Turning Internal Gap into Self-Improvement: Promoting the Generation-Understanding Unification in MLLMs
Authors: Yujin Han
Hao Chen
Andi Han
Zhiheng Wang
Xinyu Liu
Yingya Zhang
Shiwei Zhang
Difan Zou
Venue: ICLR 2026 Poster (2026)
Keywords: MLLM
Self-improvement
Unification
Primary area: foundation or frontier models, including LLMs
TL;DR: We systematically explore the internal gap in unified MLLMs, which typically manifests as understanding being stronger than generation, covering empirical validation, mitigation methods, mechanistic analysis, and the design of improved approaches.

Abstract: Although unified MLLMs aim to unify generation and understanding, they are considered to exhibit an internal gap, with understanding outperforming generation. Through large‑scale evaluation across multiple MLLMs and tasks, we confirm the widespread non‑unification of MLLMs, and demonstrate that it indeed stems from weak generation rather than misunderstanding. This finding motivates us to propose a simple yet effective internal gap-based self-improvement framework, which mitigates internal gaps by leveraging stronger understanding to guide weaker generation without relying on any external signals. We validate this strategy through comprehensive experiments: scoring generations with understanding to construct image data for post-training (e.g., SFT and DPO) significantly improves generation while promoting unification. Furthermore, we empirically discover a co-improvement effect of such self-improvement, a phenomenon well known in pre-training but underexplored in post-training. Specifically, as generation improves, understanding becomes more effective at detecting false positives that were previously misclassified as prompt‑aligned. To explain this effect, we extend learning dynamic theory to the MLLM setting, showing that the shared empirical neural tangent kernel between generation and understanding encourages aligned learning dynamics, thereby driving co-improvement. This interplay between generation and understanding further motivates a curriculum learning approach for stronger self‑improvement: progressively enhanced understanding and generation revisit samples underutilized by pre‑trained MLLMs, dynamically expanding post‑training data and leading to improved performance and unification.

Published as a conference paper at ICLR 2026
TURNING INTERNAL GAP INTO SELF-IMPROVEMENT: PROMOTING THE GENERATION-UNDERRSTAANNNNG UNIFICATION IN MLLMS
Yujin Han 18 Hao Chen?, Andi Han Zhiheng Wang Xinyu Liu? Yingya Zhang?, Shiwei Zhang*t Difan Zou
'The University of Hong Kong. CCarnegie Mellon University, Universitt of Sydney. 'RIKEN AIP, "Shanghai Artificial Intelligence Laboratory, "Shanghai Jiao Tong University. 'Hong Kong University of Science and Technology, "Alibaba Group
ABSTRACT
Although unified MLLMs aim to unify generation and understanding, they are considered exhibit an internal gap, with understanding outperforming genera- tion. Through large-scale evaluation across multiple MLLMs and tasks, we con- firm the widespread non-um con-unification of MLLMs, and demonstrate that it indeed stems from weak generation rather than misunderstanding. This finding motivates us to propose a simple yet effective internal gap-based seff-improvemmnt frame- work, which mitigates internal gaps by leveraging stronger understanding to guide weaker generation without relying on any external signals. We validate this strat- egy through comprehensive experiments: scoring generations with understand- ing to construct image data for post-training (e.g., SFT and DPO) significantly improves generation while promoting unification. Furthermore, empirically discover co-improvement effect of such self-improvement, a phenomenon well known in pre-training but underexplored in post-training. Specifically, as genera- tion improves, understanding becomes more effective at detecting false positives that were previously misclassified as prompt-aligned. To explain this effect, we extend learning dynamic theory to the MLLM setting, showing that the shared empirical neural tangent kernel between generation and understanding encour- ages aligned learning dynamics, thereby driving co- co- improvement. This interplay between generation and understanding further motivates a curriculum learning ap- proach for stronger self-improvement: progressively enhanced understanding and generation revisit samples underutilized by pre-trained MLLMs, dynamically ex- panding post-training data and leading to improved performance and unification.
INTRODUCTION
Unified Multimodal Large Language Models (MLLMs) have attracted growing attention for their capability to conduct both generation and understanding (Xie al. 2024; Wu et al., 2024a Wang let al. 2024 Team 2025a| Zhou et al. 2024: Chen et al. (025a). However, an emerging consensus that, despite being designed to unity both generation and understanding, they are not truly unified in performance (Yang et al. 2025) Mao et al. 2025 Hong et al. 2025 Yan et al., 2025 where understanding typically outperforms generation (Yang et al.) 2025). For example, Fig. shows, an MLLM's generation may be judged as prompt-misaligned by its own understanding branch, revealing an internal generation-undersandiin
gap. natural question arises:
Can the internal gap in MILMS be leveraged as free bonus, with the stronger branch guiding the weaker one to improve the model's performance and mitigate non-unificane con-unification?
"Eail to: Yujia Han (yujinhan wonnect..o....htteee
1 "Correspondence Shiwei Zhang (zhangjin.zswe alibaha-inc.com) and Difan Zou (dzou Meoouu@ss.kku.hi

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Figure 1: Illustration of MILMss internal gap. We examine a challenging case (Han et al., 2025) involving implicit physical principles using ChatGPT o3 (OpenAT, (024) and find: images produced by generation branch are identified incorrect by understanding branch, showing non-unification.
Prior works have discussed the internal gap in unified MLLMs, but their mitigation methods of- ten rely on external reward models (Yang et al. 2025) or additional supervised datasets (Mao et al. 2025), or focus solely on improving single task, e.g.. generation (Tiang et al.. 2023 Yan et al. 2025 Xie et al., 2025), without emphasizing generation untrriindddds alignment. In this paper, we ex- plore the potential of mitigating MLLMs' non-um uunifiiiattion without any external signals, and propose simple yet effective internal gap-based seff-improvement framework. We further provide a detailed analysis of the dynamic interplay between generation and understanding during self-improvement, offering strong complement to existing studies.
We begin by validating the generation-understandiii gap across multiple MLLMs and tasks. We first introduce internal metric, non-unification score, defined as the proportion of cases where the understanding branch judges the generation as crompt-misaligned
Unlike previous unification
metries that rely on external estimator (Yang et al., [225|] Mao et al., 2025). metric directly quantifies the internal consistency between two branches, avoiding biases from external assessment. Comprehensive evaluation on six unified MILMS and tasks of three difficulty levels shows that non-unification is pervasive, with non-unification score reaching up to 60%. Further quantitative analysis attributes most misalignments (60-100%) to weak generation rather than misunderstandingg consistent with prior findings on single tasks Yang et al. (025) and single models (Mao et al. 2025
After confirming widespread non-unification and stronger understanding, we propose an internal gap-based self-improvement framework that aligns MLLMs by leveraging stronger understanding to guide the weaker generation. We validate its effectiveness on mainstream MLLMs such as Janus- Pro-7B (Chen et al. 2025b): using the understanding branch to score generations and construct post- training data for generation, standard pipelines, e-g., SFT (Brown et al.] 2020 Radford et al.) 202T) and DPO (Rafailov et al., 2024), significantly boost generation (up to +20% on T21-CompBench++
(Huang et al., 2025)) and reduce the internal gap (non-unification score by as much as -16%) )) surpassing even baselines with multiple external reward models such as T21-R1 (Jiang et al. 2025
Furthermore, we empirically observe a co-improvement effect: the generation-targeted self- improvement method also enhances understanding. Specifically, self-improved MLLMs better de- tect false positives, i.c., samples previously misidentified as prompt-aligned. While co co-improvement
is well-known in pre-training (Tong et al. 2024 Wu et al. 2025a Deng et al. 2025 Zhang al.,
2025
Wu et al. 20255), it remains underexplored in post- (Yang et al. [[225 Mao et al.
