MAGO: Beyond Fixed Hyperparameters with Multi-Objective Pareto Optimization for Hybrid LLM Reasoning
Authors: Hongcheng Ding
Xuanze Zhao
Ruiting Deng
Shamsul Nahar Abdullah
Deshinta Arrova Dewi
LIU QINGYU
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-objective optimization
Pareto optimization
Large language models
Hybrid reasoning
Chain-of-thought reasoning
Reinforcement learning
Primary area: reinforcement learning

Abstract: Large language models (LLMs) with advanced step-by-step reasoning capabilities have achieved remarkable performance in complex problem-solving through chain-of-thought (CoT) reasoning. However, uniformly applying elaborate reasoning to all queries creates substantial computational inefficiency, as many problems can be solved directly without extended reasoning chains. Current hybrid reasoning approaches rely on static hyperparameters and heuristic single-objective optimization, leading to suboptimal trade-offs and poor adaptation to varying task complexities. To address these limitations, we propose a multi-objective adaptive generation optimization (MAGO) framework, which integrates multi-objective optimization with dynamic adaptive weighting into hybrid reasoning. MAGO optimizes three competing objectives simultaneously: accuracy (maintaining solution correctness), efficiency (minimizing computational costs through appropriate mode selection), and calibration (ensuring mode selection aligns with model capabilities). The framework employs Pareto frontier maintenance with correlation-aware optimization to automatically explore the full trade-off space, avoiding the spatial constraints that limit fixed-weight approaches to narrow cone-shaped regions of the objective space. Unlike existing methods requiring manual hyperparameter tuning, MAGO's Pareto optimization dynamically adapts weights based on task complexity and training progress, achieving principled and adaptive decision-making across varying problem complexities. Comprehensive evaluation on mathematical reasoning benchmarks including AIME, Minerva Algebra, MATH-500, and GSM-8K shows $2.2\times$ to $3\times$ token-efficiency gains and relative accuracy improvements of $0.6\%$ to $9.4\%$ over heuristic baselines, while remaining competitive with the strongest task-specific models. Additional experiments on CommonsenseQA and MedQA further confirm the framework's generalizability beyond mathematics, achieving $1$ to $2\%$ higher accuracy and approximately $2\times$ efficiency improvement without additional fine-tuning.

Published as a conference paper at ICLR 2026
MAGO: BEYOND FIXED HYPERPARAMETERS WITH MULTI-OBIECTIVE PARETO OPTIMIZATION FOR HY-
BRID LLM REASONING
Hongcheng Ding Dongfang College, Zhejiang University of Finance and Economics
Xuanze Zhao
Ruiting Deng Yunnan University of Finance and Economics
Dongfang College, Zhejiang University of Finance and Economics
Shamsul Nahar Abdullah INTI International University
Deshinta Arrova Dewi INTI International University
Qingyu Liu SEGi University
ABSTRACT
Large language models (LLMs) with advanced step-by-step reasoning capabili- ties have achieved remarkable performance in complex problem-solving through chain-of- of-thoughtt (CoT) reasoning. However, uniformly applying elaborate rea- soning to all queries creates substantial computational inefficiency, as many prob- lems can be solved directly without extended reasoning chains. Current hybrid reasoning approaches rely static hyperparameters and heuristic single-objective optimization, leading to suboptimal trade-offs and poor adaptation to varying task complexities. To address these limitations, propose multi-objective adaptive generation optimization (MAGO) framework, which integrates multi- objective optimization with dynamic adaptive weighting into hybrid reasoning. MAGO optimizes three competing objectives simultancously: accuracy (main- taining solution correctness), efficiency (minimizing computational costs through appropriate mode selection), and calibration (ensuring mode selection aligns with model capabilities). The framework employs Pareto frontier maintenance with correlation-awant optimization to automatically explore the full trade-off space, avoiding the spatial constraints that limit fxed-weight approaches to narrow cone- shaped regions of the objective space. Unlike existing methods requiring manual hyperparameter tuning. MAGO's s Pareto optimization dynamically adapts weights based on task complexity and training progress, achieving principled and adaptive decision- makiin across varying problem complexities. Comprehensive evalua- tion mathematical reasoning benchmarks including AIME, Minerva Algebra, MATH-500, and GSM-8K shows 2.2x to 3x effnnneffcc gains and relative accuracy improvements of 0.6% 9.4% over heuristic baselines, while remain- ing competitive with the strongest task-specific models. Additional experiments on C and MedQA further confirm the framework's generalizabil- ity beyond mathematics, achieving 1 to 2% higher accuracy and approximately 2x efficiency improvement without additional fine-tuning.
INTRODUCTION
Recent breakthroughs in step-by-step reasoning capabilities have enabled LLMs to achieve unprece- dented performance in complex problem-solving. Reasoning-enalee models such as DeepSeek- R1 (DeepSeek-Al Al 2025) and Claude (Anthropic 2025) employ CoT reasoning (Wei et al. 2022) to decompose complex problems into manageable sub-steps, thereby simulating human cognitive pro- cesses (Nye al. 202 Jung al., (0022). This paradigm has proven particularly effective in math-
ematical reasoning (Hendrycks et al. 2021 Cobbe et al. 2021 and logical inference tasks (Saha
al. 2020 Wang et al. 2023).
Equual contributions
1 'Comesponding Author: 124025877ast www.ttdentt.neeeee.m newie intiiiiiddd newinti.edu.my. (INTT International
Uni University)

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Ling
Duddion
Lomp
Duery
Colloge
Dasry
Klaannn Puin
Callertion
Figure 1: (A) Traditional single-objective approaches and (B) MAGO's multi-objective framework.
However, uniformly applying elaborate reasoning to all queries creates significant efficiency prob- lems in practical deployment scenarios. Large-scale deployment scenarios must handle diverse query types ranging from simple factual questions requiring direct answers to complex multi-step problems necessitating extensive reasoning (Rajpurkar et al. 2018 Khashabi et al., 2020). Indis- criminate use of reasoning models for all inputs leads to substantial computational waste, as rea- soning models generate hundreds to thousands of tokens for problems that could be solved with direct answers, resulting in 5 to 20 times higher resource consumption compared to non-reasoning approaches (Kaplan et al., 2020 Hoffmann et al. 2022 Suzgun et al.) 2022 Fu et al. (023)..
