Reasoning-Driven Multimodal LLM for Domain Generalization
Authors: Zhipeng Xu
Zilong Wang
XINYANG JIANG
Dongsheng Li
De Cheng
Nannan Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Machine Learning (ML) -> ML: Transfer
Domain Adaptation
Multi-Task Learning
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: This paper addresses the domain generalization (DG) problem in deep learning. 
While most DG methods focus on enforcing visual feature invariance, we leverage the reasoning capability of multimodal large language models (MLLMs) and explore the potential of constructing reasoning chains that derives image categories to achieve more robust predictions under domain shift.
To this end, we systematically study the role of reasoning in DG using DomainBed-Reasoning, a newly constructed extension of DomainBed dataset, in which each sample is paired with class-relevant reasoning chains.
Our analysis reveals two key challenges: (i) fine-tuning MLLMs with reasoning chains for classification is more challenging than direct label supervision, since the model must optimize complex reasoning sequences before label prediction; and (ii) mismatches in reasoning patterns between supervision signals and fine-tuned MLLMs lead to a trade-off between semantic richness (informative but harder to optimize) and optimization efficiency (easier to optimize but less informative).
To address these issues, we propose RD-MLDG (Reasoning-Driven Multimodal LLM for Domain Generalization), a framework with two components: (i) MTCT (Multi-Task Cross-Training), which introduces an additional direct classification pathway to guide reasoning supervision; and (ii) SARR (Self-Aligned Reasoning Regularization), which preserves the semantic richness of reasoning chains while mitigating reasoning-pattern mismatches via iterative self-labeling.
Experiments on standard DomainBed datasets (PACS, VLCS, OfficeHome, TerraIncognita) demonstrate that RD-MLDG achieves state-of-the-art performances, highlighting reasoning as a promising complementary signal for robust out-of-domain generalization.

Published as a conference paper at ICLR 2026
REASONING-DRIVEN MULTIMODAL LLMs FOR DOMAIN GENERALIZATION
Zhipeng Xul. Zilong Wang**, Xinyang Jiang"', Dongsheng Li,, De Cheng! Nannan Wang' Xidian University. "Microsoft Research Asia
National Engineering Laboratory for Integrated Aero-Space-Groume Ground- Ocean Big Data Application xu.zhipengēstu xidian. edu. en, {nnwang, (eceenng)xxiiiooo edu.eddu.c. en (xangzilcng, xinyangjiang, dengsla]emmccroooft.
ABSTRACT
This paper addresses the domain generalization (DG) problem in deep learning. While most DG methods focus on enforcing visual feature invariance, we leverage the reasoning capability of multimodal large language models (MLLMs) and ex- plore the potential of constructing reasoning chains that derives image categories achieve
robust predictions under domain shift. To this end, we system- atically study the role of reasoning in DG using DomainBed-Reasonee Reasoning, newly constructed extension of DomainBed dataset, in which each sample paired with class-relevant reasoning chains. Our analysis reveals two key challenges: (i) fine- tuning MILMs with reasoning chains for classification is more challenging than direct label supervision, since the model must optimize complex reasoning se- quences before label prediction; and (ii) mismatches in reasoning patterns between supervision signals and fine-tuned MILMs lead to trade-off between semantic richness (informative but harder to optimize) and optimization efficiency (casier optimize but less informative). To address these issues, propose RD-MLDG (Reasoning-Drivve Multimodal LLM for Domain Generalization), a framework with two components: (i) MTCT (Multi-Task Cross-Trainnngg which introduces an additional direct classification pathway to guide reasoning supervision; and (ii) SARR (Self-Aligned Reasoning Regularization), which preserves the seman- tic richness of reasoning chains while mitigating reasoning- patteer mismatches via iterative self-labeling. Experiments on standard DomainBed datasets (PACS, VLCS, OfficeHome, TerraInc) demonstrate that RD-MLDG achieves state-of -htthe- art performances, highlighting reasoning as a promising complementary signal for robust out-of-domail generalization.
INTRODUCTION
In real-world scenarios, deep learning models are often required to generalize reliably to unseen distributions. However, due to domain shift, their performance often degrades substantially when deployed in new environments. To address this issue, Domain Generalization (DG) has emerged as a key research area, aiming to learn representations and prediction functions that transfer well to unseen domains using only source-domain data.
Existing DG approaches generally fall into four categories: invariant representation learning, data augmentation, regularization, and meta-learning. While effective, these methods primarily focus on feature-level invariance and often fail to capture higher-level cross-domain commonalities, limiting their ability to generalize in complex scenarios.
Recently, multi-modal large language models (MLLMs) have made significant progress and demon- strate strong reasoning capabilities. This capability offers new opportunity for DG: rather than
De Cheng (dcheng @ kidian.edu.cm * Equal contribution: Zhipeng Xu, Zilong Wang and Xinyang Jiang contributed cqually to this work. Work done during an intersship Microsoft Research Asia.
E Corresponding author:

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Published as a conference paper at ICLR 2026
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Figure I: Illustrative examples of the printer in OfficeHome (Art Painting, Clipart, Product, Real World), Although the visual appearance differs substantially, the highlighted green segments of the reasoning chains remain highly consistent, capturing class-relevant cues that are likely to generalize well across domains.
relying solely on invariant feature representations, one could exploit reasoning chains to bridge do- main gaps and achieve more robust generalization. By producing structured, class-relevant reasoning chains, we can leverage MLLMs to explicitly decompose a classification task into interpretable and domain invariant steps with strong transferability across domains (Fig. [.. To systematically study the role of reasoning in DG, we leverage GPT-4o to construct DomainBed- Reasoning, an extension of the widely used DomainBed benchmark, where each sample is paired with class-relevant reason- ing chains. Specifically, we investigate whether an MILMM fine-tuned with DomainBed- Reasonnig can leverage generated reasoning chains to improve domain-generalized predictions.
Bomainned-Reassoinng
However, our empirical study shows that simply applying reasoning-chait supervision to classifica- tion tasks remains challenging. Our analysis reveals two key issues. First, reasoning-chain super- vision is inherently difficult to optimize. In contrast to direct label prediction, the model must first fit complex reasoning chains before producing the final label. Second, reasoning chains generated by commercial models and those produced by fine-tuned MLLMS often exhibit distinct reasoning patterns. Commercial-mode chains usually contain detailed contextual information that can assist classification, such as descriptions of background, object attributes, or scene conditions. While these chains are semantically rich and potentially informative, they are difficult for the model optimize. In contrast, self- self-generated chains from the fine-tuned MLLM tend to be simpler in logic and more fabel-focused, which makes them easier to fit but less informative for classification. These observa- tions indicate that straightforward reasoning supervision is insufficient and instead call for a more principled integration strategy.
This motivates us to design RD-MLDG (Reasoning-Drisee Multimodal LLM for Domain General- ization), the first DG framework that explicitly incorporates class-relevant reasoning chains to en- hance out-of-domain performance. RD-MLDG consists of two key components: (i) MTCT (Multi- Task Cross Fross-Training), which jointly optimizes a direct classification pathway and a reasoning- augmented pathway. The direct pathway focuses standard label prediction, offering a simple and stable training signal. This signal guides the reasoning pathway by preventing from fitting to overly complex or inconsistent reasoning chains, and instead keeps the supervision focused on the core classification objective; and (ii) Self-Aligned Reasoning Regularization (SARR), which retains informative reasoning chains while mitigating reasoning- rasoning-pattern mismatches through self- gefereeedd supervision. Together, these components enable RD-MLDG to provide a principled way to integrate reasoning into DG for robust generalization.
In summary, our work makes the following contributions:
We construct DomainBed- BennainBee-Reasoninng Reasoning, an extension of the DomainBed dataset where each sample is paired with class-relevant reasoning chains, enabling systematic evaluation of both classification accuracy and reasoning consistency under domain shift.
We propose RD-MLDG, the first DG framework that explicitly integrates reasoning by address- ing two key challenges: (i) the difficulty of optimizing reasoning-chain supervision, and (ii) mis- matches in reasoning patterns between supervision signals and fine-tuned MLLMs.
We empirically show that RD-MLDG achieves state-of-the thr performance on standard Do- mainBed benchmarks (PACS, VLCS, OfficeHome, Terralnc), demonstrating the effectiveness of reasoning-basee approaches for DG.
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Question prompt (TerraInc)
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Figure 2:
Overview of the DomainBed-F Raasonnng construction pipeline. GPT-4o generates multi-stage reasoning chains (<SUMMARY>, <CAPTION>; <REASON ING>, <REPLECTION: <CONCLUS with- out access to ground-truth labels. Multiple candidates are sampled and filtered through rejection sampling to obtain coherent reasoning chains, which form the foundation for analyzing reasoning challenges in DG.
