqiong Liu, An Yang, Andrew Zhao, Yang Yue, Shiji Song, Bowen Yu, Gao Huang, and Junyang Lin. Beyond the 80/20 rule: High-entropy minority tokens drive effective reinforcement learning for Ilm reasoning, 2025. URL httttt:///xxii..... org/ /bs/
2506.01933
Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Aruiraj, Xuan He, Jiang, et al. Mmlu-pro: A more robust and challenging multi- task language understanding benchmark. Advances Neural Information Processing Systems, 37:95266-95290 2024.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022.
An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jing Zhou, Jingren Zhou, Junyang Lin, Kai Dang. Keqin Bao, Kexin Yang, Le Yu, Lianghao Deng, Mei Li, Mingleng Xue, Mingze Li, Pei Zhang, Peng Wang, Qin Zhu, Rui Men, Ruize Gao, Shixuan Liu, Shuang Luo, Tianhao Li, Tianyi Tang. Wenbiao Yin, Xingzhang Ren, Xinyu Wang, Xinyu Zhang. Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yinger Zhang, Yu Wan, Yuqiong Liu, Zekun Wang, Zeyu Cui, Zhenru Zhang, Zhipeng Zhou, and Zihan Qiu. Qwen3 technical report, 2025a. URL htthttps://aaaee http///xxivv.oraaa//////e/
09388
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, 2025b. URL httpp://aaxxx org/ abs//bb//aa 2504. 15895
Yixin Ye, Zhen Huang, Yang Xiao, Ethan Chern, Shijie Xia, and Pengfei Liu. Limo: Less is more for reasoning, 2025. URL http:/ttaaa//aa/..o////////////a
03387
14

---

Published as a conference paper at ICLR 2026
Ping Yu, Jing Xu, Jason Weston, and Ilia Kulikov. Distilling system 2 into system 1. arXiv preprint arXiv:2407.06022, 2024.
Junyu Zhang, Runpei Dong, Han Wang, Xuying Ning, Haoran Geng, Peihao Li, Xialin He, Yutong Bai, Jitendra Malik, Saurabh Gupta, and Huan Zhang. Alphaone: Reasoning models thinking slow and fast at test time, 2025a. URL https: / www.vv.org/ab/////. 01122255522888 24863
Longteng Zhang. Xiang Liu, Zeyu Li, Xinglin Pan, Peijie Dong, Ruibo Fan, Rui Guo, Xin Wang, Qiong Luo, Shaohuai Shi, al. Dissecting the runtime performance of the training, fine-tuning, and inference of large language models. arXiv preprint vrXiv:2311.0367. 2023.
Ruiqi Zhang, Changyi Xiao, and Yixin Cao. Long short cot? investigating instance-level switch of large reasoning models. arXiv preprint arXiv:2506.04/ 2025b.
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, 2025c. URL
15778
Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E. Gonzalez, Clark Barrett, and Ying Sheng. Sglang: Efficient execution of structured language model programs, 2024. URL org/aba hte//bb//200 2312. 07104
15

---

Published as a conference paper at ICLR 2026
APPENDIX
Use of Large Language Models
17
Limitations and Future Work
17
Complete Reasoning Strategy
17
C.1
Easy Strategy Prompt
17
C.2
Normal Strategy Prompt
17
Hard Strategy Prompt
18
C,4
Design Rationale
18
Hyperparameter Optimization
18
D
Additional Experimental Details
19
D.I
Cross-Domain Generalizztion
19
D.2
Comparison with When-to-Think Baselines
19
D.3
Oracle Experiment Detailed Results
20
D.4
Model-Specific Threshold Values
20
D.5
Detailed Algorithmic Procedures
D.6
Maximum Token Limits per Model and Benchmark
21
Reasoning Integrity Analysis
21
Experimental Setup
22
Evaluation Prompt
22
E.3 Results and Analysis
23
Additional Overthinking Analysis Across Model Architectures
23
I
Analysis
23
F2
Nannotoo Ressccc--cccaoineveeeen Overthinking Analysis
23
Additional Oracle Analysis Results
24
G.1
DeepSeck-Kl-Qwen- 7B Oracle Analysis
25
G.2
Nemotron- Oracle Analysis
25
DeepSeck-R1-Llama-b Oracle Analysis
25
Cross-Model Oracle Analysis Summary
25
16