To address the substantial computational costs and resource consumption inherent in reasoning- enabled models, current research has concentrated on several key directions to improve infer- ence efficiency. Hybrid reasoning mode selection approaches develop systems that dynamically choose between detailed reasoning and concise response generation through learnable control mech- anisms (Fang al.] 2025; Zelikman et 2024 Raposo et al. 2024), utilizing specialized optimiza- tion algorithms for adaptive mode switching. Test-time compute scaling techniques allocate com- putational resources dynamically during inference to optimize the trade-off between accuracy and efficiency (Snell et al. 22044] /Zhang et al. 2025; Lyu et al. (025), enabling models to achieve bet- ter performance through adaptive inference-time computation rather than larger model parameters. Moken-budget--ae
reasoning methods explicitly incorporate computational cost constraints into the reasoning process (Han et al. 2024). developing frameworks that balance reasoning depth with predefined computational budgets. However, these methods often produce suboptimal solutions that excel in one aspect (such as accuracy or efficiency) while sacrificing others.
To address these challenges, we propose the MAGO framework, theoretically grounded approach that reformulates hybrid reasoning as a multi oujetiobeeectvv optimization problem. MAGO incorporates dynamic weight adaptation mechanisms that adjust with training progress and implements Pareto frontier maintenance (Deb et al.; 2002) Miettinen 1999) with correlation-aware weight selection to support more refined reasoning decisions. This method eliminates the need for manual hyperpa- rameter tuning while achieving mathematically sound trade-off's among three competing objectives: accuracy (maintaining solution correctness), efficiency (minimizing computational costs), and deci- sion calibration (ensuring mode selection aligns with the model's 's actual capabilities) (see Figure The main contributions of this paper are as follows:
We identify two performance gaps in existing hybrid reasoning systems: (1) static weight configurations lead to model under-performance across different scenarios, and (2) strong correlations between objectives cause multi-objective optimization to under-perform.
We propose MAGO, a multi-objective optimization framework addressing these gaps through: (1) reformulating hybrid reasoning as multi-objective optimization problem, (2) using Pareto optimization for dynamic weight selection, and (3) achieving end-to-end integration from training to deployment with zero inference overhead.
Our framework achieves 2.2x to 3x computational efficiency improvements while simulta- neously improving accuracy by 0.6% to 9.4% across mathematical reasoning benchmarks. Cross-domain evaluation on CommonsenseQA and MedQA demonstrates generalizability beyond mathematics without fine-tuning.
2 MOTIVATION
In this section, we introduce static weight challenges and multi-objective optimization challenges.
Challenge #1: Static Weight. Current hybrid reasoning approaches (Fang et al. 2025 Shao et al. 2024) rely on fixed hyperparameters that fail to adapt to varying task complexities across different queries and training datasets. We make a series of attempts across various values mathematical reasoning benchmarks to explore this limitation. The results reveal three critical limitations of static weighting schemes. First, different a values cause severe mode selection imbalances (Figure
2

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Centred Wengge [r]
Figure 2: Static weight limitations in hybrid reasoning optimization. (A) Mode selection imbalance across control weight a values with accuracy performance. (B) Dataset-specific a sensitivity across mathematical reasoning benchmarks. (C) Fixed weight search constraints in objective space. (D) Cone-shaped optimization limitations in multi-objective landscape.
=0.0001 leads to over 90% short-mode usage, sacrificing accuracy on complex problems, while a 0.01 results in over 80% think-mode usage, negating efficiency gains. Second, optimal varies sig- nificantly across datasets (Figure 2B), and no single fixed weight achieves consistent performance across diverse problem types. Third, exhaustive hyperparameter search for optimal values is computationally prohibitive, requiring independent model training for each configuration with costs scaling linearly with search space size. These limitations demonstrate that static approaches can- not accommodate the inherent variability in problem complexity and dataset characteristics while remaining computationally feasible. More details can found Appendix A.I.
Challenge #2: Multi-objective Optimization. The hybrid reasoning problem involves three com- peting objectives: accuracy, efficiency, and decision calibration. These objectives exhibit interde- pendencies creating optimization challenges. High accuracy often requires longer reasoning chains, creating tension with efficiency goals. Decision calibration considerations may favor conservative mode selection strategies, potentially affecting both accuracy and efficiency outcomes (Song al.] 2024 Wilde et al. 2024 Albeaik et al. (024). Traditional single-weight approaches constrain optimization to narrow regions within the objective space, illustrated in Figure [ Fixed weight values restrict the search trajectory predetermined directions, preventing exploration of alternative regions with superior solutions. This spatial constraint confines optimization to limited cone-shaped regions (Figure (D), missing optimal solutions in unexplored objective space areas. The limita- tion becomes pronounced when objectives exhibit different gradient scales and convergence rates, causing premature convergence spatially constrained local optima rather than exploring the full solution landscape. More details can be found in Appendix A.2.
3 BACKGROUND
In this section, we present reinforcement learning and multi-objective pareto optimization.
Reinforcement Learning. GRPO (Shao et al. 2024) provides the foundation for training hy- brid reasoning models through reinforcement learning. In this framework, represents an in- put query or problem that requires the model to generate a response. Given control tokens (<short>, <think>), the hybrid reasoning model is parameterized policy no:
Te(c, alæ) = (c(c)) Ta(a|æ,c),
(1)
where C denotes the reasoning mode selection and a represents the generated response sequence. The sequence 0; = (ds,1,...,, form the response (Shao et al. 2024).
has length T; + 1, where dg C is the control token and
The standard GRPO objective treats all tokens uniformly through a single normalization factor:
G
Tk (o) - BBDaa (=))
JCRFO(!) = LE
(0) +
Te +1
k=
where (f) and (x,s(0) represent control and response token losses, respectively. This creates two imbalances: the control token overshadowed by Ti response tokens, and longer sequences
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suppress control gradients via the shared normalization Ty++T DeGRPO (Fang al. 2025) intro- duces separate normalization and weight parameter to balance mode selection against response accuracy, preventing mode collapse. More details can be found in Appendix A.3.