2 RELATED WORK
Domain generalization (DG) has been extensively studied through approaches such as invariant
representation learning (e.g. IRM (Arjovsky al. 2019), CORAL (Sun & Saenko, (016)) data augmentation (e.g., MixStyle (Zhou et al. 2021). RandConv (Choi et (203)). and learning- based strategies including meta-learning (e.g., MLDG (Li et al.) 2018a)), adversarial training (e.g.. PAPT (Xu et al.j 2025)). ), and flat-minima regularization (e.g., SWAD (Cha et al. 2021)). Despite their success, these methods primarily operate at the representation level. More recently, CLIPP- powered models have advanced DG by providing rich multimodal representations and strong cross- domain transfer. Recent efforts mainly focus on (i) prompt optimization (c.g., CoOp (Zhou et al, 20226), CoCoOp (Zhou et al. 2022a), KgCoOp ( Yao et al. 2023), MaPLe (Khattak et al.) 2023), DPR (Cheng et al. PADG (Cheng et al. 2026), SECA (He et al. (025b)) and (ii) leveraging
CLIP a backbone for robustness (e.g.. StyLIP (Bose al. 2024). PromptStyler (Cho al. 2023
Other works (He et al. 2025a| Cheng et al. 2025 Wang et al. (025b]aa ap have also made meaning- ful progress in this direction. VOLDOGER (Choi et al.) 2025) focuses on DG data annotation for
vision-language tasks, such as image captioning, visual question answering (VQA), and visual en- tailment (VE). It alleviates the burden of manual annotation by extending LLM-based annotation techniques and provides a reliable benchmark for evaluating DG in these tasks. While effective, these methods remain confined to feature-level invariance and overlook the reasoning processes that underlie generalizable decision-makinn complementary signal for DG. Specifically, we aim to encourage process-level invariance through class-relevant reasoning chains, which serve as complementary signals to feature-level invariance for improving out-of-domain generalization.
Our work addresses this gap by introducing reasoning as
Multimodal large language models (MLLMs) have recently emerged dominant paradigm for vasion-language understanding, achieving strong reasoning (Yang et al., 2025) and generalization across modalities (e.g., Flamingo (Alayrac al. 2022). GP (OpenAl) 2023), Gemini Team let al. 2023)). ). Despite these advances, recent studies show that MLLMS underperform their vi- sion encoders (e.g., CLIP (Radford et al. 2021b), EVA-CLIP (Sun et al. (023)) on fundamental classification benchmarks such as Image Flowers 102, and StanfordCars Cars (Zhang et al. 2024). Existing improvements remain rooted label-level supervision and fail to capture the explicit rea- soning processes required for robust generalization under distribution shift. Our work addresses this gap by leveraging class-relevant reasoning chains and introducing effective training strategies to make reasoning supervision both optimizable and informative, thereby bridging the divide between feature-level alignment and process-level reasoning in MLLMs.
3 DOMAINBED-REASONE
A central challenge in studying reasoning for domain generalization (DG) is the lack of benchmarks that explicitly capture reasoning processes. Existing DG datasets provide only input-label pairs, which limits evaluation to feature-level invariance and makes difficult to assess how reasoning contributes to generalization under domain shift.
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Illustration of Challenge (see Sec. 4.1 (A) Classification token probabilities target-domain data under zero-shot and SFT, with without reasoning: (B) Probability distributions of all tokens on source- domain training data under zero-shot and SFT; (C) Training dynamics: (I-II) classification token probabilities on source-domain training data after SFT; (III) loss curves comparing reasoning- -bsed and no-thinking SFT.
Figure 3:
To address this gap, we construct BenaainBed-Reasoninng an extension of the widely used Do- mainBed benchmark enriched with structured reasoning chains. Specifically, for each image-label pair across four standard DG datasets (PACS, VL.CS, OfficeHome, and Terralne), we employ GPT-4o to generate multi-stage reasoning chains consisting of a <SUMMARY> <CAPTION>, <REASONING>, <REFLECTION>, and <CONCLUSION>, Compared to the four-step format in CLT (Xu et al. 2024), we introduce an additional reflection stage that prompts the model to self-check intermediate reasoning, which empirically reduces invalid generations and improves stability. Importantly, the ground-truth label is withheld during generation, encouraging reasoning that is grounded in visual evidence rather than rote restatement.
Generating reasoning chains without access to the ground-truth label non-trivial and may result in incomplete or inconsistent outputs. To improve quality, we adopt rejection sampling strategy: for each instance, multiple candidate chains are generated, and only those that contain all required com- ponents and produce a coherent conclusion retained. An overview of this construction pipeline is illustrated in Fig. 2 The resulting dataset pairs classification labels with independently generated reasoning chains, enabling evaluation under domain shift. DomainBed-Reasee Reasoning thus provides standardized extension of DomainBed for studying reasoning-based domain generalization, serving foundation to analyze how reasoning supervision affects out-of-domain performance (Sec.
4 CHALLENGES OF REASONING CHAIN IN DG
Bodaaonnd- provides basis to analyze the role of reasoning in DG, and study indi- cates that using reasoning-chee eohonnng-chain supervision alone for classification is challenging, especially under supervised fine-tuning. We identify two main issues: (i) the difficulty of optimizing reasoning-chain supervision under domain shift (Sec. 4.1.. and (ii) mismatches in reasoning patterns between su- pervision signals and fine-tuned MILMS (Sec. 4.2). These challenges highlight the gap between the potential of reasoning and its current limitations in DG, motivating the RD-MLDG framework introduced in the next section.
4.1 OPTIMIZATION GAP IN REASONING-CIAIN SUPERVISION
Research Question. Does directly distilling class-relevant reasoning chains into MLLMS improve domain generalization classification?
Experiment Setting. As a case study, we conduct experiments on the TerraInc dataset (Beery et al,, 2018], which contains 10 categories of wild animals across diverse camera locations, with domain shifts arising from viewpoint and background variations. We use Intern VL.38B (Chen et al. 2024) as the base model and compare two training regimes: (i) no-thinking (Li et al. 2025), where the model takes the image and input question (e.g.. "What type of object is in this photo? Choose from the following options:") and directly predicts the label, and (ii) reasoning-chan chain supervision using DomainBed Reaedonig,,,,g where the same inputs are paired with multi- stage reasoning chains. Both regimes are evaluated under zero-shot prompting and supervised fine-tum tuning (SFT). To assess their behavior, we analyze token probability distributions and training dynamics (Fig. 3
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Illustration of Challenge 2 (see Sec. 4.2) (A) Token probability distributions on source domain training data under zero-shot and fine-tuned settings: GPT-40 reasoning (A.I, A.II) vs InterVV--8B reasoning (A.III, A.IV). (B) Top-15 tokens with the largest entropy reduction during optimization, highlighting differences between GPT-4o (B.I) and IntemVI.-8P (B.II). (C) Qualitative examples corresponding to (B.I) and (B.II, with tokens receiving the strongest optimization gains highlighted in red.
Figure 4:
Observation. First, in the zero-shot setting, adding reasoning improves classification performance under domain shift: Intern shows a +43.28% 28p increase in ground-trute token probability compared to no-thinking predictions (Fig. 33 I vs III). Second, this benefit does not persist under supervision yields slightly lower accuracy (-0.93%p) than direct label supervision (Fig. A3 II vs IV). Third, token-level distributions further highlight this optimization gap. No-thinking SFT shifts a large proportion of tokens from low to high prob- ability (+43,3 (+43.38%)) (3% Fig. 3 B.. I vs II), whereas reasoning-chain SFT produces only a marginal gain (+1888%)) (Fig. 3 B. III vs IV). Training dynamics show a similar trend: no-thinking SET fits classification tokens more effectively (92.23% 86.33% above the 75% confidence threshold), while reasoning- based SFT retains more www-pobbbbiltyy samples (7.59% vs 2.05% below 25%) (Fig. 3 C.I-II). Finally, loss curves (Fig. C.III) suggest that reasoning- brased supervision converges more slowly, indicating that reasoning-char supervision is harder to optimize than direct label pre- diction.
supervised fine-tuning (SFT): reasoning-
Insight. These results suggest that zero-shot reasoning can enhance out-of-domanl generalization, but directly distilling reasoning chains during supervised fine-tuning is less effective than using label supervision alone. A key difficulty is that reasoning- supervision requires optimizing an inter- mediate step before predicting the final label, which slows convergence and reduces its effectiveness for classification learning. This limitation motivates the introduction of direct classification path- way to guide reasoning supervision, making reasoning signals more effective for classification.
4.2 MISMATCHES IN REASONING PATTERNS ACROSS SOURCES
Research Question. How do mismatches in reasoning patterns across different reasoning sources influence the supervision signals and optimization behavior of models?
Experiment Setting. We use the same dataset and base model as in Sec. 4.1 To study mismatches in reasoning patterns, compare fine-tuning with reasoning chains generated by large MILM (GPT-40) versus those produced by the target model itself (Intern VL.3-BB. We then analyze how these diffferent supervision sources affect token-level optimization by (i) comparing probability dis- tributions of all tokens in zero-shot and fine-tuned settings, and (ii) examining the top-15 tokens with the largest entropy reduction (Fig.