2025 To explain it, we extend learning dynamic theory (Ren & Sutherland 2025) to multimodal
settings and formalize joint evolution of generation and understanding during self-improvement.
Our theory reveals shared empirical neural tangent kernel (eNTK) facilitates consistent learning dynamics across generation and understanding. Consequently, aligned dynamics reduce misaligned generations and enhance misalignment detection, thus leading to co-improvement effect observed.
Finally, motivated by the co-improvement effect, we further demonstrate that curriculum learn- ing (Elman) 1993, |Begiio et al. 2009) can be incorporated into self-improvement by gradually introducing harder samples that were initially excluded due to limited capabilities in generation or understanding. Experiments show that curriculum learning enables self-improvement to dynami- cally expand post-training data, further enhancing both the performance and unification of MLLMs.
Through a systematic exploration of MLLMs' internal gap, our contributions are as follows:
We first introduce the non-unification score, an internal consistency metric to measure MLLMs' internal gap. Extensive evaluations across diverse models and tasks confirm pervasive non- unification phenomenon, which is primarily caused by weak generation.
Motivated by non-unification in MLLMs, we then propose a simple yet effective internal gap- based self-improvement framework, which leverages stronger understanding capability to guide
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Confounder: Task Difficulty Level
Fasy Two dej.
Medium Spenill
Single Olj
Color Att.
Texture
Complex
Plysics
Cheminy
Biology
EMUS
IMG
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Show
VILA-11
Example (Easy Hard)
Pro
45.33
Easy: Diee
Handt Refiersis
Handt:
BAGEL
Generation
Understansdnng
Uhi
Figure 2: Verification of internal gaps. (a) and (b) identify task difficulty as a confounder in measur- ing non-unification score (Non.): easy tasks may underestimate the gap, while hard tasks risk overes- timation. Stratifying by task difficulty (Fasy-Medium-Hane Evaluation of six MLLMs across three difficulty levels shows unified MLLMs remain non-unified, with non-unification scores approaching 60%. More details are provided in Appendix A.
yields a more reliable estimation. (c)
the weaker generation. Extensive experiments show the proposed self- seff-improvement significantly boosts both generation and unification without external signals.
In self-improvement, we empirically identify a co-improvement effect, where understanding better detects prompt-misaligne generations. Extending learning dynamics to MLLMs, we attribute this effect shared eNTK between generation and understanding.
Finally, co- co-improvement effect inspires currieulum-bass self ceff-improvement strategy: progres- sively strengthen understanding and generation enable reusing underutilized samples, thereby expanding post-training data and boosting both performance and unification.
2 RELATED WORK
Non-unification of MLLMs. There are works showing internal gap of MLLMs, typically with understanding outperforming generation (Yang et al. 2025 Mao et al. 2025 Hong et al. 2025 Yan et al. 2025 Yang et al.) (2025). However, existing studies lack systematic quantification of such gap across multiple MILMS and tasks, with conclusions often confined to single models (Mao et al., 2025) single tasks (Yang al. (025).. Additionally, their measurements of internal gap rely external models, e.g., ChatGPT (Yang et al. (2025; Mao et al. (2025) instead of measuring internal consistency, which potentially makes biased estimation by external evaluators. Therefore, we first focus on introducing non-unification metric and performing large-scale verification.
Mitigating Non-unification of MILMs. Several studies attempt mitigate internal gap within MLLMs, but they rely on external models (Jiang et al.] 22225) Yang et al., (025) or additional data (Mao al. 2025). For example, Hermesflow (Yang et al. (025) leverages external Bert (Devlin) et al. 2019) for understanding, self-critique and VQA AAtol et al. 2015) models for generation, to improve both branches. Other works (Tiang et al., al.] 2025 Duan et al. (2255) enhance weaker gener- ation by introducing multiple external reward models, e.g., BLIP (Li al. 2022) and HPMs (Wu) jet al., 2023 Xu et al. 2023). In contrast, we focus on mitigating internal gap purely through self- improvement without any external signals. Importantly, self inffimmproveeent does not conflict with existing approaches: once achieved, external signals can be incorporated to further boost MLLMs.
Co-improvement of MLLMs. Co-improvement in unified MLLMs often refers one branch im- proving when the other is improved, such as understanding gains from adding more generation data (Tong al.
2024
Wu et al. 2025a). This phenomenon has been widely observed in pre-training Wu et al. 2025a Deng et al.) 2025 Zhang et al. 2025 Wu et al.] (0255). yet it
(Tong et al.
2024
been sufficiently highlighted thoroughly analyzed in post-training (Yang et al. 2025 Mao
et al. 20255 Hong et al. 2025). Our work provides learning-dynamie perspective on it, offering
insights into interplay between understanding and generation in unified MLLMs.
PHENOMENON VERIFICATION THE NON-UNIFICATION IN MLLMS
While prior work suggests internal imbalances in MLLMs, this claim remains unverified through evaluation across diverse models and tasks (see Section 2). We therefore take the large-scale empir- ical verification of non-unification the starting point of our study.
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We first propose self-consistency metric quantify the generation generation-unddrstaatii understanding gap, termed the non-unification score. Specifically, consider an MLLM TT. a prompt y and the generated image x = Th" (y). We form image question pair (x, ())),, where q(y) == "Does this image describe y?" This pair is processed by the understanding branch my yielding binary decision: 1 if x is aligned with y, and O otherwise. The non-unifien non-unification score is the proportion of decisions equal to 0,
Non-unification score :- Ex m (x, q(y)) 0
(1)
Intuitively, unified MLLMs should have near-zero non-um non-unification score: generation renders the prompt as an image and understanding verifies the image matches the prompt.
We evaluate multiple MLLMs (Wang et al. 2024
Xie et al.) 2024| Wu et al. al.] 2024b; Chen et al. 2025b Deng al. 2025 Chen et al. 2025a). across tasks of varying difficulty. We emphasize that task diffi- culty is a confounder affecting both generation and understanding, thereby biasing the non-unification score (see Fig. aa). For example, as shown in Fig. 2i), a simple prompt like generate a car makes both generation and understanding easy, so the non- unification score is close to zero and may underes- timate the internal gap. In contrast, for harder tasks such generate mirror reflection, where the gen- cration branch may fail to capture latent physical rules and the score may be overestimated. Therefore, stratifying by task difficulty provides a more reliable way to estimate the internal gap. Specifically, we construct nine subtasks of increasing difficulty from
EM11
YEAU
RAGEL
100
Wedk: Ganenaiio (N)
80
70
50
40
Single Ob,. Testuro
Plysics
Figure 3: Weak-gennrattnn (Qwen-checked)
above 50% (even 100%) indicate internal gap mainly stems from weak generation. Fig. [1 provides human check, showing conclusions consistent with Qwen. Appendix [A.2 reports more Weak Generation results.
three benchmarks (Ghosh al. 2023 Huang al. [0255] Li et al. 2025), ranging from simple case (e.g., a cat) to complex prompts with implicit rules (c.g., ice at 60 C).. Detailed tasks and MLLMs are shown in Appendix A.I
Results. Fig. [[() demonstrates that all six evaluated models exhibit larger non-unification scores on hard (5/6) and medium (1/6) tasks. In contrast, the small non-unification scores observed on easy tasks may not indicate the absence (or near absence) of an internal gap, but rather that the tasks are too simple. On VILA-U (Wu et al. 2024b), even reaches 58.47%, meaning that nearly 60% of generations are rejected (prompt misaligned) by understanding. More discussion on non-unification is provided in Appendix A.2
To further distinguish whether non-unification comes from weak generation misunderstandinng we use stronger external model, Doven2.5-VL-72B-Inmmme (Bai et al. (025), to check the accuracy of the understanding scores. Define Weak Generation as the probability that, when the MLLM's understanding branch rejects an output, its judgment agrees with Qwen, i.e..