Multi-objective Pareto Optimization. Multi-objective optimization addresses problems with multiple competing objectives that cannot be simultaneously optimized. Rather than seeking single optimal solution, the goal of Pareto optimization is to find the of Pareto-optimal solutions:
P= fx" X: dr E X, f(x) ((=)),((x) + f(z"),
where -(r)= (2) . (x)) represents the objective vector, and z denotes Pareto dom- inance (Deb et al. 2023 Feng al. 202T). Traditional single-objective approaches using fixed weight combinations Σ, (x)((z) often fail to capture the full trade-off space, as they restrict opti- mization to predetermined directions in the objective space. Multi-objective methods enable explo- ration of diverse trade-offs by adapting weights dynamically based on the problem characteristics and solution quality. More details can found in Appendix A.4.
METHOD
In this section, we introduce the problem formulation and then present our solutions, including the MAGO framework, Pareto frontier maintenance, and end-to-end integration.
4.1 PROBLEM FORMULATION
In order to address the Challenge #1 mentioned in previous sections, we formulate hybrid reasoning training dynamic adaptive optimization problem:
Lk,(0) [3 (T<])<]] (-z)) ]
G
1 Tk
J(0) Bar,a
m(x) (0) +
(4)
G
k=
adaptive
where m(z) represents an adaptive weighting mechanism that adjusts based on input characteristics. Unlike existing approaches that rely on fixed hyperparameters, m(x) dynamically adapts balance competing training objectives without requiring manual tuning or hyperparameter search.
4.2 MULTI-OBIECTIVE ADAPTIVE GENERATION OPTIMIZATION
To realize the adaptive weighting mechanism m(x) introduced Equation 4
we propose MAGO
that dynamically balances competing objectives. The framework integrates three objectives:
mmAGo (z) = 31 Succurccy (z) + 82 Sefficiency (x) + B3 Scalibration (z),
(5)
where (31. 32, 33) are dynamically adapted weights that automatically balance the three competing objectives without manual tuning, and the three task-specific objectives are defined below:
Accuracy Objective. The accuracy objective Saccurccy (2)) measures the correctness of responses generated under different reasoning modes:
Saccumcy (x) ((c.a))To(ca w) [T(((a) yy"),
where I(() is the indicator function, y² is the ground-truth answer, and Φ(a) extracts the final answer from the response sequence a. This function parses the generated response to identify the con- cluding numerical or textual answer, enabling direct comparison with the ground truth regardless of reasoning mode length.
Efficiency Objective. The efficiency objective Sefliciency (x) captures the potential for computa- tional savings through appropriate mode selection by measuring the expected response efficiency:
lal
Sethiiency (x) = ((o,))mro(edle))
(7)
T T
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where |a] denotes the token length of the generated response sequence a, and Tmax represents the maximum allowed sequence length. The normalization term Tmaw measures the relative computa- tional cost, and subtracting from converts this an efficiency score where values approaching indicate highly efficient responses. This expectation is computed by sampling responses and calcu- lating their average normalized efficiency.
Calibration Objective. The decision calibration objective addresses critical challenge in hybrid reasoning: ensuring that the model's mode selection decisions are well-calibrated with its problem- solving capabilities. Specifically, when the model chooses the short reasoning mode, it should be confident that it can solve the problem correctly without extended reasoning. Conversely, when elaborate reasoning for
selects the think mode, this should indicate that the problem requires solution. Poor calibration occurs when the model overconfidently chooses short mode for difficult problems unnecessarily defaults think mode for simple problems could solve directly.
The decision calibration objective Scalibration (x) ensures that mode selection decisions align with the model's actual capability on the specific input by measuring decision calibration quality:
Sealibration
(z) ((c.a))mm(c.ola) mmodet e) I(o(a) =-"
(8)
To compute the model's confidence estimate, we first extract the raw confidence score from the final answer tokens. Let Lanswer denote the logits over the answer vocabulary at the final token position. The raw confidence score is defined as:
RawConf(a) = maxx(sfttaxx(Tmaa
which represents the model's highest probability assignment among all possible answer tokens. We then discretize this continuous confidence score into predefined intervals:
b Bin (RaaCConf(a)) = [RawConf(a)) x Nhins];
(10)
where Noins is the number of confidence bins (c.g., 5 10).
The model's calibrated confidence estimate is then computed using statistical calibration based on historical performance:
Pawoeel (correct x c) Historical b),
(11)
where HistoricalAAcuurayylcco returns the empirical accuracy for mode e in confidence bin 6:
HistoricalAAcuracy(e: ElEM(E,)) T(correct,)
(12)
(P((c,b) b)l
where H(c, b) represents the of historical samples with mode e and confidence bin b, and l[(orreect, indicates whether sample / produced the correct answer.
The historical statistics are maintained using exponential decay to prioritize recent performance:
HistoricalAccuracyy.
= A HistoricalAccuracy, (c, b) + (1 - A) [[(orrect(++),, (13)
where À E (0, is the decay factor. This approach leverages the model's intrinsic confidence distri- bution while correcting for systematic overconfidence underconfidence patterns through empiri- cal calibration, requiring no additional neural components while providing more reliable confidence estimates than raw token probabilities. More details can be found in Appendix A.5.
4.3 PARETO FRONTIER
The Pareto frontier mechanism provides the mathematical foundation for dynamic weight adap- tation in MAGO to address Challenge #2 mentioned in previous sections. We formalize the multi-objective optimization problem as maintaining an evolving set of weight configurations
). where each (3() = ",""",,,, o((1 represents distinct combination of weights for the three competing objectives (accuracy, efficiency, and calibration). By maintaining diverse set of non non-dominn non-dominated weight combinations, the Pareto optimization framework avoids the cone entrapment problem that constrains fixed-weight approaches to narrow regions of the objective space, enabling principled adaptation varying task requirements.
F. ( (23) (,.,,....
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At each iteration t, evaluate the performance of weight configurations using the training batch Be. For given weight vector ((5), we define the objective vector based on batch-level performance:
S,(3()) = B
Securracy (I)),
Selliciency (1),
Scalibration (x)
(14)
B
|
ZEB,
ZEB.
ZEB,
where [B:] denotes the batch size, each component represents the average performance of the corre- sponding objective over the current batch, evaluated under the policy mo trained with weight config- uration ((,).
The Pareto frontier is maintained as the set of non-dominated weight configurations:
= ( Ag( S S,(B()) > (((3))),
(15)
where S, represents the set of all evaluated weight configurations up iteration superscripts and (j) index different weight vectors in the frontier, and > denotes Pareto dominance relation.