Observation. Fig. shows how mismatches in reasoning patterns affect token-level optimization during fine-tuning, First, comparing token probability distributions (Fig. A)) reasoning chains generated by GPT-4o contain richer contextual descriptions (c.g., background. viewpoint). but fine- tuning with them yields only a marginal token shift (+1.8ppp, A.I vs A.II). contrast, when using reasoning chains generated by InternVL33-8 itself, fine-tuning produces much larger token shift A.III vs A.IV), indicating that these chains are easier for the model to fit. Second, the entropy-based analysis (Fig. 4 B) reflects the same trend: GPT-40 reasoning exposes the model to fine-gre -re-gga descriptive details that may be informative but are less directly aligned with clas- sification, whereas self- aeff-genrateed reasoning emphasizes category-
tokens (e.g., 'cat family', 'bird'). Finally, qualitative examples (Fig. [4) illustrate these reasoning styles: GPT-4o produces longer, context-rich chains, while
generates shorter, label-foeused explanations. To-
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kens highlighted in red denote those with the largest entropy reduction. Together, these observations suggest a trade-off: large-model reasoning provides richer but pander-t--optimizz signals, while self-generated reasoning provides simpler cues that focus more directly the category label.
Insight. These results suggest that mismatches between large-model reasoning (c.g., GPT-4o) and self-generated reasoning (Intern VL.3-8B) create trade-off' between contextual detail and ease of optimization. Large-model chains provide richer descriptions but include complex, high-entropy tokens that harder optimize, while self-generated chains are easier fit but focus mainly on the category label and its immediate features. This trade-off may reduce the effectiveness of direct reasoning-chan chann supervision, motivating methods that preserve useful semantic detail while making reasoning signals easier to optimize.
5 METHOD
Problem Definition. Let X' denote the input space and y the label space. In domain generalization (DG), we assume access to N. labeled source domains DS {D J where each D is an empirical distribution over X y drawn from distinct but related environment. For the m-th source domain with No labeled samples, we write D = {x,, yi ) where x; E X denotes an input image and y; y/ its corresponding label. The goal of DG is to learn a model that generalizes to unseen target domains at test time, without access to any target data during training. The unseen N, target set is denoted as TTd = {DD m==1 where each DTT is distribution over X corresponding to an unseen domain, with DT & D for all
Overall Framework of our Method. To address the challenges identified in Sec. we propose RD-MLDG (Reasoning- Driven Multimodal LLM for Domain Generalization), a framework that in- tegrates reasoning into DG. RD-MIDG has two modules: (i) Multi-Task Cross-Training (MTCT), which jointly optimizes direct classification pathway and reasoning-chain supervision, so that the classification objective provides a stable signal to guide reasoning optimization, addressing the optimization difficulty in Challenge (Sec. 4.1:; and (ii) Self-Aligned Reasoning Regularization (SARR), which leverages self-labeling to produce reasoning chains consistent with the model's own reasoning style, thereby retaining useful contextual detail while reducing reasoning- -attnerra mis- matches, addressing Challenge 2 (Sec. (42)). Together, these modules make reasoning supervision more optimizable and informative, offering structured way to incorporate reasoning into DG for improved out-of-domain performance.
5.1 MULTI-TASK CROSS-TRAINING
Motivated by the difficulty of optimizing reasoning-chee chain supervision observed in Challenge 1, Multi-Tass Cross- Tooos--raannngg (MTCT) trains the model to jointly optimize reasoning-chain supervision with a direct classification pathway. The key idea is that classification supervision serves as simple and stable anchor objective, which guides reasoning optimization and helps alleviate the difficulty of fitting intermediate reasoning steps before predicting the final label. Concretely, for each training image xi, we construct two prompts: (i) a reasoning prompt from CennainBed-Resoonee corresponding class-relevant reasoning chain and (ii) classification prompt (no-thinking) "What type of object is in this photo? Choose from the following options:" where the model selects from candidate label list and the ground-truth label y; is used as the supervision signal. We denote these instruction templates as Gressnn and Gels, respectively.
with its
We fine-tune the base multimodal large language model (MLLM) by inserting LoRA adapters into both the vision encoder and the language decoder, enabling parameter-efficient supervised fine-tuning (SFT) without updating all parameters. Given batch {(x, y/, r where r, denotes
(ri. Ti,T,) is the class-relevant reasoning chain for X; and r, = the prefix sequence before predicting token ri,t, the MTCT loss can be defined as follows:
1 B
1 Σ
1 Σ log Mog |r, Xi, 9reason)
LMTET
log poly: x;, qat.)
(I)
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where T3 denotes the length (number of tokens) of the reasoming chain r,, and is used to normalize the generation loss that longer chains do not dominate the gradients.

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Published as a conference paper at ICLR 2026
Table 1:
Multi-source DG results (%) on DomainBed benchmark datasets. The best performances in comparisons are highlighted in bold and the second-best ones are marked with underlines.
Method
Venue
VLCS
OfficeHome
TerraIne
Avg.
RexNet- 50 Baxed Method.
CORAL Sun & Saenko,
2016
ICCV 16
86. 26.30
78. 18.80=a.0
68.70+a30
47. 47.60=1.0
70.33
MLDG el 2015a
AAAI'18
84.90.100
77.20ga.0
66.80
47.70
69.15
Mixstyle Zhou
202
ICLR 21
85.20=030
77.90=a50
60.40=aa2
44.00=a.0
66.88
Domuin Drop (rio ct ml
20230
ICCV"23
87 20
79. 80g
68. 68.70gn10
51.50
71.90 11.98
SMOS (Luo
AU34]
CVPR'24
89. 89.40
79.80
71.60; 1o0
15.40...4
74.05
RES (Huang of A/45
BCCV'24
90.00g010
79 19.80
71 10.80
51.40_aaa
73.25
GGA Balas & Dio0
CVPR'25
87 87.30
79.90gu..
68. a8.
50.60
71.58
VIT-B/16 Based Method.
SWAD
Cha et al 2021
NIPS'21
91. 10
79.40 10
76. 26.90
45,4 35,40
73.25
CLIP Radtoru cun 202
96. 20120=0.0
81.70=a.10
82.00
33.40=a.0
73.33
CoOp Zhoi 2122h
UCVV222
96. 10
77.60_mm
83.90gam
48. 48.80
76.63
MaPLe Khallak et 2023
CVPR 23
96.50 96.50
82. gm
83.40
50.20
78.08
SIMPLE"
[[
2023h
ICLR
99.00..m
87.70
59.00.1aes
82.10
VL.2--D Addepalli
2024
CVPR'24
95 +0.66
82. 92.67.au.3
85.44=aar
41.18+a04
76.24
CLIP-LoRA
Lanclla & Ben Ayed
2024
CVPR'24
97. 10 10
83. 13.10
83.90
$5.70
79.95
et 2025
ECCV'24
97 e0.0.0
82.4 82.40=a.4
83.60+040
50.20
78,30
DGCLDIP Wen 2025
CVPR'25
97.
2000
87.65gn00
63.27
83.19
MLLM Baved Method.
GPT4o OpenAA 2023]
=3
=0.00
12:0000
60.49+0000
Intem 3B Zhu elal. 2025
96. 26:26
85.67
85.10
46.84
78.47
VL3-BVL3-BB RD-LDD (Ouss
98. 13+010
87.03
91.73=0.0
70.68
86.89
This formulation directly addresses the optimization difficulty identified in Sec. 4.1 making reason- ing supervision more stable and effective for domain generalization.
5.2 SELF-ALIGNED REASONING REGULARIZATION
After the first stage MTCT training, the model has already been trained with reasoning chains dis- tilled from large MILMs (e.g., GPTT-4o). However, directly relying this supervision be chal- lenging due to sassoonnn--pttter mismatches, as discussed in Sec. 4.2 To mitigate this issue, we introduce Self-Aligned Reasoning Regularization (SARR), a soft self-labeling procedure. Specif- ically, the model generates its own reasoning under the instruction Aresson From each output, we extract the predicted conclusion enclosed between <CONCLUSIONS and </CONCLUS ION> and compare it with the ground-truth label Only reasoning chains with correct final conclusions are retained and used as refined supervision signals.
These retained reasoning chains are then combined with the classification prompt qets as part of the MTCT fine-tuning process. In each round of SARR, the reasoning chains, initially sourced from GPT-4o in the first stage, are replaced by self-generated reasoning chains in later stages. The train- ing objective for each round of SARR remains the same as MTCT, where the model fine-tuned using both reasoning chains and the classification prompt. Given batch with self seff--eneeated reason- ing chains (xi, Mi, 1:) where i; denotes reasoning chain retained because its final conclusion matches the ground ground-truth label, the loss in SARR for each round can be defined as:
B'
1 Th
1
CSARR
log pe(y: X;, qais)
log pe fr.e fi,cts Xi, Areason)
(2)
B
This self-labeling procedure can be repeated for N rounds. each round, the model is trained with both classification (qals) and reasoning (greason) prompts, generates reasoning chains for the source domain training data, and keeps only those whose final conclusions match the ground-truth label. The model is then fine-tuned again with these reasoning classification pairs. Through this iterative refinement, the supervision becomes both informative and easier to fit, helping mitigate the peatoning-pattern mismatches identified in Sec. 4.2
massoning-caassiicaatioo

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Published as a conference paper at ICLR 2026
Table 2: Ablation study on each component on OfficeHome and Terralncogniaa dataset.