---

Published as a conference paper at ICLR 2026
A USE OF LARGE LANGUAGE MODELS
We used LLMs solely to aid and polish the writing (e.g., wording refinement and grammar), without generating or altering experimental designs, methods, results, conclusions. All technical content, analyses, figures, and tables were authored and verified by the researchers.
B LIMITATIONS AND FUTURE WORK
Transferability and Calibration. While our sensitivity analysis demonstrates that strategy thresh- olds are robust across model families (c.g.. transferring parameters from DeepSeck-RI to Qwen3 yields negligible < 0.3% deviation), extremely distinct domains may still benefit from lightweight calibration phase. Future work could explore completely calibration- mechanisms.
Prefill-only Trade-off. Our probe relies solely prefill hidden states predict difficulty. While this design minimizes inference latency by avoiding interruption of the decoding process, it inher- ently ignores generation-time dynamics that might reveal emerging complexity. A promising future direction is to incorporate lightweight generation signals (e.g., early step-wise entropy), though this introduces an efficiency preccision trade-off that must be carefully managed.
Labeling via proxy models. Our difficulty labels rely on proxy model and sampling protocol (10 samples temperature 0.6). Thresholds (a, 8, ?) are tuned per model and may require re-calibration when transferring across domains changing the sampling configuration. In deployment, we rec- ommend light validation phase re-establish thresholds.
Failure modes under tight budgets. For particularly hard or error- error-prone prone cases, aggressive budget reduction can harm accuracy. practical fail-safe is fall back to the Normal strategy when the probe confidence is low, the prefill signal is out-of- out-of-distribution, or the selected strategy underper- forms recent history.
C COMPLETE REASONING STRATEGY
This section provides the complete reasoning strategy configurations used in our three-tier adaptive reasoning framework. Each strategy employs specific prompts designed to guide the model's rea- soning behavior according to the computational requirements identified through our overthinking analysis.
C.I EASY STRATEGY PROMPT
For problems identified easy (overthinking region), a direct approach minimize unnec- essary computational overhead:
<think>
This looks straightforward. Let me solve it directly while double-checking my approach.
</think>
Configuration:
Temperature: 0.5 (lower randomness for direct solving)
Max Tokens: 0.4 x |ax] (reduced computational budget)
Approach: Direct problem-solving with minimal intermediate steps
C.2 NORMAL STRATEGY PROMPT
For problems in the optimal region, employ standard methodical reasoning:
17

---

Published as a conference paper at ICLR 2026
<think>
I'll break this down into clear, logical steps and solve methodically.
Configuration:
Temperature: 0.8 (standard exploration level)
Max Tokens: 1.0 x |Max] (full computational budget)
Top-p: 0.95 (diverse sampling for comprehensive reasoning)
Approach: Step-by-step logical decomposition
C.3 HARD STRATEGY PROMPT
For problems at the capability limit, we focus on efficient resource utilization and early termination of futile paths:
<think>
This appears intricate. I'll outline the main method while being mindful of computational resources.
Configuration:
Temperature: 0.4 (lowest randomness for focused reasoning)
Max Tokens: 0.5 x |Max]
Approach: Strategic method outline implement "Fail Fast" mechanism
C.4
DESIGN RATIONALE
The prompt design reflects our empirical findings from the overthinking analysis:
Easy strategy discourages overthinking by emphasizing directness and verification rather than extensive exploration.
Normal strategy encourages systematic reasoning with full computational resources for optimal performance.
Hard strategy prioritizes resource conservation, identifying likely-to-fail queries early to avoid getting stuck unproductive reasoning loops.
C.5 HYPERPARAMETER OPTIMIZATION
To determine the optimal configuration for these strategies, we avoided heuristic selection and in- stead conducted comprehensive Grid Search experiment on the MATH500 dataset.
Search Space: We evaluated 125 distinct parameter combinations (5 X 5 x 5Easy), varying Temperature (I' E [0.1, 1.2)) and Max Token Ratios (L [0.25x, 1.0x)).
Selection Criterion: We employed a constrained optimization approach:
1. Filter by Accuracy: We first identified all parameter combinations that maintained high accuracy (2 95%) on the validation set.
2. Minimize Cost: From these candidates, we selected the configuration that yielded the lowest average token consumption (960.5 tokens).
18