To address objective correlations that lead to cone entrapment, introduce correlation-an weight selection mechanism. For each training batch B, at iteration t, we compute the empirical correlation matrix between the three objectives:
ELEB, (5 (x) (1) (z) (
C,(i.j]
(16)
VEEEB, (8()()) 14 ( CSEB,(SU)0) ()
where ((0()) denotes the i-th objective function (aceuracy, efficiency, calibration) evaluated on input z, and μ 1 E.EB. (() represents the batch mean objective i. This correlation structure guides the selection of weight combinations from the current frontier, ensuring that highly correlated objectives receive balanced attention while conflicting objectives maintain proper balance.
The weight selection process employs correlation correlation-daaptive scoring function (3) that evaluates the quality of each weight configuration the current Pareto frontier and penalizes configurations leading to high correlation between conflicting objectives:
(x(3) = Σ 3,5 Bcom Σ
C. 18, 3,1,
(17)
icj
where $(()) represents the moving average of the i-th objective performance over recent iterations, and Beorr > is hyperparameter controlling the penalty strength for correlated objectives. The first term rewards weight configurations that emphasize well-performing objectives, while the second term 13 3y1 penalizes unbalanced weight allocations when objectives and j are highly correlated, encouraging more uniform distribution across correlated objectives. The optimal weight vector for the current iteration is selected as:
8 arg max ( BEF,
(18)
To prevent premature convergence and ensure frontier diversity, we employ an exploration mecha- nism that generates new candidate solutions through guided perturbation:
nnew
3 6
(19)
where 33 is the currently selected optimal weight vector, is sampled uniformly from the constraint surface {d E R3; ||]]]2 1, D d, 0} to preserve weight normalization, and e; is scaled based on the current frontier diversity measure:
((Fi) E ED exp (- Diarges
(20)
where > 0 is the base exploration rate hyperparameter, Drarge > 0 is the target diversity threshold hyperparameter, and D(F:) , E .50)F Fuclidean distance among frontier solutions (Deb al. 2002
||3(0
B60112 measures the average pairwise

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Framework
Iterative
Query
Multi-Ojjective
Parato Frantler
Weight Confgurations
Objective Update
Updated Pollcy
Suewer (X)
Pi golll (l
Training
Deployment
Quary
Trained Model
Auto Deciiion
<Shert>
Token Generation
Output
Figure 3: MAGO framework efficiently implements end-to-end integration from training to deploy- ment and inference. (A) Training pipeline with multi-objective optimization and frontier updates. (B) Inference pipeline with learned adaptive mode selection.
The frontier update mechanism integrates newly evaluated candidate solutions and maintains non- dominance:
Fi+1= = (F U ( }) DiversityFilter(; -Tiv),
(21)
where DiversityFilter( Tdiv) ensures minimum pairwise distance Tdiv between frontier solutions to prevent clustering. In implementation, the number of frontier vectors [Fr grows gradually in early training and stabilizes around 20-25, remaining below the upper bound |F 30. When the limit reached, dominated or redundant vectors are pruned through cosine-similarity filtering to preserve representative diversity. More details about algorithm convergence can be found in Appendix A.6.
4.4 END-TO-END INTEGRATION
Training and Deployment. MAGO integrates into the hybrid reasoning training framework by replacing static weight parameters with dynamic multi-objective optimization (Figure AA). At each training iteration, the system selects an optimal weight vector 3 [ 1, 83] from the current
Pareto frontier Fe using correlation-aware selection (Eq. 18 The selected weights instantiate the adaptive weighting function MMGG (z; Bi) = Sj Saeewrcy (I) - 8 Satteiency (z) + B3 Sealibration (I),
which determines the control token weight for the current batch in the training objective:
Te
J(0) Ez,..
1 G
mMnco(2;;83)4
Σ
(+)
3Dxu [-a[[
Tk
(22)
In training (Figure BA), the framework iteratively performs policy updates using the selected weights, evaluates objective performance the current batch, and maintains the Pareto frontier through non-dominated sorting this closed-loop process. During deployment (Figure BB), the trained model automatically selects between <short> and <think> modes with zero inference overhead through learned decision-making, followed by standard token generation. More details can be found in Appendix A.7.
Training Process and Reward Design. We use minimal reward function in training which en- courages efficiency while maintaining correctness:
1.0,
if <short> and d(a) y", if = <think> and d(a) y",
r(a ya,
1.0
-
7
(23)
11.0,
if (()) tv'
where Φ(a) extracts the final answer and 7 < creates preference for efficient correct re- sponses (Fang et al. (225).. Additional details including the relative advantage computation and token-level loss formulations are provided in Appendix A.8.
Framework Summary. While MAGO framework introduces training overhead, this cost is amor- tized across millions of inference queries, yielding substantial operational savings. The framework

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enables automatic adaptation to changing model capabilities and data characteristics, providing prin- cipled trade-offs between accuracy, efficiency, and calibration without manual tuning, operating en- tirely during training with zero additional inference parameters computation.
EXPERIMENTS
5.1
IMPLEMENTATION DETAILS
Experimental Setup. We employ -eepSeee-----istil----e---- 1.55 (Guo et al. 2025) as the base model for hybrid reasoning training. To construct paired long-short response data for warm-up distil- lation, we leverage DeepSeek-R
PeepSeek-R1-67111 R1-671B (Guo et al. 2025) to generate extended reasoning chains and Qwen2.5-Mann Matth-1.5--Insiiii
(Yang et al., 2024a) for concise responses. The training corpus com- prises approximately 40K samples aggregated from DeepScaleR (Luo et al. (025). OpenR1 (Face) 2025). Open'Thoughts 14K (Team] 2025), and additional open-source mathematical reasoning cor- 114K
pora Jebali al. 2024 Langlais al. 2025). To demonstrate scalability, we conduct experiments
across Qwen2.5 series backbones of varying sizes (1.5B, 7B, 14B, 32B parameters) (Yang et al.)
2024a).
All experiments are conducted on 4 to 8 NVIDIA H100 GPUs depending on model size.