Weak Generation := P(mm s(y)) "Qwen (x, 4(y)) "rmdd(x,, g(y)) 0)-
Fig. 3 shows, across different task difficulties, all MI.LMs achieve over 50% and up to 100% Weak Generation, indicating that the internal gap mainly stems from poor generation rather than misjudg- ments of understanding which well align with prior findings (Yang et al. 2025). Additionally, Ap- pendix provides weak generation score computed using Gemini-Pro-22. (Tea (025b) yielding conclusions consistent with Qwen: the internal gap primarily stems from weaker generation.
4
MITIGATING NON-UNIHICATIN: A SELF-IMPROVEMENT FRAMEWORK
4.1 METHOD: INTERNAL GAP-BASED SELF-IMPROVEMEN
The observation that understanding consistently outperforms generation then motivates our in- ternal gap-based self-improvement framework to promote unification of unified MLLMs, which leverages stronger understanding to enhance the weaker generation. Specifically, we adopt stan- dard post-training strategies such as Direct Preference Optimization (DPO) and Supervised Fine- Tuning (SFT). Given an image generation prompt y, the MLLM тo produces candidate im- ages, i.e. { = To(y). Each candidate x, is paired with the question g(y)
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Algorithm/ 1: Self- Cell--Imroveeentt (SFT)
Algorithm 2: Curriculum Replay
Input: ne. prompts P, image candidates N.
Input: ne. discard pool B, image candidates N,
epochs T'
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und Images judged (most "Does this image describe y?" and processed by understanding branch Te likely) as aligned with the prompt are labeled as chosen, while those judged (most likely) as mis- aligned are labeled rejected, forming preference data (y: Xchosen: -rejected) for DPO and supervi- sion pairs (y, Xchosea) for SFT on the generation branch. Appendix [. provides further details on post-training data construction, and Alg. outlines the SFT-based self-imrovvement procedure.
4.2
EXPERIMENT: EFFECTIVENESS OF SELF-IMPROVEMENT ON MLLMS
We then show effectiveness of proposed self-improvement through following experiments.
4.2.1 SETUP
Baseline and Data. To validate self-improvement, we apply it to two baselines: Janus-Pro-74 (Chen et al. 20255) and Show-o (Xie et al., 2024). We ablate which MLLM components to opti- mize (e.g., the LLM and vision aligner) and find that updating only the shared LLM yields substan- tial gains. Further details are Appendix F2 Experiments are conducted on 221-CompBench++ (Huang et al. 2025),, which provides about 6000 text prompts as post-training candidates. After data construction, classical post-training strategies, SFT and DPO, are applied for generation-focused seff-improvement. Further implementation details in Appendix B
Evaluation. We compare self-improved and pre-trained MLLMs on generation, unification and understanding. For generation, we follow T21-CompBench++ metrics and measure unification by non-unification score. For understanding, we use win rate (excluding ties) (Zheng et al. 2023 Chen al. 2024): given validation text prompts with images generated by pre-trained MLLMs, models judge prompt-image alignment. Win rate is the proportion of cases where the self- ieff-iedroved MLLM disagrees with the pre-trained one but agrees with the stronger external judge, e.g., Qwen2.5-VL- Ins-Intt For example, if the models disagree on three samples and the self-improved model matches Qwen on two, win rate is 2/3. Pre- and post-trained models with comparable understand- ing achieve a win rate of 0.5. Win rate enables tracking changes in understanding and generation on the same task, facilitating analysis two branches. Appendix B.I includes additional metric descriptions, as well as win rates obtained using additional external judges, e.g., Gemini-Pro-2.5.
4.2.2 RESULTS
We summarize key findings under SFT as follows. The corresponding DPO results, largely consis- tent with SFT, are provided in Appendix B.2.
Finding 1: Internal gap-based self-improvemen effectively improves generation and promotes MILM unification. Fig. 4 shows self-improved MLLMs can achieve up 20% gains genera- tion and up to 16% in unification, validating effectiveness of proposed method. Moreover, we find improvements in generation are significantly correlated with unification (pa.Non. 0.53). Spe- cially, for model level, Janus-Pro, Pro, with larger internal gap (see Fig. 2). achieves greater gains than
For clarity, name MI. Ms without self-improvement pre-trained ML.LMs, even if they may undergo post-training phases during training.
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Figure 4: Self-improvement enhances generation and unification, with gains to 20% and 16% ---on-unification score). Furthermore, improvements correlate with the internal gap (correlation 0.53): models and subtasks with larger gaps benefit more.
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Figure 5: The Co-improvement Effect. (a) illustrates an example where self-improved Janus-Pro generates prompt-aligned images and correctly scores the original as mismatched (see more cases in Appendix [B; (b) reports win rates mostly above 50%, showing self-improved MLLMs judge prompt-image alignment more accurately than pre-trained ones.
Show-o with a smaller gap. For task level, subtasks with lower unification (e.g., Texture) ben- efit more. We attribute this to internal gap-based method encouraging more post-training samples from sarger-gap subtasks, thereby enabling greater improvements. Fig. 12 further confirms this by showing post- post-training training data contain higher proportion of samples from larger-gap subtasks.
Generation-targetees self-improvement also enhances understanding, showing co-improvement effect. Fig. 5]a) shows an example that, in addition to generating more prompt- aligned images, the self-improver MLLM also better detects mismatches between the original image and the prompt. Fig. 5(b) further reports high win rates for Janus-Pro and Show-o across six sub- tasks. For instance, the self-improved Janus-Pro achieves a win rate above 50% 5 of 6 subtasks, indicating higher accuracy than its pre-trained counterpart in judging prompt-image alignment. Ad- ditionally, we also provide results on standard understanding benchmarks Table g] where self- improved MLLM consistently outperforms the pre- pre-trained model. Furthermore. in Appendix F.T we discuss additional unified MI.LMs, such as BAGEL (Deng et al., 2025), which adopt different architectures (e.g., MoT rather than shared LLM),, and present their self-improvement results.
Finding 2:
UNDERSTANDING CO-IMPROVEMENT SELF-IMPROVEMENT
Section 4.2.2 reveals co-improvement effect in seff-improvement, an underexplored phenomenon in unified MILMS (see Section 2). Understanding this effect is crucial, as it highlights the unique interplay between generation and understanding and may inspire more effective self- self-improvement.
5.1 LEARNING DYNAMICS OF GENERATION AND UNDERSTANDING
We extend the learning dynamics framework (Ren & Sutherland 2025) to the multimodal setting, as it provides principled way analyze how MLLMs лo evolve after self-improvemment
post- training data (y., ). Specifically, the framework helps to answer: (1) Generation: given a text input yo, how generated images of the self-improved model differs from that of the base model;

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(2) Understanding: given image imput Xo, how understanding output of the self-improved model differs from that of the base model.
Suppose Xo (from the pre-trained MLLM) and Yo misaligned. If generation and understand- ing share aligned learning dynamics, e.g., jointly decreasing incorrect generation To (xolyo) and misunderstanding To (yo Xo), the co-improvement occurs.
Settings. We first consider the setting where generation and understanding share the same tokenizer, as in Show-o and EMU3 (Wang et al., 2024). This contrasts with decoupled designs (e.g., Janus- Pro) that use separate tokenizers. Nevertheless, our later analysis in Section 5.2 indicates that the conclusions drawn under the shared-tokenizer assumption also apply decoupled architectures. Additionally, our theoretical framework can also be extended to MLLMs that employ diffusion models for modeling continuous image tokens (Xie et al., 2024 Zhou al. 2024). Then, we denote V as the unified vocabulary of text and image tokens with size (Yo, Xo), with image token sequence X (fo.1,..... To.as) of length M and text token sequence Yo = (..,,,...: Vo. of length L, our goal is to analyze how MLLM's generation and understanding outputs on (Xo: yo) change after self improveept on the post- pest-training sample (y X.
| Given validation example
Following Ren Sutherland (2025), we adopt standard causal masking in MLLMs (Wu et al. 2024a Wang et al. 2024, Wu et al. 2025b) and define the input to generation branch as Vo yo Xo E Rdx(1+= and input to understanding branch as X = [xo yo E (M++)
We denote the logit network as hg. which outputs understanding and generation logits z! he (Xo)), M+1:M+1) and zgnn = (ho()))], ++1+++M) respectively. We define the likelihood of sam-
plc (Yo, Xo) under generation and understanding branch with shared LLM as
(xo 11))=
II (Zo., No,No, Am
II Rm
[softmax(2)) (0nd)) 20.a.