---

Published as a conference paper at ICLR 2026
Table Comparison Pop-Perforning Strategy (Ours) vs. Heuristic Baselines on MATH500. Our
grid-search tuned configuration achieves the best trade-off between accuracy and efficiency.
Strategy Configuration
Deseription
Acc (%)
Avg Toks
Max Toks
Insight
DiffAdapt (Ours)
Grid-search tuned: =ormal(T=0.88.
95.0
960.5
10,208
Best trade-off between
Hank("=004, 0.5x). Easy(T=0.5, 0.4x)
creativity and stability.
Baseline (Conservative)
Heuristic uniform conservative (7=0.6, full
94.0
1,003.2
12,461
Slightly lower accuracy;
length for all)
higher tnken cost.
Baseline (Aggressive)
Heuristic uniform high temp (T=1.2,
88.0
1,274.8
32,626
Suffers from "reasoning loops" on hard queries. Good efficiency but fails
length for all)
Baseline (Elliciency)
Heuristic aggressive pruning (7=0.3,
0.25x length for all)
complex reasoning tasks.
Result: Table 8 demonstrates that our chosen configuration is empirically optimal, outperforming heuristic baselines in efficiency while preserving top-tier accuracy.
These strategies, combined with the corresponding sampling parameters, implement the adaptive computational allocation strategy motivated by our U-shaped entropy curve analysis.
D ADDITIONAL EXPERIMENTAL DETAILS
D.1
CROSS-DOMAIN GENERALIZATION
To empirically demonstrate the robustness and transferability of DiffAdapt beyond pure math rea- soning, extended our evaluation to diverse out-of-domain benchmarks, including Minerva (sci- entific reasoning), GPQA (graduate-level domain knowledge), and MMLU-Pro (comprehensive general reasoning).
MMLU-Pro Results. We report the detailed zero-shot transfer results on MMLU-Pro in Table 9 By using the probe and thresholds trained solely on the DeepMath dataset, DiffAdapt consistently outperforms the fixed-strategy baseline by 3-7 % across different token budgets and model architec- tures (DeepSeek-P) R -eeek--R--Qeeilll (i--ee//l.lama) This confirms that the "difficulty signal" captured by our probe is generic and effectively transfers to unseen domains without re-training.
Table 9: Performance comparison on MMLU-Pro (OOD Generalization) DifTAdapt is applied zero- shot using probes trained on math data.
DeepSeek-RR R1 -ween-n
PeepSeek-Rl-Llanee Improvement
Token Budget
DiffAdapt (%)
Baseline (%)
Improvement
DiffAdapt (%)
Baseline (%)
33.3%
32.02
28.21
+3.81%
22.14
17.50
+4.64%
50.0%
35.24
31.07
+4.17%
31.29
27.86
+3.43%
66.7%
35.00
31.07
+3.93%
33.74
31.79
+1.95%
83.3%
35.83
30.36
+5.47%
35.45
32.14
+3.31%
100%
36.48
30.71
+5.77%
39,90
32.50
+7.40%
D.2 COMPARISON WITH "WIIEN-TO-TIING BASELINES
We further compared DiffAdapt against specialized "when-to-think" methods like ThinkLess. Un- like these methods which typically require expensive two-stage training (SFT RL), DiffAdapt is training-free, plug-and-pg and-play approach for the LLM.
Setup. We applied DiffAdapt the ThinkLess Stage-1 model ("TL-1.5B TTL-L.5B-Warrupp' Warmup") and compared against their fully trained Stage-2 RL model (TTL-115B-RL')
Results. Table
10
presents the results on MATH500 and GSM8K.
Efficiency: On GSM8K, DiffAdapt consistently outperforms the RL baseline while using fewer tokens.
On MATH500, while the RL model achieves higher peak accuracy, Diff'dapt outperforms the Warmup baseline by significant margins (+4-8%) and achieves
Effettfffeei
19

---

Published as a conference paper at ICLR 2026
competitive performance the RL model in low-resource regimes using ~35-500 fewer tokens, without requiring any RL training.
Table 10: Comparison against ThinkLess (TL) baselines.
DiffAdapt is applied to the TL-1,5B-
Warmup model.
MATHE500
GSMSK
Budget
Warmup-
DifTAdapt
TL-1..BI-I.
Analysis
TI-Warmup DiffAdapt
Analysks
33. 35
64.4% (851 tok)
55.6% (1039 tok)
+8.%% Lass Toke
67.0% (336 tok)
66.1% (409 tok)
+0.95
50.0%%
67.6% (954 tok)
63.3% (1460 tok)
+4.3% 1-35% Taks
77.0% (357 tok)
72.0% (465 tok)
+5.0%
72.2%
68.0% (973 tak)
69.2% (1775 tok)
Comparable 550% Toks
78.2% (361 tok)
74.3% (510 tok)
+3.9%
1009
sok)
73.5% (2020 rok)
peaks higher
78.6% (364 tok)
(573 tok)
Comporable
D.3 ORACLE EXPERIMENT DETAILED RESULTS
This subsection provides the complete numerical results from Oracle experiment across eight reasoning benchmarks. Table II shows the accuracy and average token consumption for each strat- egy on every benchmark.
Table 11: Detailed Oracle Experiment Results Across Reasoning Benchmarks With Qwen3-4B
Easy Strategy Acc (%)
Normal Strategy Acc (%)
Hard Strategy
Oracle Selection
Benchmark
Tokens
Tokens
Acc (%)
Tokens
Acc ( (%)
Tokens
GSMSK
89.90
169.93
93.20
561.80
93.50
511.55
96.20
197.96
MATH
82.80
718.33
96.20
2662.06
93.40
2424.69
1278.90
GPQA
46.46
812.55
50.51
3022.33
47.98
2952.44
70.20
1001.14
MMLU-Pro
601.36
526.14
65.36
2076.16
62.50
1873.14
74.64
690.04
Minerva
46.69
468.74
55.15
2795.14
53.31
2248.22
65.81
866.31
OlympiadBench
50.96
1628.91
73.63
6722.40
68.59
5895.97
76.89
2756.25
AIME 2024
16.67
3504.90
60.00
10672.23
46.67
10166.87
66.67
4429.37
AIME 2025
16.67
2442.83
53.33
13733.47
40.00
10634.77
56.67
4675.00
Key Observations. The detailed results reveal several important patterns: (1) Strategy Distribu- tion: Across all problems, 82.3% benefit from Easy strategy, 7.7% from Normal strategy, and 10.0%
from Hard strategy, confirming the prevalence of overthinking in current reasoning approaches. (2) Benchmark Characteristics: Mathematical competition problems (AIME 2024/2025) require the highest computational resources, while basic arithmetic (GSM8K) achieves optimal performance with minimal tokens. (3) Universal Improvement: Oracle selection achieves higher accuracy than any fixed strategy across all benchmarks while maintaining efficient token usage. (4) Efficiency Gains: The Oracle demonstrates substantial token savings compared to always using Normal or Hard strategies, with efficiency improvements ranging from 3x (GSM8K) to 5x (AIME series). These results provide the empirical foundation for our DiffAddat framework and establish clear performance targets for practical adaptive reasoning systems.
D.4 MODEL-SPECINC THRESHOLD VALUES
This subsection provides the specific threshold values used for difficulty classification across differ- ent models in our framework. The thresholds a (correctness), 3 (entropy), and y (correctness) are determined empirically for each model to optimize the strategy assignment performance.
Table 12: Model-Specific Threshold Values for Difficulty Classification
Model
(Normal)
B (Entropy)
? (Hard)
DeepSeek-RR -iobeekk-R-QQe---BB
0.85
0.35
0.60
DeepSeek-RR RI-Llama-88
0.85
0.35
0.60
Qwen3-4B
0.88
0.32
0.65
Threshold Selection. We use a heuristic procedure guided by the each model (no exhaustive search). Concretely, we pick the knee where high-correctness,
scatter of
20