OfficeHlome
Terralncognita
Idx
Method
Art
Clipart
Product
Real
Avg.
L100
L
L43
L46
Avg.
GPT-4o
87.10
85.36
95.20
92.82
90.12
67.28
59.71
64.47
50.49
600.49
3.79
3.64
2.70
3.49
3.41
0.16
0.25
0.15
0.33
0.22
CLS only
66.59
$1.19
91.46
92.10
89.16
75.26
64.66
69.98
55.99
66:17
Reasoning only (Baseline)
87.03
82.13
93.77
91.45
61.26
69.40
MTCT
89.00
83.71
95.49
93.53
90.23
77.82
65.03
72.71
59.41
68.74 54Mp)
SARR
88.30
95.29
62.08
69.51
59.27
66.67(1))))
MTCT SARR
90.13
84.57
96.00
93.62
91.08/12
82.43
66.54
73.62
61.15
70.94(74.44%))
IateraVL3-8B
81,95
78.26
91.91
88.27
85.10
61,84
41.27
45,60
38.66
46.84
CLS only
87.27
95.11
69.70
10
Reusoning only (Baseline)
86.59
82.45
94.33
91.66
88.76
74.42
60.39
68.21
55.21
64.56
11
MICT
89.33
83.53
95.79
93.67
90.58 Rill
76.78
60.55
71.94
59.47
67.19 sslle)
12
SARR
90.89
83.34
95.95
93.44
90.91 112.349pp)
74.97
60.96
68.28
56 56.96
65. 2y 73M
SARR
91.72
96.25
91.73112.97900
66.12
73.38
70.65 (1.0055)))
(M.)+CIS only
(U.ll) Reasening snly
(4310) MTCT
(0.10) Reesosing only
(RII)+ MTCT
Tiekke
Renter
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Fieitww
Figure 5:
Probability distributions on TerraInc source domain training data under direct label prediction, reasoning-only SFT. and MTCT SFT: (A) all tokens and (B) class tokens.
Analysis of MTCT (Sec. 5.1)
6 EXPERIMENTS
Datasets and Evaluation Protocols. We adopt four widely used benchmarks for evaluation: PACS (Li et al. 2017) (4 domains, 9,991 samples, 7 classes). VLCS eLi al. 2017] (4 domains, 10,729 samples, classes). OfficeHome (Venkateswara et al. (2017) (4 domains, 15,588 samples, 65 classes), and Terrainc (Beery et al. 2018) (4 domains, 24,788 samples, 10 classes).
For each dataset, we use the extended DomainBed Reasoonig version, where each image- label pair is paired with structured reasoning chains generated by GPT-4o (see Sec. B We evaluate classification under domain shift using the standard DomainBed ceave-one-domain out protocol and report the average test accuracy (Avg-acc) across all target domains. To reduce variance, results are averaged over three runs with different random seeds.
Implementation Details. We follow the supervised fine-tuning (SFT) procedure described in Sec. Each training stage runs for 3 epochs with a batch size of 128 and learning rate of 5e-4. We use AdamW as the optimizer. LoRA adapters (rank 8) are applied to both the vision encoder and the language decoder for parameter-efficiee fine-tuning. For SARR (Sec. 5.2). we set the number of self- self-labeling rounds to N 3. All experiments are conducted on 4 NVIDIA A100 GPUs (80GB).
6.1 RESULTS ON MULTIPLE DOMAIN GENERALIZATION
To demonstrate the effectiveness of our proposed RD-MLDG, we compare it with representative SOTA methods (Tab. D. Built on Intern RD-MLDG achieves the best average accuracy of 86.89% surpassing strong commercial MILM (GPT-40, 83.46% and competitive CLIPP/VT approaches (c.g., DGCLDTP, 83.19%). Per-dataset, RD-MLDG attains new best results on VLCS (87.03%), OfficeHome (91.73%). and TerraInc (70.65%), while remaining highly competitive on PACS (98. 13%). To sum up. our proposed method has two main advantages: (1) Compared to feature-level invariant representation learning, RD-MLDG leverages class-relevant reasoning chains, which are explicitly observable and understandable by humans, rather than relying on latent feature representations that are difficult to interpret. (2) Through an efficient two-stage supervised fine-tuning procedure, our method avoids the need for complex training strategies while substantially enhancing the classification ability of the underlying MLLM under domain shift.
6.2 ABLATION STUDY
To further evaluate the effectiveness of each component, we conduct ablations using Intern VL3-2B and Intern VL OfficeHome and TerraIne; results are shown in Tab. 2 The very low zero-shot
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(A.I) Class-relevant token
(A.II) Domaain-specffic token
(B.1) Class-relevant token
(B.1I) Domain-speciffc token
Beitat chanl box docitueed
can Mami.ua
blesal Chan Vtat Mariican
10
2.20 122.00
700
100 200 500
0/
110 500
30
0104 100/041
7:00
120
Analysis of SARR (see Sec. 5.2 from token entropy on TerraIne (A) and OfficeHome (B). We track the entropy of selected class-relevant (A.I I & B.I) and domain-speciftc tokens (A.1I & B.II) across fine- tuning iterations, with vertical bars denoting variation across samples. Blue curves correspond to fitting GPT-4o reasoning chains, and orange curves to Intern V3--8B self-generated reasoning chains.
Figure 6:
result of Intern VL332B (2nd row) is due to its limited instruction-following ability, but fine-tuning with reasoning chains supervision (4th row) quickly recovers strong baselines.
Effectiveness of Multi-Task Cross- Teass-Trainng.. On both OfficeHome and Terralne, direct label pre- diction outperforms sesssoonnn-chiiin supervision: +0.56%p and +0.47%p on InternVL3-22 VB (3rd vs. 4th rows), and +0.63%p and +2. 13%p on VL3-8B (9th vs. 10th rows), supporting the obser- vation from Challenge 1 that reasoning-only supervision is less effective for classification. Relative to the reasoning-only baseline. MTCT yields additional improvements of +..3%pp and +2. +2.74% on Intern Intern VL3-2B t4th vs. 5thh rows). and +1.81 1p and +2.63%p on Intern VL3-8B (10th vs. 11th rows), supporting that the direct classification pathway provides stable signal that helps reasoning super- vision contribute more effectively. Moreover, when combined with SARR, MTCT brings further gains of +1.28%p and +4.27%p InternVL3-22 (6th vs. 7th rows), and +0.83%p and +5.36% on Intern (12th vs. 13th rows), reinforcing its role in guiding reasoning optimization.
Fig. 5 provides further evidence from the token-probability perspective. In Fig. A, the all-token distributions show that MTCT still struggles to fully fit the semantically rich reasoning chains generated by GPT-4o, with 19.33% of tokens remaining at low probability after fine-tuning. By contrast, Fig. B highlights consistent improvements in the class-token distributions: the pro- portion of high-confidence class tokens (>0075) rises from 86.33% to 90.23%, while the share of low-confidence class tokens drops from 7.59% to 3.19.. These results indicate that MTCT does not substantially enhance the modeling of all reasoning details, but it does strengthen the fitting of class tokens, which directly supports the classification objective.
Effectiveness of Self-Aligned Reasoning Regularization. On OfficeHome and TerraInc, SARR improves accuracy over direct reasoning-chain supervision by +1.20%p and +0.67%p on InternVB. 2B (4th vs. 6th rows), and by +2.14% and +0.73% on Intern VL3-8B (10th vs. 12th rows). When combined with MTCT, it brings further gains of +p.6%pp and +2.20%p on InternVL3-2B (5th vs. 7th rows), and +1.15%p and +3.46%p on Intern (11th vs. 13th rows). These results support the insight from Challenge 2: self-labeled reasoning retains class-relevant information from large- model supervision while producing signals that the model can more effectively.
Fig. 6 A reports token-entropy dynamics on TerraInc. We note two key observations. First, for class- relevant tokens (e.g., mark, floating), GPT-4o and Intern reasoning chains start with similar entropy in the zero-shot stage, but as fine-tuning proceeds, entropy decreases faster for Intern 8B, showing that SARR shifts supervision toward signals the model can fit more readily. Second, for domain-specific tokens (e.g., demanding, viewpoint), GPT-4o reasoning chains have higher initial entropy than self- reffreereeted chains, reflecting richer but harder-to-fe details; with SARR, the model reduces fitting pressure on these tokens and instead focuses class-relevant ones. This reallocation of optimization effort helps explain the accuracy gains of SARR. Results on OfficeHome (Fig. 6 B) show the same trend.