(Generation)
1.
(y ()) - II no (Mo.e xo No.ce)
II
[softmax(2]) (2 vo
(Understanding)
where the softmax is applied cosemn-wise.
One-step learning dynamics. At epoch t, we define the one-step learning dynamics of eval- uation data pair Xo) likelihood after training one-step on post-training data (Y.., x.) as AG,(xo Pb)= = log T (xo (1) (an)-looye (ox 1%) for generation branch and AU:(Yo Xx) ==
log (Yu (g)) - log (Yu .%) for understanding branch. We consider self-improvement with SFT and relate the dynamics of understanding and generation in the following proposition. Self- improvement with DPO are analyzed in Appendix[D2.
Proposition (Learning Dynamics of Generation and Understanding under SFT). Consider self- improvement proposed in Section 4 with SFT and at epoch l.
The one-step learning dynamics of generation is
M M
AGi(Xo 126) = 11 Σ Σ (Cro. - T TOT (Do, - cr.,,) + 0
(2)
where ग. = softmax(z,) and n = [ho (Ju)) are the logits at position r obtained by rumning he on Ya and K%,,, (Yo, V.) = (Vo, 2.) 2
VVV is empirical neural tangent kernel (eNTK).
The one-step learning dynamics of understanding is
AUMyo | (b))
11
(y: xo) (ce.) #2) K..
(cm. x (i. c....)
y.
+ +(n")
where wo. (y xx)
Ey ne, (Xoly') and Vd denotes the concatenation of prompt y; + yo and xo.
no, (xall)
2 "Appendix provides more detailed preliminaries.
We omit potential special tokens (e.g., [SO1]) for simplicity.
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Sintarry
Densiee
Yo
fandom (Label. Pre trained, Selt improved) (ro. xo) similar (Ys.
Paity K31 X.)
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(b) Proxy [3K Vallle C3. Vulllr
(c) False Correction Group: AG.
Figure 7: Empirical Evidence from Self-improved Janus-Pro with SFT. (a) Compared to random samples, (yo, 3 the false positive correction group are more likely be matched with highly similar post-training pairs (Yu, Xu). (.). (b) Such high data similarity makes X (Jo, Vi) be the dom- inant term in Equation (3). thereby promoting aligned learning dynamics AG, and AU, (c) With aligned dynamics, AG, implies AU, < O: both the probability of mis-generation me (Xn Yo) and misjudging Te (yo xo). are reduced, i.e., false positive correction and co-improvement occur.
Proposition I shows the learning dynamics of generation (AG, in Equation (2) and understanding (AU, in Equation (3) are similar. The key difference is that AU; includes an additional eNTK term, (J,, which measures alignment between }/8 (i o 0) and the post-training data V
We therefore hypothesize: for co-improved pair (Yo, xu). there likely exist post-training samples (yu:x.) that are highly similar, leading [| (D.. (..P)) PullF 2 (D,. V.)lF.. Hence, un- derstanding update AU, in Equation 3 is dominated by (Do. which a shared eNTK term with the generation update AG, in Equation 2 Aligned updates between generation and understanding, i.e., aligned AG; and AU., can jointly reduce the probabilities of mis-generation (x Yo) and misunderstanding Ty(Yo Xo), thereby yielding co-improvement.
To test this hypothesis, we combine empirical evidences from Section 4.2 with theoretical results in Proposition [ and empirically examine: for sample (yo, Xo) of which understanding improves, whether there exist similar post-training samples (y,, X.). Such similarity may render AU, dom- inated by the eNTK term k (W). which is shared by both generation and understanding branch, thereby aligning the updates of the two branches, i.c., aligned AG, and AU,
5.2 EMPIRICAL EVIDENCE
First, samples where understanding improves be classified into two cases: (1) False Positive Correc- tion: when image Xo and text Yo actually mis- aligned (Qwen label= = 0), pre-trained MLLMs incor- rectly judge them aligned (score 1), while self- improved MLLMs correctly predict misalignment (score O): (2) False Negative Correction: when Xn and yo are aligned (Qwen label = 1), pre-trained MLLMS incorrectly predict misalignment (score 0), while self-improved MLLMs correctly judge alignment (score 1). Using self- celf-improvement with example, Figure osoows shows ap- proximately 80% of the understanding improvement originates from case (1), i.e., false positive correc- tion.
(Labe), Pro-trained, Solf-improved) 11
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69
(I Pre-trained, improwed) (0.1.0)
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SFT Janus-Pre
Figure 6: On T21-CompBench++, under- standing gains primarily (80%) arise from false positive correction See Appendix [
for results on additional MLLMs.
Our verification below mainly focuses on false positive correction. Specifically, consider prompt yo and misaligned image X9 (generated by pre-trained Ms..Ms).
identifies, for each (yu. Xy) in the false-positive correction group, its most similar counterpart in the post-training data, showing that these samples typically have higher-similarity bost-training pairs (y... x.). particular, the prompt yo attains an average similarity of about 0.8, significantly higher than randomly sampled reference.
Fig. a
8

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Published as a conference paper at ICLR 2026
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Table I: Curriculum learning-based self-improvement (C-SFT) yields better generation (higher Gen.) and understanding (higher Und.), and alleviates non-unification (lower Non.). which even surpasses baselines rely on external reward models, such as T2I-R1 (built on Janus-Pro-7B) and HermesFlow (built on Show-o). Additional post-training strategy, c.g., DPO, and evaluations on more benchmarks are provided in Appendix C.2
Fig. (b) supports that, the understanding branch of data in false positive correction group is dominated by the eNTK term JC (O', Vu)- Since Vo is the concatenation of yo and Xo, use the similarity between (yo, xo) and its nearest post-training counterpart (yu, xy)) as proxy for
the eNTK. Based on proxies, we consistently observe ( VWll 2 || (1%, (.))
Fig. (ec) shows, for false positive correction samples, the generation update satisfies AG, < O, i.e., the mis-generation probability [x | yo) decreases. Combined with Fig. (b)),,, this further implies AU, < O, meaning the misunderstanding probability Ty Xo) also decreases.
The above empirical evidence supports the hypothesis derived from Proposition T explaining both the emergence of false positive correction and co-improvement. We provide details on how each empirical result, e.g., the proxy of eNTK, was obtained and interpreted in Appendix
CURRICULUM LEARNING FOR STRONGER SELF-IMPROVEMENT
Co-improvement effect motivates curriculum learning (Elman 1993 Bengio et al. 2009) ap- proach for stronger self-improvement:: as generation and understanding improve together, difficult samples that pre-trained MLLMs could not previously utilize (due to weak generation or inaceu- rate understanding) can be incorporated later, forming adaptive data expansion process based on prompt complexity (Li & Zhang, 2025). To demonstrate co-improvement incorporates more unused prompts, compare two settings: (1) jointly improving generation and understanding, and (2) en- hancing only a single branch (e.g., generation). As shown in Table 2 co-improvement contributes about 1000 additional samples from discard pool B (defined in Alg. versus roughly 600 for single- branch enhancement, supporting our motivation. Alg. showw details of curriculum learning.