---

Published as a conference paper at ICLR 2026
low-entropy points concentrate; choose 3 at the elbow separating low high-uncertainty regimes; and set y to capture the reliability drop-off region in correctness. We optionally verify stability with small validation split. This selection ensures that:
Normal threshold (a): Captures problems where the model performs consistently well with low uncertainty
Entropy threshold (9): Distinguishes between confident and uncertain predictions
Hard threshold (+): Identifies problems beyond the model's reliable capability range
These model-specific thresholds reflect the inherent differences in reasoning capabilities and uncer-
tainty patterns across different architectures and scales.
D.5 DETAILED ALGORITHMIC PROCEDURES
This subsection provides the complete algorithmic descriptions three main stages our DiffAdapt framework. These algorithms detail the implementation procedures that correspond to the conceptual framework presented in Section
Algorithm Data Generation with Proxy Model Sampling
Input: Set of problems X' { LLM, thresholds a, 3, y
2: Initialize labeled dataset D +-
3: for all problem x X' do
4: Generate
10 complete reasoning sequences with max length 32K 5: Compute correctness rate C(z) and average entropy (x)
6: C(x) > a and H
then
7:
Ylahel t 'Normal'
8:
else if C(x) < then
9:
Vlabel + 'Hard'
else
11:
Mlabel < 'Easy" end if
D Overthinking cases
13:
Add (z, Vlabel) to D
14:
end for
15:
Output: Labeled dataset D
D.6 MAXIMUM TOKEN LIMITS PER MODEL AND BENCHMARK
This subsection reports the maximum token limits used for each model each benchmark and de- scribes how they were determined. For each model-benchmarkk pair, we first ran the model under generous cap (e.g., 32K tokens) to observe its longest response length in a less constrained setting. We then selected a nearby rounded integer as the ber-benchmark max tokens used our analy- ses. This procedure standardizes evaluation across tasks and enables percentage- truncation in Figures [nd
Procedure example. With 32K cap, Qwen3-4B produced longest responses of approxi- mately 1,500 tokens on GSM8K and 18,000 tokens on AIME24; we therefore set max tokens 1, 1,500 and 18,000 for those benchmarks, respectively. Analogous rounding applied all model bench-bbnccmark pairs (see Table 13
E REASONING INTEGRITY ANALYSIS
A primary concern with efficiency-orienteee reasoning methods is the potential risk of compromising reasoning integrity- speciffcall...lll.l..... whether aggressive token reduction leads to carly truncation or logical gaps. To rigorously evaluate this, conducted blind, pairwise L.M-assa-Juude study.
21

---

Published as a conference paper at ICLR 2026
Table 13: Maximum token limits (in tokens) per model and benchmark (ID and OOD), Values are
rounded from observed maxima under large cap (e.g., 32K).
Model
GSMSK
MATH
AIME 2024
AIME 2025
OlympiadBench
Minerva
MMLU-Pro
GPQA
Qwen3-4B
1500
12000
18000
18000
15000
3500
3000
4000
DeepSeek:RR RI- 11-Qwen-7B
500
3000
15000
16000
5500
1750
3000
5500
DeepSeck- RI Ril-Llam--Be
700
3000
14000
14000
5500
1750
1750
3000
Nemotron- 1.5
3500
4000
7000
6000
5500
5000
3500
5000
ThinkPrune 7B
500
3000
15000
14000
5500
1750
2500
4500
E.1 EXPERIMENTAL SETUP
We randomly sampled N = 50 queries from the GSM8K test set. For each query, we generated two responses:
System A (DiffAdapt): Our proposed method with adaptive strategy selection.
System B (Baseline): The standard Normal strategy (Temperature=0..8 full token budget).
impartial judge. To ensure fairesss, the evaluation was blind (model identities were anonymized) and pairwise (side-by-side comparison). The judge was ex- plicitly instructed evaluate based on logical completeness, coherence, and conciseness, and to penalize any instances of unjustified truncation.
We employed Qwen3-30B-A3B
E.2 EVALUATION PROMPT
The specific prompt used for the LLM--as-aJuudee evaluation is provided below. explicitly asks the judge to focus on the preservation of coherent reasoning under token constraints.
LLM-as-a-Judge Prompt
You will compare two systems on the same GSM8K math word problem.
Problem: (problem)
Ground-truth solution (for verification only): (ground-truth solution]
System A:
Strategy: (strategy Tokens used: (tokens a}
Final preciction: (Correst/Incorreet)
Reasoning trace:
<<<
(reasoning trace..A
>>>
System B:
Strategy: (strategy_2)
Tokens used: (tokens.B)
Final prediction: (Correst/Incorrect)
Reasoning trace: <<<
(reasoning.trace.l) >>>
Decide which reasoning trace better preserves coherent, legically complete reasoning under tight token budgets. Explain your reasoning and output the winner (System A, System B, or Tie).
22