6.3 PARAMETER ANALYSIS
We further analyze how the number of self-labeling rounds N affects performance on TerraInc (Fig. 7 top). We report p-values from paired t-tests, where runs with the same random seed are compared across adjacent N settings. Accuracy shows a small but statistically significant improve- ment from 70.06% at N to 70.59% N (p < 0.01). At N = 3, accuracy reaches 70.65%
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impputt en L100
Imiputt N on 1:8
Impact of it 143
Iripuct of en 146
Average of hour Derant
Poods
patce
Value
(LD CX- U
CAJI ON 2)
OLIDD LIM IN 3)
(N 41
(A.) IN
Zam
Zaro
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Zoraate
AATS
6.5
210
535
4.6
375
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431 611
Figure 7: Impact of the number of self-labeling rounds N in SARR (Sec. 5.2 TerraInc using Intern VL3- 8B. Top: classification accuracy four domains (LI00, L38, L43, L46) and their average, with paired f-test p- values for adjacent N shown on the right axis (reference line at p = 0.05). Bottom: zero-shot token probability distributions (log-scale) on L100, using supervision data from each self-labeling round.
but the improvement over N = 2 is not statistically significant (p = 0.07). For N > 3, accuracy remains stable between 70. 70.50% and 70.60% with p-values above 0.1.
We also examine the zero-shot token-probability distributions across different self-labeling rounds (Fig. 7 bottom). From N = 1 to N 2, the proportion of high-confidence tokens increases (2.27%) while low-confidence tokens decrease (0.29%) indicating a clearer supervision signal. For N 2 3, the probability distribution remains nearly unchanged, showing that self-labeling converges within the first few rounds. We therefore set N = 3 in our experiments.
7 CONCLUSION
This work investigated how reasoning can contribute to domain generalization and identified two key challenges: optimization difficulty and reasoning-pattemi mismatch. To address these issues, proposed RD-MLDG, which integrates class-relevant reasoning chains with label supervision through MTCT and SARR. Experiments on DomainBed show that RD-MLDG achieves the best reported performance among strong baselines.
ACKNOWLEDGMENTS
This work was supported in part by the National Key R&D Program of China under Grant No.2023YFA 1008600, in part by the National Natural Science Foundation of China under Grants 62576262, U22A2096, in part by the Key Research and Development Program of Shaanxi Province under grant 2024GX-YBXMBI
2024SF-YBXM-647 in part by the Fundamental Research Funds for the Central Universities under Grant QTZX25083, QTZX23042.
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Table 3: Cross-domain divergence of visual emheddings and reasoning-chain embeddings for each Terrannc class.
bird
bobcat
cal
coyote
dog
empty
opossum
rabbit
raccoon
squirrel
Avg.
Domain divergence (Visual)
0.209
0.251
0.314
0.387
0.213
0.144
0.266
0.167
0.258
0.176
0,239 0.099
Domain divergense (Text)
0.054
0.048
0,103
0.114
0.093
0.126
0.091
0.103
0.142
0.115
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A TRAINING PIPLINE FOR MCTC AND SARR MODULE
A. QUANTITATIVE VALIDATION OF REASONING CHAIN DOMAIN INVARIANCE
To verify the hypothesis that reasoning chains more
than visual features,
performed a quantitative validation. Following existing work that measures cross-domain distri- butional discrepancies (Guo et al. 2023b), we compute the Maximum Mean Discrepancy (MMD)
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between a source domain D m denote the embedding extracted from CLIP's vision encoder for visual features, from CLIP's text encoder for reasoning-chatle embeddings. Under a linear kernel, MMD reduces to the squared distance between mean embeddings:
{(x,, 94 [4)=
No and a target domain D y = {x, برك Let f(x)
N.y
N
MMD Ded
1 N
((x,)
1
m
((x;)
N
m
Using identical domain splits for both modalities, we compute the MMD for each category in Ter- ralnc. As summarized in the Tab. reasoning-chain embeddings exhibit dramatically lower cross- domain divergence than visual embeddings (average 0.239 - 0.099, a 58.6% reduction). This large gap provides clear quantitative evidence that reasoning chains capture semantically stable, class- relevant information that is far less sensitive to style, background, or environmental shifts than vi- sual features. Therefore, the results strongly support our core hypothesis that reasoning chains are significantly more domain invariant...
Table 4: Rejection rate across SARR iterations (N) on the four TerraInc domains.
L100
L38
L43
L38
Avg.
N=0
41.78
39.25
40.84
36.18
39.51
N=1
21.66
22.14
17.56
15.57
19.23
N=2
18.49
17.98
15.07
13.64
16.30
N=3
17.07
15.74
14.43
12.01
14.81
A.2 REJECTION RATE ANALYSIS IN SARR ITERATIONS
To quantify the filtering behavior of SARR, measure the rejection rate, defined as the percentage of self-generated reasoning chains whose <CONCLUSION> does not match the ground-truth label. Using Intern VL3-8B on the Terraine dataset (see Tab. 4 observe that the rejection rate is rel- atively high at N 0 (39.51%) ))), reflecting the mismatch between GPT-4o-style reasoning and the model's native prediction tendencies. After the first SARR iteration, the rejection rate drops sharply to 19.23%, and continues to decrease at N = 2 and N = 3, eventually stabilizing around 15-17%.
This consistent downward trend indicates that, SARR iterations progress, the model's self- generated reasoning chains increasingly lead to correct conclusions. Rather than performing random filtering, SARR progressively strengthens the semantic alignment between the reasoning chain and the model's final prediction. The improving correctness of ceff-generated chains demonstrates that the reasoning process itself becomes more stable, coherent, and predictive, providing direct evidence that RD-MLDG enhances reasoning quality rather than relying on incidental regularization effects.
Table 5: MTCT and SARR Performance LLaVA-1.5 for TerraInc (%).
Terrainc
Idx
Method
L.100
T,38
L.43
T.46
Avg.
InternVL.3-2B Reasoning only (Baseline)
74.79
61.26
69.40
58.26
66.00
2
InternVI.3-BB MTCT
77.82
65.03
72.71
59.41
(8.74()2.74%p)
3
InternVL.3-2B- + MTCT + SARR
82.43
66.54
73.62
61.15
(1.94(14:94%6)
4 InternVL.3-8B Reasoning only
74.42
60.39
68.21
55.21
64.56
5
InternVL33-8B- + MTCT
76.78
60.55
71.94
59.47
67. (7.119(2.639))
InternVL.3-8B MTCT SARR
81.74
66.12
73.38
61.36
70.65
7
LLaVA-1.5-7B- Reasoning only (Baseline)
75.34
58.20
62.51
52.24
62.07
8
LLLVA-115-7B- + MTCT
79.87
58.93
63.38
53.48
63.92 (1188)p
LLaVA-1.5-BB MTCT SARR
80.93
60.01
64.51
55.14
65.15(13.06%))
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Published as a conference paper at ICLR 2026
Algorithm RD-MLDG with MTCT and SARR
Require: Souree domains { {Di, I base model fs. DomainBed BenaainBedRRcsooning Reasoning dataset, self-labeling rounds N
Ensure: Fine-tuned model
Stage Multi-Task Cross-Trainin (MTCT)
1: for each minibatch {(r, Vt, r.)) do
2: Construct classification prompt Jets and reasoning prompt Qresson
Compute classification loss Lek
4:
Compute reasoning loss Liasson
5:
Update 0 with LMiCr
Lets + Lieason (Eq.
6:
end for
Stage 2: Self-Labeled Reasoning Regularization (SARR)
7:
for l: = 1 to N do
8:
for each sample (Ji; Ui) do Generate reasoning f; with Tressn
9:
10:
Extract predicted conclusion between <CONCLUSICN> and </CONCLUSION> tags if conclusion
11:
then
12:
Retain (z;, Mr. Ps) supervision
end if
14;
end for
15:
Fine-tune retained pairs with LSARR = Lets + Lieasee (Eq.
16:
end for
B RESULTS ON SMALLER OPEN-SOURCE MODEL
To evaluate whether MTCT and SARR can generalize to smaller open-source models without dedi- cated reasoning pretraining, we conducted additional experiments on LLaVAA1.5-7B, whose reason- ing ability is substantially weaker than that of Intern VL.3. This is directly reflected in the reasoning- only basclines (see Tab. 5): under the same setting on TerraInc, InternVL3-2B achieves an average accuracy of 66.00%, whereas LLa initial reasoning capability. Despite this weaker backbone, our method remains consistently effec- tive. As shown in the table, MTCT improves LLaVA-115-7B from 62.07% to 63.92%, and adding SARR further increases performance to 65. 65.15%. A similar improvement observed on Intern VL.3- 2B, indicating that both components provide stable gains even when the underlying model lacks strong reasoning pretraining.
reaches only 62.07%, indicating clear gap in their
These results collectively demonstrate that RD-MLDG does not rely strong reasoning backbone. Both MTCT and SARR transfer effectivvly to smaller, weaker, and fully open-source multi- modal models, showing that the proposed framework is broadly applicable beyond large reasoning- centric models.