Training Configuration. Training involves supervised fine-tuning (1 epoch) followed by MAGO
reinforcement learning (600 steps), implemented using VeRL. (Jiang et al., 2025) and Mega- tron (Shoeybi et al. 2019). We optimize using AdamW (AbuKaraki et al. 2024) with learning rate 1x 10 batch size 128, weight decay 0.01, and momentum 3 = (0.9, 0.999). Context length is 16K during warm-up and 24K during reinforcement learning. MAGO hyperparameters. correlation penalty Bear = 0.1, exploration rate co = 0.05, diversity threshold Taiv = 0.2, maximum frontier size [F 30, calibration bins Noins 10, decay factor À = 0.95 for historical accuracy, and reward preference y 0.1 favoring correct short responses.
Evaluation Benchmarks and Baselines. We evaluate on six benchmarks: AIME 2024 (Ji al. 20255), Minerva Algebra (Hendrycks et al. (02T), MATH-500 (Lightman et al. (023). and GSM-
8K (Cobbe et al., 2021) for mathematical reasoning, CommonsenseQA (Talmor et al. 2019) and
MedQA-USMLE (Jin et al. (02T)) for cross-domain generalization. All benchmarks report Pass@r
accuracy and token usage per query. We compare against three baseline categories: (1) Base
LLMs: DeepSeek-R1- R1-.5 (Guo et al.] (025),
and Qwen2.5-Math- 1.5th
Instruct (Yang et al. 2024a); (2) Shortened CoT: Model Merging (Team et al. (025) with coeffi-
cients (0.5, 0.6, 0.7) and CoT-Valve (Ma et al. (0225) with a {4, 6, 8); (3) Hybrid Reasoning:
DeGRPO (Fang et al. 2025) with fixed a = 0.001, random router, and Qwen2.5-7B router (Ong
et al. 2024). Additional details provided in Appendix A.9.
5.2 RESULT
Multi-Objective Optimization Evaluation. We first evaluate MAGO on the 1.5B backbone. Across mathematical reasoning benchmarks, MAGO yields 2.2x to 3x token-efficiency gains and 0.6% to 9.4% relative accuracy improvements over heuristic baselines, with consistent improve- ments across all evaluated tasks. Table [ shows that MAGO achieves superior token efficiency (7,164 vs. 18,063+ baseline tokens on AIME) and competitive or superior accuracy most bench- marks, including AIME (0.2741) and MATH-500 (0.8247). while remaining close to the best scores on Minerva Algebra (0. 94.9483 vs. 0.9577) and GSM-8K (0.8469 vs. 0.8572). Unlike baseline meth- ods that require dataset-specific hyperparameter tuning and router-based approaches that struggle with complex datasets, MAGO's Pareto optimization framework automatically calibrates reason- ing strategies to achieve optimal efficiency-accuracy trade-offs, demonstrating the effectiveness of principled multi-objective optimization over heuristic approaches hybrid reasoning systems. To validate scalability, we further apply MAGO to larger backbones (7B, 14B, and 32B). As model capacity increases, Pass@1 I improves consistently across all benchmarks while average token usage per query decreases slightly, indicating that MAGO's Pareto optimization generalizes effectively to larger-seale models without increasing inference cost. More details can be found in Appendix 10
Mode Collapse in RL. Our Pareto optimization prevents mode collapse by maintaining balanced reasoning mode selection throughout training, avoiding the extreme preference for short responses that characterizes vanilla GRPO. Figure (A) illustrates the Mode Collapse issue in standard GRPO,
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Table I: Comparison of MAGO against baseline reasoning methods on mathematical benchmarks.
Models
Type
AIME 2024
Minerva Algebra
MATHI-500
GSMSK
Pasc 1
#Tokens
Puss@1
#Tokens
Pass G
#Tokens
Pase 1
#Tokens
DeepSeck-R1-1 -1-1BB
Gao et
2025
0.2800
18063
0,9577
3029
0.8608
5675
0.8347
1919
Q-1.5B Yong et
202-10
Base LLM
0.0200
1,300
0.7771
933
0.5168
855
0.7022
466
QMath-
Tang 2024g
0.1133
1128
586
0.7604
0.8572
Merging-0.5
Feam di ml. 2025
0.1383
8636
0.9292
834
0.7740
1524
0.8352
601
Merging-0.6
0.1733
10615
0.9321
1091
0.7900
3000
0.8381
747
CoT
Merging-0.7
S Mā et Bl.
Tean S1P KMPhh)
2025
Shart Co
0.2000
0.1667
15854
10692
0,8079
0.9398
1834
1903
0.7060
0.8108
3723
4347
0.7726
0.8458
120]
773
Cal-Valve
Mi
2125
0.1933
17245
0.9468
2656
0.80124
5167
0.7970
1009
CoT-Valve
MG M0
A125
0:2267
17722
0,9439
2965
0.8036
5820
0.8108
1396
Router Random Fang et
2025
Hybrid
0:1300
8093
0,9032
1736
0.7484
30366
0,8205
1086
Router Q-7B Ong ctam
0.1480
795
0.7781
0.8587
DeGRPO-Qwen-1.. (Fang al. 2025
Hybrid
0,2506
7262
0.9216
1228
0.8037
2645
0.8418
649
MAGO-Quen-11 (Cuis)
Pareto
0.2741
0.8247
0.8469
MAGO-Quen-788 (Ours)
0.2960
6890
0.9562
1102
0.8424
2426
0.8611
592
MAGO-Qwere 14B (Ours)
Pareto
0.3112
6724
0.9621
1041
0.8538
2368
0.8725
571
MAGO-Q 1.00--Qenn32BBB (Ours)
0.3254
6587
992
0.8652
0.8834
552
Gollaww Analya's
Accuney Think and Short Samsko
-ammpho with Senect Roparmwo
120
Training dynamics comparison between vanilla GRPO, DeGRPO, and MAGO frame- works. (A) Mode collapse analysis showing sample distribution over training steps. (B) Accuracy evolution for think and short reasoning modes during training. (C) Number of samples achieving correct responses for both reasoning modes.
Figure 4:
Table 2: Cross-domain generalization on CommonsenseQA.