Setup. Following the experimental setup in Sec- tion 4.2.1 we adopt self-improvement with curricu- lum learning strategy. For Janus-Pro and Show-o, curriculum learning is introduced at epoch 10, during which the models regenerate and rescore previously unused prompts to produce additional post-training samples. Evaluation follows the same metrics in Section 4.2.1 We provide more imple- mentation details in Appendix [[and ablation study in Appendix F.4
Und.
Self-improved Und.
Gen. Self-improved Gen. 603
0
649
1091
Table 2:
Co-improvement (self-improved both Und. and Gen.) adds 1091 samples from discard pool B, compared to roughly 600 when improving only single branch.
Baseline. Apart from generation-only models, e.g., SDv1.5 (Rombach et al. 2022). we consider two unified MLLM baselines: T2I-R1 (Jiang et al.] 2025) improves generation of Janus- Pro- 7B by using multiple external reward models and provides comparison for Janus-Pro-7B -B--eeB self- improvement. And Hermesflow (Yang et al. (025) similarly employs external reward models, e.g., Bert (Devlin al. 2019), enhance Show-o, serving as a reference for Show-o-based approach.

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Published as a conference paper at ICLR 2026
Results. We report only SFT-based self-improvement with curriculum learning (denoted C- SFT) on 121-CompBench++ evaluation set. Results for DPO-based method and additional bench- marks, such as GenEval (Ghosh et al., 2023) and Science-T21 (Li et al. 2025), are provided in Appendix C.2 As shown in Table [ incorporating curriculum learning enables unified MLLMS achieve stronger self-improvement: compared with standard self-improvement, C-SFT' delivers consistent gains in generation, understanding, and unification across most tasks, even surpassing baselines that rely on external rewards, such as T2I-RI and Hermesflow. These results confirm the effectiveness of incorporating curriculum learning into the self-improvement process.
CONCLUSION AND LIMITATION
This paper systematically investigates interration-undesttaaniiin gap in MILMs through empirical validation, mitigation, mechanistic analysis and improved method design, showing gap-based self- improvement mitigates non-unification and induces co-improvement between the two branches.
This work has the following limitations. First, our exploration is restricted to limited unified MLLMs, such as Janus-Pro and Show-o. Second, we attribute the observed co-improvement to shared eNTK between generation and understanding. A deeper question, however, is why such NTK sharing arises in unified MLLMs, which calls for further investigation into model's mechanisms.
REPRODUCIBILITY STATEMENT
We provide experimental details in Appendix Appendix B and Appendix including evaluation tasks and hyperparameters, ensure the reproducibility of experiments in Section 3 Section f and Section 6 The proof derivations are presented in Appendix D and Appendix to guarantee the reproducibility of theoretical results in Section
ETHICS STATEMENT
This work aims to explore and mitigate the internal MLLMs. All experiments are conducted on publicly available datasets and open-source models, ensuring that no private or sensitive data are involved. Our study focuses on explaining the phe- nomenon and developing mitigation methods, and does not directly deploy downstream applications that could raise ethical concerns.
gap in unified
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Appendix
CONTENTS
Additional Details and Full Results on Internal Gap
16
A.1 Additional Details
16
Full Results
17
Additional Details and Full Results on Self-Improvement
19
B
Additional Details
19
2
Full Results
21
Additional Details and Full Results on Curriculum-L.arrinng -aaeeennnnn---eeddddd Self- Ioaf-veproveentt
22
Additional Details
23
2
Full Results
24
Understanding Co-improvement in Self-Improvement
24
D.1
Full Theoretical Analysis under SFT
24
D.2
Full Theoretical Analysis under DPO
25
Derivations and Proof Details
27
Learning Dynamics under SFT
29
F.2
Learning Dynamics under DPPO
31
Ablation Study and More Explorations
32
Self-improvements Additional Models
32
Ablation on Updated Model Components
33
Ablation on Image Candidates N
33
F4
Ablation on Curriculum Learning
33
F5
Improvement with External Reward
33
The Use of Large Language Models (LLMs)
33
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A ADDITIONAL DETAILS AND FULL RESULTS ON INTERNAL GAP
A.1 ADDITIONAL DETAILS
In this section, we provide an overview of MLLMs and tasks evaluated in Section Unified MLLMs aim integrate generation and understanding, with common approaches including extending un- derstanding MLLMs with external diffusion models for generation (Dong et al. 2023; Tong et al.,
2024 Ge et al. 2024, Yang et al., 2024; Tian al., 2024 Chen al.) 2025a Xie et al. (024).
or representing both images and text as discrete tokens and training unified transformers under au- toregressive paradigm (Team] 2025a Zhou al. 2024 Qu et al. 2024, Chen et al. 2025b) Wang et al.
e 2024). Despite aiming to unity tasks, most MILMMs emphasize single-task SOTA performance while overlooking models' internal alignment. Intuitively, truly unified MILMS should maintain in- ternal consistency between generation and understanding. Therefore, we first quantify at scale the non-unification problem unified MLLMs.
Evaluated MLLMs
Our evaluation covers the following MLLMs:
EMU3 (Wang et al. 2024 unified model for both generation and understanding, which converts multiple modalities such as images, text, and video into discrete tokens, and performs next-token prediction in mixed multimodal sequences based on an LLM-style transformer archi- tecture. EMU3 pursues maximal architectural unification between generation and understanding, sharing the same image tokenizer for both tasks and employing a common LLM backbone for generation and understanding.
Show-o (Xie al., 2024) also follows an LLM-style transformer architecture and an autore- gressive paradigm. In its default setting, generation and understanding share the same visual muderstanding/geneease
encoder and LLM component. A distinctive feature of Show-o that it adopts different attention mechanisms for text and image tokens: causal attention for the for- mer and full attention for the latter. Moreover, for image tokens during training, it is modeled using discrete diffusion and incorporates mask token prediction mechanism similar to that of MaskGIT (Chang et al. 2022).
VILA-U (Wu et al., 2024b) also adopts a shared LLM and a unified next-token prediction paradigm to integrate generation and understanding tasks. To better learn the discrete token sequences resulting from concatenated images and text, VILA-U innovatively trains unified foundation vision tower by applying CLIP-like contrastive loss (Radford al. 2021] between visual and textual tokens, while simultaneously enforcing accurate reconstruction of images after the decoder. This design promotes the performance of unified MILMs.
Janus- (Chen et al. 2025b) differs slightly from the above models. While continuing to fol- low the LLM-style shared transformer and autoregressive paradigm, it emphasizes decoupling generation and understanding tasks at the tokenizer stage. By employing separate image tok- enizers for the tasks, Janus-Pro aims mitigate conflicts arising from using single unified tokenizer to serve tasks which require different representations.
BAGEL (Deng et al. 2025). in contrast, adopts an architecture that explicitly separates genera- tion and understanding. Inspired by the Mixture-of-Trantee Transformers (MoT) paradigm (Liang al.) 2025), BAGEL employs two dedicated transformer experts to handle the two types of informa- tion, respectively. The only point of interaction between the tasks through the self-attention mechanism within each transformer block, while other components, such as visual tokenizers and FFN, are fully decoupled by task.
BLIP3-o (Chen et al. 2025a), compared with the aforementioned models, adopts an even more decoupled design by combining an autoregressive paradigm with diffusion models. Specifically, BLIP3-o follows an understand-ther wewddd-tte---eeeeee then-generate pipeline: it first performs image understanding using an pre-trained understanding MLLM (c.g.. Qwen2.5-VL) to produce visual features that serve as semantic-leved conditions for the subsequent image generation task. Then, leveraging these semantic conditions, DiT (Peebles & Xiel (023) learn the distribution of the original image representations in the CLIP (Radford et al. 1002 embedding space via flow matching. During inference, a diffusion-based visual decoder will reconstruct pixel-level images from the CLIP representations generated by the DIT.