---

Published as a conference paper at ICLR 2026
E.3 RESULTS AND ANALYSIS
Table summarizes the results of the blind evaluation.
Table 14: Blind pairwise comparison of reasoning quality (N=50) by Qwen3-30B Judge.
Outcome
Count
Percentage
Judge's Common Rationale
DiftAdapt Wins
38
76%
"More direct," "Avoids unnecessary repetition." "Efficient logic"
Baseline Wins
6
12%
"More detailed explanation" (in cases). "Truncation" (in case)
Tie
6
12%
"Both reasoning paths are identical"
Frequency of Logical Failure We performed manual failure analysis the 6 cases where the Baseline won:
Subjective Preference (5 cases): The Baseline produced a more verbose explanation which the judge preferred, even though DiffAdapt's response was correct and logically complete.
Truncation Error (1 case): Only a single instance (2%) involved actual logical failure due aggressive token reduction (misclassified Easy).
This low failure rate (2%) confirms that DiffAdapt's fallback mechanisms (Normal strategy for am- biguous cases) effectively preserve reasoning integrity while significantly reducing computational cost.
F ADDITIONAL OVERTHINKING ANALYSIS ACROSS MODEL ARCHITECTURES
To demonstrate the universality the overthinking phenomenon, we present additional overthinking analysis results for two more model architectures: DeepSeek-R CeepSeek-RI-Distille R-Dissill--wee-n11.... 1.5B and Nemotron- Research-Reassoinng. Secasoning-Qwer Qwen- 1.5B. These results complement the main analysis presented in Sec- tion 3 and provide further evidence that the U-shaped entropy pattern is consistent across different model sizes and architectures.
DEEPSEEK-RE R1 -DISTILLL SEEE---EENNNNNN 1.5B OVERTHINKING ANALYSIS
Figure shows the overthinking analysis for the DeepSeek-BR ---beeek------iill----- RR--DisilllQwen---- model. Despite being a smaller 1.5B parameter model, it exhibits the same characteristic U-shaped entropy curve:
Simple Problems (Difficulty 1-2): High entropy with good correctness, indicating over- thinking behavior
Certainty Region (Difficulty 3-6): Reduced entropy with maintained performance
Difficult Problems (Difficulty 8+): Increased entropy with declining performance
The entropy reduction of 23.3% from simple to optimal regions demonstrates strong overthinking evidence, consistent with our findings across model architectures.
F.2 -
OVERTHINKING ANALYSIS
Figure pressensss the analysis for Nemotron- -eserorchheeeehhh-- Research-Reassoingg Reasoning- 1,5B, another 1.5B param- eter reasoning model. This model shows the most pronounced U-shaped pattern:
Simple Problems (Difficulty 1-2): High entropy with strong correctness, showing clear overthinking
Certainty Region (Difficulty 3-6): Significantly reduced entropy with peak performance
Difficult Problems (Difficulty 8+): Highest entropy with declining accuracy
This model demonstrates 21.3% entropy reduction from simple to optimal regions, providing ad- ditional validation of the overthinking phenomenon across different reasoning architectures.
23

---

Published as a conference paper at ICLR 2026
Revealing Overthinking: Why Simple Problems
High Reasoning Uncertainty
Overthinking Region
Cortainty Rugion
Capabillty Limit
Generation Entropy (Reasoning Uncortainty)
0.80
Correctnees Rate
0.56
0.4
0.45
Comadrees Raie
0.40
Generat Entroyy (Uncertamt)
Problem Difficulty
Figure 5: Overthinking phenomenon in DeepSeek- SrepSeck-R--Dingg BIik--RR-DD------OI RI-Distil-Qwen--..B model showing the char- acteristic U-shaped entropy pattern across difficulty levels.
Revealing Overthinking: Why Simple Problems Show High Reasoning Uncertainty
Generation Entropy (Reasoning Uncertainty)
@vvrthinking Region
Certainty Region
Capability Limii
Correciness Rale
0.8
0.7
02
0.6
Genwrsdoon Entropy (Unowtsaityy
Problem Difficulty
Figure 6: Overthinking phenomenon in Cestrro--Reeearc--Reeaae -aapa---eeee1555 model demon- strating the universal U-shaped entropy pattern.
G ADDITIONAL ORACLE ANALYSIS RESULTS
This section presents comprehensive Oracle analysis results across multiple model architectures to validate the generalizability of our findings. We conduct the same Oracle experiment described in Section on three additional models: DS-Qwen-7B, Jemotron-1.5B. and SeepSeek-Rl-Llamee These models represent different scales, architectures, and training methodologies, providing robust evidence for the universal applicability of adaptive reasoning strategies.
24