C RESULTS ON SINGLE DOMAIN GENERALIZATION
Background. Most existing DG studies assume access multiple source domains. However, in practice, models are often required to generalize from single source domain to multiple unseen target domains (Single Domain Generalization, SDG). This setting is more challenging because the model cannot rely on inter-domain variation during training, making reasoning supervision poten- tially more valuable.
Experiment Setting. We conduct SDG experiments on the OfficeHome dataset, following the standard protocol where one domain (Art, Clipart, Product, Real World) is used as the source do- main and the other three serve as target domains. We compare our method with representative SDG approaches, including domain-invariant representation learning (IRM, CORAL, VREx), data augmentation strategies (Mixup, CutMix, OrgMixup), adversarial learning (DANN, CDANN), and recent single-domain adaptation methods (RIDG, SAGM, PAPT). For our approach, we evaluate Intern VI.3-2B with RD-MLDD MGD under the SDG setting.
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Published as a conference paper at ICLR 2026
Table 6: SDG results (%) OfficeHome. One domain is used as the source domain and the others are used as the target domain. The best performances in comparisons are highlighted in bold and the second-best ones are marked with underlines.
Method
Venue
P
R
Avg.
DANN (Ganin et 2016
IICATTG 16
55.20
49.30
48.40
58.40
52.80
CORAL (Sun & Sacnko 2016
ICCV'16
55.60
52.80
50.30
59.40
54.50
IRM (Arjovsky al 2019
$4.90
53.20
48.60
59.20
$4.00
MMD LL al.
ICCV'
55.10
$2.00
50.30
59.30
54.20
OrgMixup Zhang
2018
ICLR'18
56.00
54.40
50.40
61.00
55.50
Mixup Yan al.
2020
55.50
54.10
49.40
59.40
$4.60
CutMix Yun
2019
CVPR'T 19
53.50
52.20
47.70
60.20
$3.40
CDANN Ganin et cl
2016
55.20
49.90
47.60
58.60
52.80
GroupDRO (Sagawa
2019
ICLR'20
55.10
52.00
50.30
59.30
54.20
MTL Blanchard el all 2021
IMLR'21
55.30
53,30
49.00
60.40
$4,50
ARM Zhang et al 2021
NcurIPS'21
55.00
51.60
47.30
59.30
53,30
VREx Krueger ct al.
202
ICML'21
55.50
52.60
49.10
59.30
54.10
Mixstyle (Zoo ct 2021
ICLR'21
44.30
29.80
33.60
48.50
39.00
ERM 2013
2013
ICLR'21
$5.60
52.80
50.30
59.40
54,50
SAM [
2018b
ICLR'21
56.90
53,80
50.90
61.50
55,80
SagNel (Nam al.
CVPR'21
$6.90
53,40
50.80
61.20
55.60
Fishr Rame
ICM1:22
55.10
51.20
49.20
$9.90
$3.90
RIDG CCen
2023b
ICCV'23
56.80
55.40
50.50
60.90
$5.90
SAGM Wang
CVPR'23
57.70
54.80
$1.50
61.40
56.30
ITTA (C
Chen
20250
CVPR'23
56.00
51.50
50.50
61.60
$4,90
UDIM Shin et al.
ICLR'24
58.50
55.70
$4.50
64.50
58.30
PAPT
Xu ct al. 2025
CVPR'25
59.81
68,30
59.12
60.87
62.03
Intern VL3-2B RD-MLDG (Ours)
89.79
92.54
86.43
88.72
89.37
Observation. As shown in Tab. 6 RD-MLDG achieves large margin over all baselines, reaching an average accuracy 89.37%, compared to the best baseline PAPT at 62.03%. This indicates that reasoning supervision, even from single domain, provides strong class-relevant signals that transfer effectively to unseen target domains. Notably, while prior methods rely on feature-level invariance adversarial strategies, our approach explicitly incorporates reasoning chains, which appear more robust under severe domain shift.
Table 7: Base-to-new generalization results on FGVC-Aircraft.
FGVC-
PGVC-Aireraft
Idx
Method
Base
New
H
1
Intern
20.35
17.83
19.01
2
+ Reasoning only (Baseline)
39.20
15.44
22.11
3
MTCC
56.42
18.60
(1..8(1)))
4
MMCC SARR
60.11
24.33
(f1.3())))
D RESULTS ON BASE-TO-NEW EXPERIMENT
To evaluate the broader advantages of reasoning driven domain generalization, we perform a base- to-new class generalization experiment using the FGVC-Aireraft dataset (Maji et al. 2013).. Unlike the appearance-hlr
shifts seen in the DomainBed benchmarks, the base-to-new protocol intro- duces semantic and compositional shifts within the label space. In this setting, the model must transfer reasoning from familiar categorics to structurally distinct, unseen ones. This task is more challenging than traditional domain shifts it requires the model to handle shifts in the underlying category structure rather than just appearance.
The FGVC-Aireraft dataset is particularly well-suited for this evaluation because its categories defined by fine- grained structural and compositional attributes, such as wing geometry and engine configuration. These attributes make the task heavily reliant structured reasoning rather than
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Table 8: VQA and VE results (%) VOLDOGER benchmark dataset. The best performances in comparisons are highlighted in bold and the second-best ones are marked with underlines.
VQA
VE
Method
Real
Cartion
Pencil
Real
Cartoon
Pescil
47.
52.51
st
50.41
73.10
DLIP
Lea 20233
59.19
30.29
51.32
50.88
52.92
66.73
48.26
52.93
53.62
53.39
Open Sperr-Source Models.
B1 BLIP2-FFaTTT-M Ti ml 20
65.29
64.41
61.18
62.92
63.45
63.82
73.13
72.34
72.00
70:30
PaliGemma Steiner & dil
80.59
79:41
75.29
75,59
77.72
33.43
33.91
35.02
34.79
34.29
LLaVA-Next VXUnE-15
2024
80.29
67.65
64,12
64.12
09.93
55.76
55.25
57.55
55.15
56.03
LLaVA-Next Mistral77
81.76
65.88
70.05
70.36
Proprictary Models.
GPT-4-Visko
OpenAl
2024-01.09 OkRAA
76.47
61:75
(PP-44 2024-05-13 (OperAA
27.35
79.56
71.08
73.13
72.47
70.74
71.85
Claud: 3 Haiku
75.00
67.35
62.06
62.35
66.69
58.18
63.55
67.36
66.47
64.01
Clande Sonnet
68.24
74.12
72.35
70.29
71.25
59.
72.25
72.04
71.08
68.70
Claude 3 Ogus
63.55
63.82
61.76
63.24
03.09
59.91
66.05
61.18
64.06
62.95
Gomina 1. Pro Tears
2023
73.23
68.24
68.25
68.82
69.63
64.63
60.52
65.13
64.29
63.09
Gentiai
Method (Our).
LLaVA-1.5.00 shot)
LLaVA-1.5-78- Ressoning only (Roseline)
75.88
67.51
LLAVA-1.5-BB. MTCT
78.53
68.82
64.71
69.41
72.35
65.97
67.74
66
68.08
TTAVA-1.5-7R 5-7R MTCT + SARR
80.88
71.17
67.06
67.35
71.65 62
73.96
69.12
68.89
68.54
70.13
IntemV1.3-38 3-23 (Zero shot)
0.59
0.29
0.29
0.59
0.44
0.34
0.23
0.34
0.34
0.31
IntemVL3 2B Reasoning only (Baseline)
74,12
66.76
66.18
63.82
67.72
67.97
63.67
65:55
67.17
66.09
IntemVL3-22 MTCT
83,53
69.41
68.53
69.12
72.65
71,20
65.05
66.94
66.71
67.48
Im2nVL3-28 MICT SARK
IneemVL33-BB shot)
ImsnVL3-SB Reasoning only (Baseline)
20.88
73.50
IntenV1 3.88 MTCT
77.94
71.47
73.96
71.57
70.62
IntemVL3-SB MTCT SARR
85.00
81.25
72.94
69.41
77.28
74.76
70.85
70.62
72.11
72.09
surface-level visual cues, providing an ideal benchmark to test the model's ability to generalize through reasoning across different domains.
Experiment setting: Concretely, following existing work (Zhou al. 2022b) that adopts the widely used base-to-new evaluation setting, we randomly split the 100 aircraft categories into 50 base and 50 new classes. All models are trained only on the base classes, and at test time we evaluate them on both base and unseen classes, reporting performance mean H. This setting ensures that good performance requires not only retaining accuracy on seen categories, but also transferring learned reasoning semantically novel and compositionally distinct categories, thereby creating a genuine conceptual generalization scenario.
each split well their harmonic
Observation: As shown in the Tab. 7 RD-MLDG improves harmonic mean accuracy from 19.01 % + 34.63%, representing a substantial gain under this challenging conceptual shift. Notably, accu- racy on unseen new classes increases from 15.44% 24.33%, demonstrating that reasoning super- vision enhances transfer to semantically novel and compositionally distinct categories rather than merely improving robustness to stylistic or appearance- level variations. These results provide direct empirical evidence that RD-MLDG benefits conceptual generalization beyond the appearance- level shifts captured by existing benchmarks.