16

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Published as a conference paper at ICLR 2026
100
160
70
Tlm Oster Ah
Todleo
Speii
Comple
Poysin
Chenkey
De4kgy
Easy Task
(b) Medium Task
(c) Hard Task
Figure 8: Full Results on Weak Generation based on -2- across six MILMs and nine tasks indicates that the primary cause of non-unification is weak gener- ation, as reflected by weak generation scores exceeding 50% on the majority of tasks.
Our evaluation
LMG1
MASE:
NEAS
HARE:
RAIDA
14
DUPP
ELP7
100
66
68-
Woak
20-
01-
Spurca
Coogle
(a) Easy Task
(b) Medium Task
(c) Hard Task
Figure 9: Full Results on Weak Generation based on Gemini-Pro-2.5. Our evaluation across six MLLMS and nine tasks indicates that the primary cause of unifufiiicttinn is weak generation, as reflected by weak generation scores exceeding 50% on the majority of tasks.
Evaluated Task We select nine subtasks from three benchmarks: GenFval (Ghosh et al., 2023)
T21-CompBench+e (Huang et al.) 2025), and Science-T21 (Li et al. 2025). We then categorize subtasks into three difficulty levels Easy. Medium, Hard) according to the complexity of generation and understanding required. Table provides detailed description of each subtask. We observe that Easy subtasks focus on the generation and understanding of simple single objects, e.g., a cat. Medium subtasks introduce relatively complex understanding such as spatial relationships (e.g.. on the top of) that are typically made explicil in prompts, and Hard subtasks involve implicit reasoning not stated in the prompt, e.g., tree in winter, requiring MLLMs leverage strong prior knowledge about physics, chemistry, and biology.
A.2 FULL RESULTS
Following the non-unification score defined in Section we evaluate six MLLMs on subtasks across three difficulty levels and observe the widespread presence of the internal gap, as shown in Figure 2 In addition, we find substantial variation in non-unification across ML..Ms Show-o and EMU3 exhibit relatively small internal gaps, whereas recent models such as BAGEL and BLIP3-o have larger gaps but stronger performance (Deng et al. 2025 Chen et al.) 2025a). It should be noted that the absolute performance of an MLLM is independent of its non-un unifiiiiiitooon score. First, non-unification measures only the relative diserepancy between generation and un- derstanding, rather than an MLLM's absolute performance on each task. Moreover, differences in training configurations, such data scale and pipeline design, can make comparisons between absolute performance and the relative gap across models unreliable.
Full Results.
Stronger Understanding and Human Check. As described in Section 3 we use stronger external models, such as --eee2..--L--2--Innses-
evaluate the scores given by the understanding branch in order to identify the source of the internal capability imbalance in MLLMs, i.e., the internal gap. &
Figure presents the weak generation rates across nine subtasks based on Qwen's 's judgments. where we observe that most models exhibit more than 50% weak generation on the majority of tasks.
*Notation: external signals (ES), internal signals (IS) and curriculum learning (CI.).
'oor fair comparison, generate images for T2I-R1 directly from original prompts, without using the understanding branch for prompt expansion.
17

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Published as a conference paper at ICLR 2026
Difficulty
Task
Evaluation Size
Prompt Example
Source
Single Obj.
80
photo of cat photo of stop sign and dog
Easy
Two Obj.
GenEval
Color Attri.
100
photo of red cake and a parple chair
Texture
300
fluffy clouds and a glass table cat the top of sufa
Medium
T21-CompRench++
Spatial
300
Complex
300
The prickly green cactus contrasted with the smooth white walls.
Physics
118
A ice block at sixty degrees Celsiua, clear, simple and realistic. iron ball that has been exposed to pxygen for decades, simple, clear and realislin. A sweetgam tree in winter with high
Hard
Science-T2155
Chemistry
49
Biology
60
realism.
Table 3: Subtasks categorized by difficulty level. As shown in Table E] select nine subtasks from three benchmarks to construct evaluation data with progressively increasing generation and understanding difficulty. Easy tasks involve only object generation, while Medium prompts require both generation and reasoning over spatial relations, colors, and textures. Hard tasks contain implicit reasoning, requiring MLLMs to possess accurate prior knowledge.
Human Check (scora=0 samples) Marrsnees
Figure 10: The interactive interface used in the human-check procedure, illustrated using Janus- Pro's evaluation on the texture subtask as an example. Human annotators are asked to determine whether the indeed aligned with the prompt.
pairs flagged by the understanding branch misaligned (score 0)
should be noted that a weak generation rate of zero may arise partly from misjudgments of the under- standing branch, e.g., Janus-Pro and VILA-U in Bi- ology have nearly zero weak generation, and in other cases, e.g., Show-o, from a non-unification score of zero for that task, which naturally leads a weak generation rate of zero.
Stot--
Janu Pro
BLIPS
Weak Geneeaatinn (%)
90
80
60
To ensure the accuracy of our evaluation, we further introduce a human check. The full procedure is as follows. (1) Obtain the understanding- -branch scores. Following the definition of the non-unification score in Section 3, we first compute the understanding branch's judgment of whether each generated image satisfies its prompt. (2) Select samples predicted as incorrect (score = 0). We then collect all samples for which the understanding branch outputs score = (misaligned
50
Single
Testure
Plysics
Figure 11: Human-evaluated weak genera- tion aligns with Qwen-based results, con- firming weak generation as the primary cause of non non-unification and supporting the use of Qwen as external judges win rate.
praggpppropptt pairs). (3) Perform human re-evaluation of these score 0 samples. All selected samples manually inspected, where
18

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Published as a conference paper at ICLR 2026
annotators decide whether each image satisfies the corresponding prompt (assigning of 0 or 1). The evaluation is conducted by two PhD-level annotators: one performs the initial annotation, and the other verifies ensuring accurate understanding of both prompts and images. (4) Compute the human-evaluated weak generation score. We the calculate the human-evaluated weak generation score which measures the probability that humans agree with the understanding branch conditioned on the branch predicting score = O, i.e.,
Human-evaluated Weak Generation PP (m (x, q(y)) Shuman (x, q(y)) mb (x, g(y)) 0).
where Snuman denotes the human score.
Figure I further presents weak generation results based on human evaluation, which yield con- sistent findings: MILLMs achieve weak generation scores exceeding 50% the majority of tasks, further emphasizing that non-unification primarily stems from weak generation rather than misun- derstanding. Moreover, the weak generation scores obtained from human evaluation are closely aligned with those derived from Qwen-based evaluation, with an average score difference of 1.01 % for Easy tasks, 8.21 2 for Medium tasks, and 19.67% for Hard tasks. The relatively larger discrep- ancy for Hard tasks may indicate that Qwen also faces limitations in understanding images involving implicit reasoning. Nevertheless, the overall agreement between human evaluation and Qwen as- sessing MILMs supports the continued use of Qwen as an external judge in subsequent studies, such as evaluating the win rate for understanding Section 4.2.T
B ADDITIONAL DETAILS AND FULL RESULTS ON SELF-IMPROVEMENT
B. ADDITIONAL DETAILS
Data Details. We use approximately 6000 prompts from T21-CompBench++ (Huang et al. 2025) training data, where strictly follow the official data split defined in 121-CompBench++ to obtain the training and evaluation sets.
Implementation Details. During the construction of SFT and DPO datasets, we feed each input image together with its corresponding question:
Question
You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language. Does this original image describe {prompt}? If it describes the scene, score I; if it does not fully describe, score O. Please answer the following format: The score {your score}.