Model
Accuracy (%) 1
Tokens Query J
Token Reduction
DeGRPO
73.1
312
CoT-Valve
73.8
298
1.05
MAGO (ours)
74.9
152
2.05
where the model develops an excessive preference for short outputs during training, with the num- ber of think samples dropping precipitously to near zero within 120 training steps. In contrast, the proposed framework demonstrates significantly more stable training dynamics, maintaining a bal- anced distribution between think and short samples throughout the process. The vanilla GRPO's rapid collapse to predominantly short-mode usage (below 10 think samples) indicates a failure to properly balance competing objectives of accuracy and efficiency. Our Pareto-based optimization prevents this catastrophic collapse by maintaining diverse weight configurations that ensure neither reasoning mode abandoned, enabling adaptive strategy selection based on query complexity rather than converging to suboptimal modes.
(B) reveals that our approach achieves more balanced training dynamics across 600 steps, with both reasoning modes converging smoothly after approx- imately 300 steps. While DeGRPO exhibits high volatility with fluctuations in accuracy for both modes, the proposed method demonstrates stable convergence patterns with reduced variance. The think mode maintains consistent performance around 0.6-0.7 accuracy while the short mode gradu- ally improves from 0.4 0.5, contrasting sharply with DeGRPO's 's chaotic dynamics where accuracy fluctuates wildly between 0.3 and 0.8. Figure (C) demonstrates superior sample efficiency, show- ing that the model quickly learns to activate short mode while ensuring correctness. The intersection point between think and short correct responses occurs later in training (around step 400), indicating more thorough exploration of reasoning mode trade-offs before settling on optimal strategies.
The U-Shape Learning Curve. Figure 4
Cross-Domain Generalization. To evaluate the generalization ability of MAGO beyond math- ematical reasoning, we perform additional experiments on CommonsenseQA. a benchmark that assesses everyday reasoning and contextual understanding. The objective is examine whether

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Published as a conference paper at ICLR 2026
proposed Pareto-base basto- adaptive optimization, trained only on mathematical reasoning data, can effectively transfer to a different reasoning domain without further fine-tume teeint.nng.... The same inference settings described in Section 5.1 adopted for all methods. Representative hybrid reasoning base- lines, including DeGRPO and Call-Valve are used for comparison. The experimental results are presented in Table 2 All results averaged over three random seeds to ensure stability. MAGO achieves 74.9% accuracy, outperforming DeGRPO and CoT-Valve by 1.8% and 1.1%, respectively, while reducing the average number of generated tokens from 312 to 152, corresponding to 2.05 improvement in efficiency. These findings demonstrate that MAGO's Pareto-based adaptive opti- mization generalizes effectively across reasoning domains and maintains a stable balance between accuracy and computational efficiency. We also evaluate MAGO on MedQA-USMLI (Jin et al.) 2021, medical question answering benchmark, where MAGO achieves over 2.0x efficiency im- provement while maintaining competitive accuracy. More details can be found in Appendix A.18.
Computational Complexity. We analyze the computational and memory complexity introduced by the multi-objective optimization process. Let |B] denote the batch size. M = 3 the number of objectives, and [F&] the number of maintained frontier vectors, with an upper bound [Fmaxl = 30. For space consideration, more details are provided in Appendix A.19.
RELATED WORK
Reasoning (Hybrid and Efficient). Recent hybrid reasoning advances combine multiple paradigms for efficiency. Chain-of-thougle and program-aided reasoning integrate natural language with code (Gao al., 2022; Ranaldi et al. 2024), while self-refinement methods iteratively improve chains (Madaan et al. 2023 Ji et al.) (025a). Tree-of- ofee-of- structures reasoning as search (Yao 20233|Pander et al. 2025). adaptive frameworks select strategies by complexity (Zhou al., Tu al. 2025), and multi-path reasoning aggregates diverse chains (Zhu et al. 2024: Zhang 2024c). Compression (Omri et al. 2025 Han et al., 2024) and selective generation (Jo al., Yang et al., 2024b) reduce tokens while maintaining accuracy. However, these lack principled frameworks for jointly optimizing strategy selection and efficiency across diverse distributions.
al.
2023
et al.
2022
Effective Reasoning (Single Methods). Single-paradigm optimizations enhance reasoning with- out hybridization. Prompt compression preserves semanties with 20x ratios (Jiang et al.; 2023), knowledge distillation transfers capabilities to smaller models (Shridhar et al.) 2023), and specula- tive decoding accelerates inference (Leviathan et al.) 2022). Structured pruning removes redundant steps (Tao et al. 2023 Men al. 2024), early-exit uses confidence thresholds (Tang et al. 2023 Xu et 2025), leven optimization skips steps (Lee et al. [024), and cache-based approaches reuse patterns (Yang et al., (025a(b) b). These optimize singular objectives, missing opportunities.
Multi-Objective Optimization (MOO). MOO in language models balances competing goals. Pareto-optimal solutions identify accuracy-efficiency trade-off's (Mukherjee et al., [0244] Huang
et al. 2024), weighted scalarization combines objectives (Yang et al.] [202cc] Li & Ma 2018).
and RL optimizes multiple rewards (Zhang al.) [2024b). Constraint-
methods ensure
safety (Zhang et al. [2244a] Peng et al.] 2025). dynamic adjustment adapts priorities (Low & Kumar
2024 Krishna Vali y, preference learning captures values (Dai et al. 2023 Shen et al. 2025).
and evolutionary algorithms handle trade-offs (Bai et al., 2023 Li et al. 2024). However, MO0 in
inference mode selection remains underexplored, missing context-aware optimization opportuitics..........
7 CONCLUSION
We present MAGO, multi-objective adaptive generation optimization framework that integrates Pareto frontier maintenance with correlation-aware weight selection for hybrid reasoning in LLMs. Our framework combines three competing objectives (accuracy, efficiency, and calibration) through dynamic weight adaptation using Pareto frontier maintenance and correlation-awe aware selection. This principled approach eliminates hyperparameter tuning while preventing the mode collapse observed in existing reinforcement learning methods. Experiments show that MAGO delivers 2.2x to 3x token-efficiency gains along with 0.6% to 9.4% relative accuracy improvements over heuristic meth- ods on mathematical reasoning tasks. Cross-domain evaluation on CommonsenseQA and MedQA further confirms the framework's 's transferability beyond mathematics without additional fine-tuning.
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Published as a conference paper at ICLR 2026
ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics. Our research focuses on developing multi-objective optimization for adaptive reasoning in large language models. We identify the following ethical considerations:
Privacy. No personally identifiable information is collected or processed.