---

Published as a conference paper at ICLR 2026
G.1 DEEPSEEK- RI RI-QWEN-7B ORACLE ANALYSIS
Figure 7 shows the performance-token trade-offs for ReepSeek-RI-Qwen- across all eight rea- soning benchmarks. The results demonstrate consistent Oracle superiority with average improve- ment of +12.3% over the best fixed strategy, validating our findings across larger model scales.
Performance Token Consumption for Different Strategy Selections
GSMBK
MATH
AIME24
AIME25
150
Performacce (5)
x
*
50
40-
75
15
20
370
.00
480
450
16K
OlympiadBench
GPOA
Minerva
MMLU-PRO
76
Always Easy
Always Medium
Performance 0%a
Hard
Assignment
10
50
28
16
1.x
1.6
630
1:24 3.5c
Average per Average per
Average Tokens per
Average per
Figure 7: DeepSeek-R1-Qn R1-ween--7 Oracle Analysis: Performance vs. Token Consumption Trade- offs. The Oracle strategy (gold stars) consistently outperforms all fixed strategies across mathe- matical reasoning tasks (GSM8K, MATH), competition problems (AIME24/25, OlympiadBench), and out-of-domain benchmarks (GPQA, Minerva, MMLU-Pro), achieving optimal Pareto efficiency with average +12.3% accuracy improvement.
1.55 G.2 NEMOTRON- 1.5B ORACLE ANALYSIS
Figure presents the Oracle analysis for Nemotron-L.35 demonstrating that adaptive strategy se- lection benefits extend to smaller model scales. Despite the reduced parameter count, the Oracle achieves +7.9% average improvement while maintaining superior token efficiency.
G.3 DEEPSEEK- R RI-LLAMA- 8B ORACLE ANALYSIS
Figure shows the most compelling results from DeepSeek-RR RI----RR----aaaai which achieves the high- est Oracle benefits with +16.2% average improvement. This model demonstrates exceptional token efficiency, with Oracle strategy consuming significantly fewer tokens while achieving superior per- formance across all benchmarks.
G.4 CROSS- ORACLE ANALYSIS SUMMARY
Table 15 summarizes the Oracle analysis results across all four models, demonstrating the universal effectiveness of adaptive strategy selection.
Table 15: Cross-Model Oracle Analysis Summary
Model
Parameters
Avg. Accuracy Improvement
Dominance Rate
Token Efficiency
Qwen3-4B
4B
+7.2%
100%
Mixed
DS-Qwen-7B
7B
+12.3%
100%
Moderate
Nemotron-I-35 1.5B
1.5B
+7.9%
100%
High
DeepSeek-HH 11---1----aan 8BBma-8-88
8B
+16.2%
100%
Excellent
Key Insights:
25

---

Published as a conference paper at ICLR 2026
Performance vs. Token Consumption for Different Strategy Selections
MATH
40-
Performance (N)
*
95.
92.5
60-
10
87.
25
80
OlympiadBench
GPQA
Minerva
MMLU-PRO
Always Fasy
Always Medium
Perfor mance (%)
Always Hard
70-
55-
Oracle Assignment
65
40
45
c0-
40
2.00 326
3K
2.40
2.5K
3.0k
2K Average Tokens per
Average e Tokens per
Average
Average Problem
Figure 8: Nemotron-1.55 Oracle Analysis: Performance vs. Token Consumption Trade-offs. Even smaller scale (1.5B parameters), the Oracle strategy demonstrates consistent advantages across all benchmarks, achieving +7.9% average accuracy improvement with efficient token utilization, confirming the scalability of adaptive reasoning approaches.
Performance vs. Token Consumption for Different Strategy Selections
GSMBK
MATH
AIME24
AIME25
15
Perforannc (N)
X
15
30
60
30-
70-
15
70
70
10
15K
1.8C
111
OlympiadBanch
GPQA
Minerva
MMLU-PRO
Always Lasy
*
Always Medium
Perforance (95)
10-
55
Always Hard
Oracle Assignment
13
50-
45-
65
12K
1Ac 5,
1.6 1Ac
7.4
3.5
660
16
1.7K
1.4c
3.4k
1.24
1.4K
Average Tokens per Problem
Average Tokens per Problem
Average Tokens per Problem
Average Tokens per Problem
Figure 9: DeepSeek-RI-Llamaa offs. This model shows the strongest Oracle benefits with +16.2% average accuracy improvement and exceptional token efficiency. The Oracle strategy achieves superior performance while consum- ing 10-35% fewer tokens than fixed strategies, demonstrating optimal resource utilization.
Oracle Analysis: Performance
Token Consumption Trade-
Universal Dominance: Oracle strategy achieves 100% dominance rate across all models and benchmarks
Scalable Benefits: Performance improvements scale with model capability, ranging from +7.2% to +16.2%
Consistent Token Efficiency: All models show improved resource utilization with adap- tive strategy selection
Robust Generalization: Benefits span mathematical reasoning, competition problems, and out-of-domain tasks
26