We record the prediction probability from the understanding branch and select the image with the highest predicted probability of [your score} highest predicted probability {your score) = as the rejected sample. The chosen images are used both as positive samples for DPO and as SFT samples, whereas the rejected images are used as negative samples for DPO. worth noting that, for DPO, we adopt the common practice of applying the negative ligeeiidddoodd (NLL) loss (Pang et al.) 2024: Dubey et al.) (024) over the preferred response each pair, in order to enhance DPO. We conduct self-improvement on Janus- Pro-7B and Show-o (option (a) and 512 x 512) using four 80 GB NVIDIA A800 GPUs, with self- improvement epochs set to 20 for SFT and 30 for DPO, respectively. Self-improvement requires approximately 7-8 hours. The detailed hyperparameter configurations presented in Table 4
as the chosen sample, and the image with the
Evaluation. In addition to evaluating the self-improved MLLMs on the validation set of T21- CompBench+++, we also conduct evaluations on GenEval and Science-T21. As introduced in Ap- pendix A.I GenEval is a relatively simple benchmark focusing object and its basic attributes, whereas Science- T2I involves more complex prompts that require implicit reasoning. For image generation metrics, we follow the evaluation protocols and metric definitions specified by each
"Our implementation is based on http://imibcommKKKAAiiaanaaaaagn "Our implementation is based on http://ggnnhooooo//yyyyy//////nn
19

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Published as a conference paper at ICLR 2026
Hyperparameter
Janus-Pro-7Bt
Show-o
Optimization
Optimizer
Adam
Adam
Learning rate
x 10-7
x 10-5
Adam(W) 8
[0.9. 0.95]
[0.9. 0.999]
Weight decay
0.05
0.01
Warmup steps (Ratio)
0.03
0.1
Epoch
20 (SFT) 30 (DPO)
20 (SFT) / 30 (DPO)
Grad. accumulation
Per-GPU batch size
Trainable modules
Trainable parts
LLM
LLM
Full Fine-tuning
Loss weights
DPO
0.01
0.01
Weight NLL
0.1
0.1
CFG Weight
Data Construction
Image Size
384 x 384
512 x 512
Images per Prompt
10
10
Data Size
1326
226
Table 4: Hyperparameter configurations in self self-improvement. For trainable parts, we only con- sider the LLM components shared by generation and understanding, which are sufficient to promote MsL.s. Additional trainable modules are discussed Appendix F2
Midell
Testune
Shape
Coler
Spotial
Nan-Spatial
Camples
Overall
Gan Und
Nov.
Und.) Non.) Crn Und Now || [Gen]]
Und Non
Gennt Und Nov. [Gex.] Und.) Non.j Cen. Und
Janus meueen
35.63
50.00
43.35
33,49
50,00
43.00
53:22
50,00
27.33
16.81
50,00
31.00
31.40
50:00
2.33
33.73
50,00
10.33
35.21
50.00
26.22
-FT
$3.93
63:22
20.67
38.63
53.85
34.00
73:41
54.63
10.3
23.73
26.67
22.00
31:45
75:00
1.00
38.57
75.00
4.38
43.20
58.90
16.95
CCSFT
55.38
66.57
28.33
39.86
64.52
33,67
73.27
14
12:20
24.7
38.46
21.67
31.44
75:00
2:33
38.78
70.00
3.33
44.18
61.13
16.02
-PP
40.08
53.55
43.00
33.49
57.59
47.00
51.72
63.64
27:12
16.49
41.67
30.00
31.32
66.67
2.00
38.61
50.00
6.67
35.44
55.62
25.07
C.DPO
42.13
53.33
45.33
33.46
55.36
40.00
53.17
35.71
28.81
15.74
42
32.53
31.38
50.00
2.00
37:58
78.57
6.33
35.64
56.00
25.80
T21 pl
$0.91
52.50
34.57
37.50
53.49
36.00
70.47
35.29
11.33
24.22
45.00
23.67
31.58
75:00
1.00
38.53
72.73
3.33
42.22
55.67
18.33
and
Shonespoows
65,90
40.00
0.31
53.72
50.00
0.67
72 50
50.00
8/00
19.31
$0.00
4.67
31:43
90.00
0.00
35.11
90.00
0.00
49,66
50,00
0.85
SFF
73.26
50.00
0.00
90.53
100.00
0.00
72.33
50.00
8.00
42.65
100.00
0.67
31.32
50.00
0.00
96.33
50.00
0.00
53.67
66.67
0.11
-CSFT
74.11
50,00
0.00
59.75
100.00
0.00
72.5%
50.00
800
42:70
(00:001
1133
31.53
50,00
0.00
36.42
50.00
0.00
52.82
66.07
0.06
- -DPV
69.97
50.00
0.31
51.55
5050
0.00
7367
30.0
0:38
42.59
66.07
2.00
11.81
90.00
0.00
35.71
50.0
0.01
51.50
52.75
0:45
- -CPPO
70.37
90.00
0.00
57.12
90.00
1.00
35 19
50.00
800
46
100.00
1.39
11.52
90.00
0.00
15.47
50.00
0.00
52.45
58.31
0.39
67.96
40.00
0.39
51.81
90.00
0.35
72.96
50.00
0.34
18:45
0100
4.00
11:42
90.00
0.00
95.78
90.00
0.00
49.65
41.67
0.85
Table 5: Evaluation Results T2l-CompBench+4 Self-improvement enhances MLLMs in gener-
ation, understanding, and unification, achieving results comparable to or even surpassing those of baselines that leverage external rewards.
benchmark. In addition, we adopt the definition of unification from Section 3 namely
unification 1 non-unification score.
For evaluating understanding capability, we introduce the win rate metrie. Specifically, the win rate (excluding ties) is defined as the proportion of samples where the understanding prediction changes after self-improvement and agrees with the We let pre and Teelf denote the pre-trained and self-improved MLLMs, respectively. We define generations by pre-trained MILMS as Xpre = -prr (y) for the prompt y. Win rate is:
of stronger judge S.wen2.5-VL--72--nnsite
Σ Trudd (Xpre> g(y)) # Tell
(Xpre: (xnd
Tund (Xpre: sQwen]
Win rate
(4)
I Fund (xpre: q(y)) Tin (Xpre- q(y)))
where SQwen (Xprexg(y) E 1) metric, which enables the simultaneous quantification of generation, understanding, and unification within the same task, thereby providing better depiction of the synchronous changes between gen- eration and understanding. In addition, we evaluate the understanding performance of MLLMs on dedicated benchmarks and provide illustrative examples for both before and after self-improvemennt
1111:11111 is oracle label provided by Qwen. We introduce the win rate
20

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Published as a conference paper at ICLR 2026
Model
Single Ohj.
Two Ohl
Counting
Coloes
Poston
Color Art
Ovenill
Oea.7
Und."
Non..
Lnd
Not.j
Cen.7
Und.7
Noa..
UndiT
Not.
[Cea.7
Und.7
Non.
Gen.1
Und
Not.