Environmental Impact. We report detailed computational requirements in Appendix A.9.
Potential Harms. Our optimization framework could potentially be applied harmful applica- tions. We emphasize the importance of responsible deployment and adherence to Al safety guide- lines.
REPRODUCIBILITY STATEMENT
To facilitate reproduction of results:
Code. Complete implementation including training scripts and evaluation code will be released upon paper acceptance. For review purposes, we provide pseudocode in Appendix.
Experimental Details. Hyperparameters and experimental setup are fully specified Appendix A.9. Hardware specifications are provided in Appendix A.9.
Data. We use publicly available datasets: AIME 2024, Minerva Algebra, MATH-500, and GSM- 8K for mathematical reasoning evaluation; CommonsenseQA and MedQA-USMILE for cross- domain evaluation; DeepScaleR, OpenRI, and OpenThoughts-I 14K for training.
ACKNOWLEDGEMENT
This research was funded by Zhejiang Province Philosophy and Social Sciences Planning Project 250225X)) and Zhejiang Province Sino-Foreign Cooperative Education Research Center, Zhejiang Provincial Education Science Planning Project (No.2025SCG21)
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A APPENDIX
All appendices are provided in the supplementary text.
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Reviews:
[{"review_id": "WeFMeOgjhs", "reviewer": "Reviewer_VZpK", "summary": "This paper introduces the Multi-Objective Adaptive Generation Optimization (MAGO) framework, designed to address inefficiencies in hybrid reasoning approaches for large language models (LLMs). ​Current methods often rely on static hyperparameters and single-objective optimization, which fail to adapt to varying task complexities and result in suboptimal trade-offs between accuracy, efficiency, and calibration. ​MAGO leverages multi-objective optimization and dynamic adaptive weighting to optimize these three competing objectives simultaneously. ​By maintaining a Pareto frontier and employing correlation-aware optimization, MAGO explores the full trade-off space, avoiding the limitations of fixed-weight approaches. ​The framework eliminates the need for manual hyperparameter tuning and achieves principled decision-making across diverse problem complexities. ​Experimental results on mathematical reasoning benchmarks (AIME, Minerva Algebra, MATH-500, GSM-8K) demonstrate that MAGO improves computational efficiency by 2.2x to 3x while enhancing accuracy by 2.6% to 9.4%. ​The paper also highlights MAGO's ability to prevent mode collapse during training and achieve stable optimization dynamics.", "questions": "Line 216-218: Why do we take the logits corresponding to the final answer token to compute the raw confidence score instead of all the tokens in the generated answer.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper addresses a critical limitation in hybrid reasoning systems by reformulating the problem as a multi-objective optimization task, which is a significant advancement over static hyperparameter-based methods.\n\n- The introduction of dynamic weight adaptation and Pareto frontier maintenance is innovative and eliminates the need for manual hyperparameter tuning, making the framework more adaptable to diverse problem complexities.\n\n- The authors provide extensive experimental results on multiple mathematical reasoning benchmarks, demonstrating significant improvements in both accuracy and efficiency compared to existing methods.\n\n- MAGO effectively prevents mode collapse during training, ensuring balanced reasoning mode selection and stable optimization dynamics.", "weaknesses": "- The framework is primarily evaluated on mathematical reasoning tasks, which may limit its generalizability to other domains. ​Broader validation across diverse reasoning tasks is necessary to establish its applicability.\n\n- While the training overhead is claimed to be amortized over inference, the paper acknowledges that this may pose challenges for resource-constrained scenarios. ​Further analysis of the trade-offs between training cost and inference efficiency would strengthen the paper.\n\n- The proposed framework involves intricate mechanisms such as Pareto frontier maintenance, correlation-aware weight selection, and dynamic weight adaptation. ​This complexity may hinder practical adoption and require significant expertise for implementation.\n\n- The paper does not provide concrete examples or case studies of how MAGO could be applied in real-world scenarios, which would help demonstrate its practical utility.", "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": "48ir84WGz1", "reviewer": "Reviewer_MZa3", "summary": "This paper proposes MAGO (Multi-objective Adaptive Generation Optimization), a framework for training hybrid reasoning systems in LLMs that dynamically choose between short (direct) and think (chain-of-thought) modes. The key innovation lies in reformulating the hybrid reasoning optimization as a multi-objective optimization problem balancing three competing objectives: accuracy, efficiency, and decision calibration. Rather than using fixed hyperparameters as in existing methods, MAGO employs Pareto frontier maintenance with correlation-aware weight selection to dynamically adapt optimization weights during training. Evaluation on mathematical reasoning benchmarks (AIME, Minerva Algebra, MATH-500, GSM-8K) shows efficiency improvements of 2.2x-3x while simultaneously improving accuracy by 2.6%-9.4% compared to baseline methods like Thinkless and CoT-Valve.", "questions": "1. Why is calibration defined as the difference between model confidence and empirical accuracy rather than other common calibration metrics (expected calibration error, Brier score)?\n\n2. Can you evaluate on non-mathematical reasoning tasks (e.g., commonsense reasoning, multi-hop QA, code generation)? The current scope limits the paper's impact.\n\n3. Visualize how the Pareto frontier evolves during training. How does frontier size grow? Do solutions converge or continue diversifying? When does the optimal weight selection stabilize?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper identifies a genuine limitation in existing hybrid reasoning systems—the dependence on static hyperparameters that vary significantly across datasets. The reformulation as a multi-objective optimization problem is novel for this domain, and the motivation clearly demonstrates why fixed-weight approaches fail (Figure 2)\n\n2. The Pareto frontier maintenance approach is mathematically well-motivated. The use of correlation-aware weight selection (Equation 17) to handle interdependencies between objectives addresses a real challenge in multi-objective optimization. \n\n3. The paper evaluates on multiple mathematical reasoning benchmarks and includes several comparison baselines (router-based methods, model merging, CoT-Valve)", "weaknesses": "1. While accuracy and efficiency are straightforward objectives, the decision calibration objective (Equation 8) lacks rigorous justification. The approach relies on binned historical accuracy statistics, but several concerns arise: (1) How are the bins (Equation 10) initialized during early training when insufficient historical data exists? (2) The exponential decay (Equation 13) may create staleness issues when task distributions shift. (3) The paper doesn't analyze how this objective theoretically prevents miscalibration or what formal guarantees exist. \n\n2. DeepSeek-R1-Distill-Qwen-1.5B. This is a relatively small model, and generalization to larger models (7B, 13B, or larger) remains unclear. Does the Pareto frontier maintenance scale computationally with model size?