---

Published as a conference paper at ICLR 2026
These comprehensive results provide strong empirical evidence that adaptive reasoning strategies offer universal benefits across diverse model architectures, scales, and problem domains, directly motivating the design and deployment of our DiffAdapt framework.
27


Reviews:
[{"review_id": "klAD1LrhQc", "reviewer": "Reviewer_zvSP", "summary": "This paper studies the overthinking phenomenon in reasoning LLMs, where models exhibit high uncertainty and unnecessarily long reasoning traces even on simple problems. Through systematic entropy analysis across models and datasets, the authors reveal a consistent U-shaped entropy pattern across difficulty levels, indicating computational inefficiency. Building on this finding, the authors propose DiffAdapt, a difficulty-adaptive inference framework that predicts problem difficulty using a lightweight probe on hidden states and dynamically selects from Easy/Normal/Hard reasoning strategies. The approach requires no retraining of the base LLM and can be deployed with existing inference frameworks. Empirical results on five models and eight benchmarks show that DiffAdapt can reduce token usage by up to 22.4% while maintaining or improving accuracy.", "questions": "- Have the authors tested whether a probe trained on DeepMath generalizes to other domains or reasoning benchmarks?\n- How transferable is the probe across models of similar size or architecture?\n- What motivates the specific parameter choices for the three reasoning strategies, and have alternative configurations been compared experimentally?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The proposed DiffAdapt framework only requires training a small external probe, yet it effectively improves reasoning efficiency and model performance without modifying or fine-tuning the LLM itself.\n- The identification of a consistent U-shaped entropy–difficulty pattern is novel and insightful. It sheds light on the overthinking behavior of reasoning models and provides valuable guidance for future “long-to-short” reasoning research.\n- The framework is complementary to reinforcement learning–based length control methods (e.g., ThinkPrune). This suggests DiffAdapt can be combined with those training-based long-to-short approaches for even greater efficiency gains.", "weaknesses": "- Model-specific probe requirement: The main weakness lies in the need to train a separate probe for each LLM. This introduces (1) additional training overhead and (2) potential generalization issues — the probe’s limited transferability could restrict DiffAdapt’s applicability across different models or domains.\n- Lack of probe analysis experiments: The paper would be stronger if it included an analysis of the probe’s generalization ability. For example: (a) How well does a probe trained on the DeepMath dataset transfer to other reasoning tasks or domains? (b) Can probes be transferred between models of similar architecture or scale (e.g., between Qwen-3/4B and LLama-3B)?\n- Ad-hoc reasoning strategy design: The selection of parameters for the Easy/Normal/Hard strategies (e.g., temperature = 0.5/0.8/0.4, token ratio = 0.4×/1.0×/0.5×) feels somewhat heuristic. The paper lacks experimental justification or ablation to support why these specific hyperparameters are optimal.", "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": "0xNS4FB5Vd", "reviewer": "Reviewer_PqhK", "summary": "The paper observes a U‑shaped entropy–difficulty pattern (high entropy for easy/hard, low for medium) and posits “overthinking” on easy items. DiffAdapt trains a light probe on prefill hidden states to predict Easy/Normal/Hard and selects a matching reasoning strategy at inference. Across five model families and eight benchmarks (including OOD), DiffAdapt maintains or improves accuracy while reducing tokens (up to 22.4%). The approach is orthogonal to Length‑Control RL and integrates with mainstream serving stacks.", "questions": "- Multi‑task generalization: In a realistic mixed‑task setting, how robust are the probe and (α,β,γ) without re‑tuning? Can you show cross‑domain transfer or per‑task calibration drift?\n- Sensitivity of results to (α,β,γ); can you show heatmaps or robust ranges?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Strategy selection (not just length/temperature) based on a learned difficulty proxy. Notably, the paper’s identification of a U-shaped entropy–difficulty curve is an interesting empirical finding. Previous work mainly reported monotonic increases of entropy with problem difficulty, but not this symmetric “overthinking” pattern on easy items. This observation provides a concrete diagnostic for inefficiencies in reasoning-token allocation.\n- Consistent savings across models/benchmarks; OOD and LC-RL results broaden applicability.\n- Oracle and ablation studies (vs. fixed strategies, DEER) clarify where gains come from.", "weaknesses": "- Heuristic thresholds (α,β,γ) are set per model from scatterplots; it’s unclear how stable they are across multi‑task mixtures and domain shifts, or how often the probe/thresholds need re‑tuning.\n- Stage‑1 data generation (multiple long samples per item) is expensive for new domains; low‑budget or few‑shot variants are not discussed.