E EXPLORING THE EFFECTIVENESS OF RD-MLDG ON VQA AND VE (VISUAL ENTAILMENT)
RD-MLDG is designed to be flexible and adaptable across various tasks. Both MTCT and SARR are built on structured reasoning-chain supervision, making the framework task-agnostic. This means that long task follows general "vision reasoning - output" pathway, RD- MLDG can be seamlessly applied without requiring changes to the model architecture or the opti- mization process.
In the Sec. 3 we adopt the DomainBed setting because is the most widely used and standardized benchmark for studying domain generalization. Building on this foundation allows us investigate reasoning-chaie chain supervision within a estabessabl DG framework, ensuring that our analysis of the challenges introduced by reasoning in DG is performed under consistent and community-
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recognized evaluation conditions. Through this setting, identify and study two fundamental challenges caused by reasoning in DG: (i) the optimization difficulty and high-entropy gradients mismatch between external LLMs and target multimodal
(Sec. 4.1. and (ii) the reasoning- models (Sec. 4.2). MTCT and SARR are explicitly designed to address these two issues, and their mechanisms are independent of the specific downstream task used to study them.
Extension to VQA and VE: To verify the task generality of RD-MLDG beyond visual classifi- cation, extend our method to two multimodal tasks: VQA and Visual Entailment (VE). We construct reasoning-augmentt
versions of both VOLDOGER-VE -QQ and VOLDOGER-VE (Choi al. 2025) so that their data formats align with DomainBed Reasoning. Each sample is aug- mented with the same five structured components: <SUMMARY>, <CAPTION>, FREASONINGS <REFLECTION>, and <CONCLUS TON>. This ensures that both MTCT and SARR can applied without architectural changes to any model. Both datasets contain four visually heterogeneous do- mains (Real, Cartoon, Pencil, Oil). The style shifts significantly affect visual perception, textual grounding, and multimodal alignment. This makes VQA and VE natural testbeds for assessing whether reasoning-driven DG generalizes beyond classification.
Across both the VQA and VE tasks, our results (see Tab. 8 consistently demonstrate that RD-MLDG extends well beyond visual classification and remains effective in multimodal set- tings that require joint visual grounding and reasoning. On the VQA task. Intern VL3-8B improves from 73.60% (reasoning-onf) baseline) to 75.44% with MTCT and further to 77.28% with SARR. On the VE task, the same model improves from 70.59% to 70.62% and finally to 72.09% after apply- ing both modules. These gains are not isolated to a single model: ALaVA--155-77 which lacks any reasoning-oriented pretraining, shows even larger improvements, rising from 63. 63.68% - 69.41% 71 71.22% on VQA and from 67.12% + 68.08% + 70.13% on VE. EnternV33-22 exhibits a similar trend on both tasks.
Observation:
Beyond absolute gains, RD-MLDG also enhances the competitiveness of the underlying models relative to stronger multimodal LLMs. After applying MTCT and SARR, Intern VL3-8B surpasses most zero-shot MLLMs, including BLIP2, the Claude 3 family, Gemini 1.0 and 1.5, and several LLaVA-Next variants, in average accuracy across all four visual styles for both VQA and VE. The fact that these improvements appear consistently across two distinct multimodal tasks, each involv- ing substantial style-induced domain shifts (Real, Cartoon, Pencil, Oil), further confirms that MTCT and SARR enable the model to learn domain-invariant reasoning patterns that remain stable across different cross cross-modal decision -making objectives.
Taken together, the consistent performance gains across VQA and VE, coupled with improvements over a large set of open-source and proprietary multimodal baselines, provide strong evidence that RD-MLDG is task-agnostic and broadly applicable reasoning-drive confirm that our approach enhances the robustness and domain invariance reasoning chains in diverse multimodal environments, thereby validating its generality far beyond visual classification.
DG framework. The results
F LARGE LANGUAGE MODEL USAGE STATEMENT
We relied on large language model (LLM) only for language refinement (grammar, wording, and clarity). The LLM did not contribute to the conception of ideas, study design, data collection, analysis, or figure/table generation, and it did not write technical content. All methodological and experimental contributions, as well as the interpretation of results and final decisions, are by the authors.
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Instruct prompt:
have an image and a question that want you to answer. need you to strictly follow the format with five specific sections: SUMMARY, CAPTION, REASONING, REFLECTION, and CONCLUSION, is crucial that you adhere this structure exactly as outlined and that the final answer in the CONCLUSION matches the standard correct answer precisely. To cxplain further: In SUMMARY, briefly cxplain what steps you'll take to solve the problem. In CAPTION, describe the contents of the image, specifically focusing on details relevant to the question. REASONING, outline step-by-step thought process you would use solve the problem based on the image. In REFLECTION, critically assess your reasoning, considering potential ambiguities, alternative interpretations, or justifying why your logic is most sound. In CONCLUSION, give the final answer in a direct format, and it must match the correct answer exactly. If it's multiple choice question, the conclusion should only include the option without repeating what the option is. Here's how the format should look: <SUMMARY~(Summariz how you will approach the problem and explain the steps you will take reach the answer.] </SUMMARY> <CAPTION-(Provide detailed description of the image, particularly emphasizing the aspects related to the question.] </CAPION> <REASONING~IProvide chain-of-thought, logical explanation of the problem. This should outline step-by-step reasoning.] </REASONING> (REFL and evaluate your reasoning. Consider any uncertainties or edge cases, and reaffirm why the answer reliable.] </REFLECTION> <CONCLUSION~<Slate the final answer in clear and direct format. It must match the correct answer exactly.] <CCONCLUSION~(Do not forget </CONCLUSION=1) Please apply this updated format meticulously to analyze the given image and answer the related question, ensuring that the answer matches the standard one perfectly.
Question prompt:
Question: Please describe the category of the image. will provide a series of options, each separated by comma.
Options: ['bird', 'bobcat', 'cat', 'coyote', 'dog', 'empty', 'ppossum', 'rabbit', 'raccoon', 'squirrel']
Figure 8: Input prompt template used for reasoning-chain generation. The prompt consists of parts: (i)
question instruction paired with an image and candidate label options, and (ii) explicit guidelines requiring the model (GPT-4o) to output reasoning in a structured five-section format (SUMMARY. CAPTION, REASON- ING, REFLECTION, CONCLUSIÓN), These generated reasoning chains are then paired with ground-truth labels and used to fine-tune downstream multimodal LLMs.