\n\n3. The router baselines (Router Random, Router Q-7B) seem surprisingly weak—Router Q-7B achieves only 14.8% on AIME while Thinkless achieves 25%. This brings up there might be some possible implementation or configuration issues.\n\n4. The paper mentions diversity filtering (Equation 21) but provides insufficient detail. How quickly does the frontier grow? What happens when |F_t| approaches |F_max|? The exploration mechanism (Equation 19-20) could lead to unbounded frontier growth early in training.", "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": "l747wfY9vG", "reviewer": "Reviewer_7KZE", "summary": "This paper targets hybrid thinking, driven by the commonly known fact that reasoning-enabled LLMs often waste compute by using long CoT on easy inputs. With the emergence of deep reasoning models such as DeepSeek-R1, hybrid thinking is a trending topic with prevalent work such as Thinkless. Existing hybrid reasoning methods rely on fixed hyperparameters (e.g., a single controller weight) and single-objective heuristics, which lock the system into narrow, suboptimal regions of the accuracy–efficiency trade-off. \n\nThis paper takes a different approach along this line. The authors propose to reframe hybrid reasoning as multi-objective optimization over three goals: accuracy, efficiency, and decision calibration. They call the framework MAGO. In short, MAGO introduces a dynamic weighting mechanism over those three objectives, learned via Pareto frontier maintenance plus correlation-aware weight selection. Experiments on AIME-24, Minerva Algebra, MATH-500, GSM8K show that MAGO brings good improvements by 2.6% - 9.4%.", "questions": "- ``Q1``: Why choose answer-token max-prob as confidence instead of sequence-level confidence or verifier-based scores?\n- ``Q2``: Does MAGO generalise to other domains rather than math reasoning?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- ``S1``:  The paper is very well motivated. The authors use a section (Sect. 2) with pilot experiments to demonstrate their motivation. In particular, it shows that fixed weights cause mode imbalance and dataset-specific sensitivity (Fig. 2A–B), and that scalarization constrains search to a cone-shaped region of objective space (Fig. 2C–D), missing better trade-offs. This strongly motivates the proposed methodology.\n\n- ``S2``: The proposed MAGO makes sense. Experiments on several math reasoning benchmarks show both token savings and accuracy gains.", "weaknesses": "- ``W1``: The scope of the evaluated benchmark is a bit limited and narrow (math). Additional experiments on SuperGPQA and CommonsenseQA would strengthen the contribution by showing the generalization of MAGO in extended domains.\n\n- ``W2``: In Tab. 1, the authors are suggested to include the reference for each method.\n\n- ``W3``: Computational overhead is one of my concerns. I notice that the authors have admitted the limitations of extensive overhead by Pareto optimization in the last section. It would be great if the authors could show some computational complexity analysis.", "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": "BshWqCpSFD", "reviewer": "Reviewer_XguT", "summary": "The paper proposes MAGO, a multi-objective adaptive generation optimization framework for hybrid LLM reasoning that learns when to use short versus think modes. MAGO formulates training as a three-objective problem—accuracy, efficiency, and calibration—combining a Pareto frontier over weight vectors with a correlation-aware selection rule and exploration to avoid “cone entrapment” inherent in fixed scalarizations. The adaptive weight mMAGO(x) scales the control-token loss in a GRPO-style objective, aiming to stabilize mode selection and prevent collapse. On math benchmarks (AIME 2024, Minerva Algebra, MATH-500, GSM8K), the paper reports 2.2–3× token savings and small-to-moderate accuracy gains over several baselines.", "questions": "Could you add a quantitative DeGRPO baseline in Table 1 using your training setup, given that MAGO is positioned as an advancement over GRPO/DeGRPO?\n\nExpectations in Eqs. (6)–(8): During training, how many (c,a) samples per x are used to estimate Saccuracy, Sefficiency, and Scalibration? Are these single-sample estimates, and if so, how do you control variance for the correlation matrix Ct (Eq. 16)?\n\nSince the contributions mention logical inference and general problem solving, do you have held-out non-math evaluations (even small-scale) using the same trained model to substantiate cross-domain claims? If not, please limit claims or provide such experiments.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "MAGO is described with explicit equations: adaptive objective (Eq. 5), calibration via confidence binning and exponentially decayed historical accuracies (Eqs. 9–13), Pareto frontier maintenance and selection (Eqs. 14–21), and integration into the GRPO-style loss where mMAGO(x) scales control-token terms (Eq. 22).\n\nFigures 4A–C provide interpretable evidence that MAGO mitigates mode collapse and reduces variance relative to vanilla GRPO and DeGRPO during RL, with later, more stable crossovers between modes.", "weaknesses": "The text states that MAGO achieves the highest accuracy on AIME, Minerva Algebra, and MATH-500, but Table 1 shows DeepSeek-R1-1.5B surpasses MAGO on Minerva Algebra (0.9577 vs 0.9483), and QMath-1.5B surpasses MAGO on GSM8K (0.8572 vs 0.8469). The abstract and Section 5 claim accuracy improvements of 2.6%–9.4%; the largest improvements visible in Table 1 over strong baselines appear smaller on some tasks (e.g., GSM8K +0.5 points over Thinkless). These discrepancies need reconciliation.\n\nDeGRPO is analyzed qualitatively in Figure 4 but not reported as a quantitative baseline in Table 1. Router baselines include a 7B router, while MAGO uses a 1.5B single model; compute-normalized comparisons (e.g., wall-time, FLOPs/token including routing overheads) are not provided. It is unclear whether all baselines share the same SFT data, RL steps, and inference constraints.\n\nThe contributions highlight logical inference and general problem solving, but experiments focus on math-only datasets. The conclusion acknowledges this limitation; however, the earlier claims of breadth may mislead.\n\nOverall, I think the most important thing is the proposed method still lack of the novelty and the performance is mainly tested on math problem which increases the concerns that whether the proposed method can be generalized to other domains.", "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)