\n- Missing head‑to‑head with “when‑to‑think” switchers (e.g., Thinkless/AdaCoT); this would position the magnitude of gains.", "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": "O9vTRbhNgQ", "reviewer": "Reviewer_S9HU", "summary": "The paper introduces DiffAdapt, a lightweight, training-free framework for difficulty-adaptive reasoning in large language models (LLMs). The authors identify a U-shaped entropy pattern across problem difficulty levels, indicating an “overthinking” phenomenon where models exhibit high uncertainty on easy problems despite high accuracy. To address this, DiffAdapt uses a small probe trained on the model’s hidden states to predict problem difficulty and select among Easy/Normal/Hard inference strategies dynamically, without retraining the base model. Experiments on five LLMs and eight benchmarks show that DiffAdapt maintains or improves accuracy while reducing token usage by up to 22.4%, and decreases end-to-end inference time by up to 6×. The framework is compatible with existing inference systems and complements length-control RL methods.", "questions": "1. How sensitive is DiffAdapt to the chosen thresholds (α, β, γ)? \n\n2. Could DiffAdapt extend beyond reasoning tasks—for example, to summarization or dialogue where difficulty varies dynamically?\n\n3. How does probe misclassification (wrong strategy selection) affect performance or stability?\n\n4. In the Hard strategy, generating only a \"method outline\" may fail on problems requiring detailed computation. Could you discuss potential limitations of this approach—particularly for problems that may still benefit from detailed computation?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The work identifies and systematically analyzes a previously underexplored phenomenon—overthinking in reasoning LLMs—via a novel U-shaped entropy pattern analysis. This empirical finding is insightful and establishes a theoretical foundation for adaptive reasoning.\n \n2. The proposed DiffAdapt framework is conceptually elegant: predicting difficulty from hidden states and dynamically adjusting reasoning strategy without any retraining of the base model.\n\n3. The experimental section is comprehensive, spanning five models and eight benchmarks (both in-domain and out-of-domain). The Oracle analysis provides solid upper bounds that justify the design choices.\n\n4. The contribution is practically important: it offers a lightweight, deployment-ready framework. DiffAdapt requires no retraining of the base LLM and is compatible with common inference optimizations.", "weaknesses": "1. The evaluation is heavily focused on mathematical and scientific reasoning. It remains unclear whether the proposed three-regime (Easy/Normal/Hard) framework generalizes to other domains, such as commonsense reasoning, dialogue, or creative writing, where difficulty is harder to quantify.\n\n2. The difficulty predictor leverages only prefill hidden states, which simplifies deployment but may overlook valuable cues from generation dynamics. Discussion or experimentation on this trade-off would be helpful.\n\n3. The paper does not provide ablations on the probe’s architecture or its sensitivity to training data size. Such results would clarify how much of DiffAdapt’s performance depends on probe complexity or data scale.", "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": "QCD0qqCqX4", "reviewer": "Reviewer_7RDd", "summary": "This paper investigates the inefficiency of reasoning LLM, which often generate excessively long reasoning traces regardless of task difficulty. Through a systematic empirical analysis across multiple models and datasets, the authors discover a U-shaped entropy pattern on simple problems, low entropy on medium difficulty, and again high entropy on hard problems. This reveals a phenomenon termed overthinking, where models over-allocate computational resources to easy problems. Building on this observation, the paper proposes DiffAdapt, a lightweight, training-free framework that predicts problem difficulty from the model’s hidden states and dynamically selects among three reasoning strategies to adapt computational budget during inference. DiffAdapt requires no retraining of the base LLM and is compatible with existing inference systems.", "questions": "1. Appendix D.2 states that thresholds for distinguishing Easy/Normal/Hard are heuristically chosen per model based on entropy, with only a small sanity check. Since these thresholds depend on model family, temperature, and domain, does this design undermine generalization and zero-shot ability? Would each new deployment require manual calibration?\n2. Could the authors show how often misclassification (e.g., Hard -> Easy) causes early truncation or logical failure, and evaluate the real effectiveness of this fallback mechanism during deployment?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The proposed DiffAdapt is a simple yet effective solution that does not require retraining or finetuning the base LLM. By attaching a small hidden state probe to predict difficulty, the method enables difficulty-aware computation allocation at inference time, making it highly practical for real world deployment. The paper conducts extensive experiments across five reasoning models and eight benchmarks, providing convincing evidence for the generality and robustness of DiffAdapt across architectures, scales, and domains. It can be seamlessly combined with reinforcement-learning-based length control methods such as ThinkPrune and LC-R1, as well as deployed under common inference frameworks, which underscores its engineering value and scalability.", "weaknesses": "1. Limited evaluation of reasoning-chain integrity under tight computational budgets. The paper primarily reports accuracy, token reduction, and speed-up metrics but does not evaluate the integrity or consistency of reasoning traces (e.g., whether truncated reasoning affects logical completeness). This omission makes it difficult to judge whether DiffAdapt preserves coherent chain-of-thought reasoning when aggressive token reduction is applied.", "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)