20


Reviews:
[{"review_id": "qmnAuHBzmp", "reviewer": "Reviewer_Zr5a", "summary": "This paper tackles the problem of domain generalization (DG) by proposing a novel approach that leverages the reasoning capabilities of Multimodal Large Language Models (MLLMs). Instead of relying on traditional methods that seek feature-level invariance, the authors argue for pursuing process-level invariance by training models to generate class-relevant reasoning chains, which are hypothesized to be more robust to domain shifts. The paper proposes RD-MLDG that jointly optimizes a direct classification path and the reasoning-generation path, and introduces an iterative self-labeling stage where the model generates its own reasoning chains, which are then filtered and used as the new supervision signal. The authors demonstrate that RD-MLDG achieves state-of-the-art results on four standard DG benchmarks (PACS, VLCS, OfficeHome, and TerraIncognita).", "questions": "1. In the SARR filtering step, what percentage of self-generated reasoning chains are typically discarded for leading to an incorrect conclusion? Does this rejection rate decrease as the SARR rounds progress?\n\n2. The core hypothesis is that reasoning chains are more domain-invariant than visual features. Figure 1 provides a great qualitative example. Have you considered any quantitative validation of this hypothesis? For instance, one could measure the embedding-space similarity of reasoning chains for the same class across different domains, and compare this to the similarity of visual features for that same class across domains.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper addresses domain generalization by proposing a novel approach that leverages the reasoning capabilities of Multimodal Large Language Models, which is a challenging and practical scenario.\n2. The paper is well written and easy to follow.\n3. The paper provides extensive experiments, showing the effectiveness and versatility of the proposed method.", "weaknesses": "1. The quality of the entire DomainBed-Reasoning dataset influences on reasoning chains generated by GPT-4o. This introduces a potential dependency and bias. \n\n2. The proposed training procedure appears computationally intensive. It involves an initial MTCT stage followed by N=3 rounds of SARR. Each SARR round seems to require a full generation pass over the source data, a filtering step, and another fine-tuning stage.", "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": "cxNLvXAVqu", "reviewer": "Reviewer_b6ME", "summary": "This paper explores the integration of reasoning supervision into DG within MLLMs. The authors construct DomainBed-Reasoning, a new benchmark where each sample in DomainBed is paired with structured reasoning chains generated by GPT-4o. To address two key challenges -- (1) the optimization difficulty of reasoning-chain supervision and (2) the mismatch between external (GPT-4o) and self-generated reasoning patterns -- the authors introduce RD-MLDG. It comprises two components: Multi-Task Cross-Training (MTCT), which jointly optimizes classification and reasoning pathways to stabilize learning, and Self-Aligned Reasoning Regularization (SARR), a self-labeling mechanism that aligns supervision with the model’s own reasoning style. Experiments on four DG benchmarks (PACS, VLCS, OfficeHome, TerraIncognita) show consistent improvements over strong CLIP- and MLLM-based baselines. The paper argues that reasoning can serve as a complementary, process-level signal for domain generalization.", "questions": "See Weakness.\n\nAdditional Questions:\n1. How do you evaluate the quality and logical validity of reasoning chains beyond coherence filtering?\n\n2. Would RD-MLDG still outperform baselines if the reasoning text were semantically perturbed or replaced with neutral descriptions (to test for stylistic bias)?\n\n3. Could MTCT or SARR generalize to smaller open-source models without reasoning pretraining (e.g., LLaVA-1.5) or purely visual transformers?\n\n4. Do the authors observe changes in reasoning chain complexity or diversity across SARR iterations, and how does this correlate with accuracy gains?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The work connects reasoning in MLLMs with robustness under domain shift, introducing a conceptually novel direction -- process-level invariance -- that goes beyond traditional feature-invariance approaches.\n\n2. DomainBed-Reasoning is a non-trivial extension with structured reasoning chains, multi-stage generation, and rejection sampling to ensure coherence. This dataset can serve as a testbed for future studies on reasoning-based generalization.\n\n3. RD-MLDG addresses two empirically observed issues with distinct and complementary mechanisms (MTCT for optimization stability, SARR for reasoning alignment). The framework is simple yet principled.\n\n4. The authors provide extensive ablations, token-level entropy analyses, and convergence diagnostics that convincingly support the claimed effects of MTCT and SARR.\n\n5. The paper is well-written, visually organized, and provides enough procedural detail for replication.", "weaknesses": "1. DomainBed-Reasoning relies entirely on GPT-4o-generated reasoning chains. These synthetic sequences likely encode stylistic and distributional priors from GPT-4o’s pretraining, rather than domain-grounded reasoning. As a result, RD-MLDG might learn to imitate linguistic style alignment rather than to capture transferable causal or process-level invariances. While the dataset is well-constructed, it remains uncertain whether the performance gains derive from genuine reasoning integration or from the regularizing effect of textual augmentation.\n\n2. The paper treats reasoning chains as structured textual supervision but does not formally define what constitutes “reasoning” in the DG context -- e.g., whether it implies causal inference, compositional abstraction, or hierarchical explanation. Without such formal grounding or reasoning-quality diagnostics (e.g., logical coherence or factual correctness), it is difficult to evaluate whether RD-MLDG truly improves reasoning-driven generalization or simply leverages additional text-conditioned supervision.\n\n3. The improvements attributed to reasoning may partly arise from multi-task regularization and self-training dynamics rather than reasoning-specific mechanisms. For example, MTCT’s stabilization could be viewed as a standard auxiliary-task regularization effect. SARR’s iterative self-labeling closely resembles confidence-based pseudo-labeling, widely used in semi-supervised and DG settings.\nDemonstrating qualitative differences (e.g., changes in internal representation structure or reasoning coherence) would strengthen the claim that RD-MLDG explicitly enhances reasoning.\n\n4. The method assumes that reasoning text provides process-level invariance, but the reasoning generation pipeline (via GPT-4o) is independent of the underlying visual feature encoder. This leads to potential semantic misalignment between visual features and textual reasoning tokens. While the model benefits empirically, the lack of explicit cross-modal alignment may limit scalability to other MLLM architectures or reasoning tasks.\n\n5.  The four benchmarks (PACS, VLCS, OfficeHome, TerraIncognita) mainly test appearance-level domain shifts. Thus, the improvements reflect robustness to visual variation, not necessarily reasoning robustness. Experiments involving compositional or conceptual domain shifts (e.g., cross-task reasoning transfer or counterfactual settings) would better substantiate the central claim that reasoning contributes to domain generalization.", "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": "tq7OxXZUbi", "reviewer": "Reviewer_dvM6", "summary": "This paper proposes **RD-MLDG**, a *reasoning-driven multimodal large language model (MLLM)* framework designed to improve **domain generalization (DG)** — the ability of models to perform well on unseen domains. Unlike prior DG methods that focus primarily on learning invariant visual representations, this work leverages **reasoning chains** that explicitly describe class-relevant visual evidence to encourage *process-level* generalization.\n\nTo support this, the authors introduce **DomainBed-Reasoning**, an extension of the DomainBed benchmark that pairs each image with structured reasoning chains generated by GPT-4o. Through empirical analyses, they identify two key challenges:\n1. **Optimization difficulty** — supervising models with reasoning chains is harder than optimizing for direct label prediction.\n2. **Reasoning-pattern mismatch** — the reasoning patterns generated by large language models differ from those produced by the fine-tuned model.\n\nTo address these issues, the paper introduces two components:\n* **MTCT (Multi-Task Cross-Training):** jointly optimizes classification and reasoning objectives to stabilize training.\n* **SARR (Self-Aligned Reasoning Regularization):** a self-labeling approach that aligns the model’s reasoning style while maintaining semantic richness.\n\nExperiments on PACS, VLCS, OfficeHome, and TerraIncognita demonstrate that RD-MLDG achieves **state-of-the-art performance** (average 86.89%), outperforming prior DG methods based on non-MLLM architectures.", "questions": "- Line 053: Please ensure the sentence ends with a period.\n- Line 318: The description of how reasoning chains are combined with the classification prompt is unclear.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "* **Clear problem identification and analysis:** The authors systematically diagnose optimization and reasoning-pattern gaps through quantitative studies (e.g., token probability and entropy analysis).\n* **(Minor) Dataset contribution:** DomainBed-Reasoning provides a useful benchmark for reasoning-based domain generalization and may foster further research, even if its construction is relatively straightforward.", "weaknesses": "* **Limited methodological novelty:** The distinction between MTCT and SARR losses is unclear, as they appear almost identical. In particular, the novelty of SARR seems limited, resembling a form of rejection sampling.\n* **Evaluation scope:** The experiments focus mainly on visual classification tasks; it remains unclear whether reasoning-driven DG generalizes to other multimodal tasks (e.g., VQA, image-text retrieval).\n* **Baseline coverage:** Comparisons are mainly against non-MLLM DG methods, making it difficult to assess the true advantage of RD-MLDG within the MLLM landscape.\n* **(Minor) Writing:** The introduction could benefit from citations to recent survey papers summarizing reasoning-based or MLLM-based DG approaches.", "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": "bYtbm2M8Dp", "reviewer": "Reviewer_jvkU", "summary": "The paper introduces RD-MLDG, a framework that leverages reasoning chains in MLLMs to improve robustness under domain shift. The authors construct DomainBed-Reasoning, an extension of DomainBed in which each sample is paired with structured reasoning chains generated by GPT-4o. The authors propose two modules: Multi-Task Cross-Training (MTCT), which stabilizes reasoning-chain learning via an auxiliary classification pathway, and Self-Aligned Reasoning Regularization (SARR), which iteratively incorporates self-generated reasoning chains aligned with model predictions. Experiments on four DG benchmarks demonstrate state-of-the-art performance, with detailed ablations and analyses supporting the method’s contributions.", "questions": "- Could you provide more details on reasoning-chain generation, such as average number of reasoning chains generated per instance, rejection rate, and filtering criteria?\n- Could you clarify in Table 1, for MLLM-based methods, which results are zero-shot and which are fine-tuning-based?\n- Do you expect RD-MLDG to extend beyond image classification to other tasks? Additionally, given that preparing reasoning-chain supervision is non-trivial and both MTCT and iterative SARR introduce added fine-tuning steps, can you share your thoughts on the efficiency and scalability of the framework when applied to larger datasets?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper makes good contribution by explicitly incorporating reasoning into domain generalization, an area where most prior methods have focused solely on feature-level invariance. The construction of DomainBed-Reasoning provides a valuable new benchmark for studying reasoning under domain shift, while the proposed RD-MLDG framework is empirically effective. The analysis of optimization gaps and reasoning mismatches is thorough and convincing, and the introduction of MTCT and SARR demonstrates clear performance improvements across multiple benchmarks.", "weaknesses": "- Dataset construction transparency: While DomainBed-Reasoning is central, the paper lacks sufficient details on reasoning-chain generation such as rejection sampling and filtering.\n- Baseline fairness: Comparisons to prior DG methods and GPT-4o appear favorable, but it is unclear whether all baselines were fine-tuned under identical settings (prompt design, data splits, training budgets). For example, in Table 1, mixing zero-shot GPT-4o results with fine-tuned InternVL models may inflate apparent gains.\n- Limited task scope: The study focuses exclusively on image classification. It remains uncertain whether the proposed framework generalizes to other multimodal DG tasks. Also although LoRA adapters are used, both MTCT and iterative SARR introduce added fine-tuning steps. Training efficiency and scalability to larger datasets are not fully addressed.", "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)