Gees.7
Und.7
Noo.
and
94.55
50,00
3.75
55.86
50.00
4.04
61.50
50.00
2.50
S4404
50.00
2.13
175.00
50.00
5.00
71.00
50.00
2000
70.36
50,00
6:24
-SFT
96.25
100.00
2:50
$7.88
0.00
7.07
63:00
50.00
330
57.23
66.67
1.06
75.00
40.00
5.00
65.00
50.00
13.00
70.50
51.11
3,61
-CSFF
95.75
100.00
6.25
$8.87
0.00
5.03
66.25
0.00
6.25
58.30
100.00
6.51
70.00
40.00
6.00
64.00
66.67
15.00
90.57
51.11
7.84
- -DPO
96.55
5000
2.50
19.90
0.00
6.05
56-35
10.00
6:35
58
90
50.00
6.19
71.00
50.00
6.00
09.00
100.00
13.00
75:20
50,00
6.17
-GOPO
91 11
100000
4:25
85
96
0.00
5:10
99:0
90.00
5.35
58.55
100000
1.06
87.00
50.00
1.00
49.00
41.33
2000
99:44
57-92
6.1
121- (powed)
94.75
50,00
7:50
56.87
0.00
6.08
56.75
0.00
7.50
57:23
100.00
1.06
63.00
60.00
3.00
10.00
$0.00
23.00
90.77
43.30
8.32
and
Stow Ostaviior
97.50
20.00
1.25
50.81
50.00
2,02
76.25
50.00
2.50
15.11
50.00
0.00
25.00
50.00
2,00
53.00
50.00
0.000
70.31
20.00
1.30
- -SF
17.50
50.00
1.25
91
50.00
0.00
61.25
50.00
0350
76.72
50.00
0..00
57.00)
50.00
2,00
02.00
50.00)
01.00
71.40
50.30
0.54
-CSSP
16.25
30190
1.25
50.87
30.00
0100)
07.50
10.00
0330
76:73
50.00
1.00
64000
50.00
1.00
06.00
50.00)
1.00
73:22
50100
0.72
-DPO
00.25
50190
1.25
54
81
50.00
0.00
71:35
10:00
0100
54.04
50.00
0.00
33.00
50.00
130
$2.00
50.00
01000
71:25
50.90
0138
- -GOPP
07.
50,00
1.25
54
81
90.00
(.00)
70/00
90:00
0100
56
50.00
0.00
59.00
50.00
100
00
50.001
0.00
71.42
50.00
0.78
06.35
50,00
1:25
64
50.00
1.01
66:35
90.00
1.35
50.85
50.00
1.06
35.00
50.00
2.00
46.00
50.00
0.00
69.08
50.00
1.10
Table 6: Evaluation Results on Geneval. Self-improvement enhances MLLMs in generation, under-
standing, and unification, achieving results comparable to or even surpassing those of baselines that
leverage external rewards.
Physics
Chemistry
Biology
Overall
Model
Gen.f
Und.1
Non.
Gen.t
Und.1
Non.
Gen.f
Und."
Non.
Gen.
Und.1 Non...
Gen, and Und.
Janus-Pro-7B, (Ravviine))
25.37
50.00
3.39
25.57
50.00
2.04
22.54
50.00
5,00
24.49
50.00
3.48
SFT
25.47
33.33
3.39
26.85
100.00
0.00
22.90
75.00
3.33
25.07
69.44
2.24
C-SFT
25.48
25.00
1.69
26.66
100.00
2.04
23.41
80.00
6.67
25.18
68.33
3.47
DPO
25.72
50.00
1.69
25.37
100.00
0.00
23.49
0.00
3.33
24.86
50.00
1.67
C-DPO
25.72
50.00
25.44
22.76
66.67
24.64
55.56
25.52
0.00
2.54
25.28
100.00
2.04
22.64
66.67
5.00
24.48
55.56
3.19
Gen, and Und.
Show-Drguclle!
25.56
50.00
5.93
26
50.00
0.00
22.48
50.00
0.00
24.72
50.00
1.98
SFT
26.57
60.00
1.69
26.62
50.00
0.00
22.48
50.00
0.00
25.22
53.33
0.56
C-SFT
27.12
60.00
27.63
23.38
50.00
26.04
53.33
DPO
26.05
0.00
5.08
25.76
50.00
0.00
21.53
50.00
0.00
24.44
33.33
1.69
C-DPO
25.93
50.00
5.93
25.71
50.00
0.00
22.51
50.00
0.00
24.72
50.00
1.98
HemmesFlow/Paraaa)
25.61
54.00
5.46
26.47
50.00
0.00
21.91
50.00
24.66
51.33
1.82
Table 7: Evaluation Results on Science-T21- Self-improvement enhances MILMs in generation, understanding, and unification, achieving results comparable to or even surpassing those of baselines that leverage external rewards.
B.2
FULL RESULTS
Full Results on Self-Improvement. Table 5 Table 6 and Table 7 report the improvements in generation, understanding, and unification of self-improvemed MLLMs across three bench- marks. Results of self-improvemeed MLLMs comparable to, and even surpass, two base- lines, T21-R1 and Hermesflow, which rely on external rewards. Taking Janus-Pro under SFT as an example, self-improvement boosts its generation and unification performance on T21-CompBench++ by an average of 8% and 10%, respectively. Moreover, compared to pre- trained Janus-Pro, its understanding capability is enhanced with win rate greater than 50%. Im- provement also observed on GenEval and Science-T21. These experiemnts verify the effectiveness of our proposed approach.
Toeal
EPL
(a) SFT, Lanus-Pro-7P
(b) SFT, Show-o
Figure 12: Building self ceff-improvement data based on internal gaps yields more samples from large gap tasks, thus guiding more gains on such tasks.
Additionally, MLLMs with larger internal gaps (e.g., Janus-Pro-7BB and larger gap subtasks (e.g.. Texture) exhibit greater gains after self-improvement. We claim that this may be because tasks with larger internal gaps encourage more samples from those subtasks in the post- post-training data, thereby benefiting the learning of those specific subtasks. Figure I2 demonstrates that subtasks with larger internal gaps constitute a higher proportion of the post-training data, which contributes to their greater performance gains, supporting our hypothesis.
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Improved Understanding: Additional Results Understanding Benchmarks and Exam- ples For image understanding evaluation, we consider the benchmarks POPE (Li et al. (023b), MMB (Luu et al.] 2024). SEED (Li et al. (023a), GQA (Hudson & Manning 2019), and Since all these benchmarks are
MMMU, and conduct the evaluation using VIMEvalKiE in multiple-choice format, we compute accuracy using exact matching. Table presents the results of the pre-trained Janus-Pro and the self-improved Janus-Pro on various under- standing benchmarks, showing that the MLLM's understanding ability further enhanced af- ter self-immproeement, with gains up to 3%. Table 8 also provides the self-improvemmen re- sults for Show-o. We observe that SFT-based self-improvement not only enhances gener- ation but also improves understanding ability, for example, POPE increases by nearly 2%.
We further present examples of self- improvement for Janus-Pro and Show-o under SFT (Figure [4] and DPO (Figure [5 which clearly demonstrate that after self- improvement, the models not only generate images that better satisfy the prompts, but also more accurately identify misalignments between the original image and the prompt, thereby providing correct evaluation scores (from score I to score 0). The improvements observed on understanding benchmarks, to- gether with these concrete examples, further support the co-improvement conclusion in Section 4.2.2 generation-targeted self- eef--imprveement can also enhance understanding.
(a) SFT
(b) C-SFT
Figure 13: Introducing an additional evaluator Gemini-Pro-2.5. Under Gemini's evaluation, self- improved model demonstrates better understand- ing ability with win rate greater than 0.5.
Model
POPE
MMB t
SEED t
GQAT
MMMU 1
Janus-Pro-7B
89.04
76.23
70.09
56.02
32.86
SFT
88.45
76.97
70.44
56.12
35.24
+ DPO
89.06
76.41
70.10
56.26
33.71
C-SFT
89.03
77.18
70.48
56.02
35.24
+ C-DPO
89.10
76.47
70.86
56.17
34.33
Show-o
64.05
30.91
52.86
56.82
23.33
+ SFT
65.27
31.92
54.14
57.22
24.00
+ DPO
64.71
30.82
52.73
57.03
23.33
C-SFT
65.82
32.34
54.32
57.33
23.33
C-DPO
64.97
31.14
52.90
57.09
23.33
Table 8: The self-improved MLLMs demonstrated improvements on understanding benchmarks.
Improved Understanding: Additional Results on External Evaluator In Section 0 we use Qwen2.5-VL-729 1a-n:-55VL-22B-nntttii Instructt the external evaluator. To further validate our findings regarding changes in the model's ''s understanding capability, we replace Qwen with the closed-source Gemini- Pro-2.5 (Team 20255) to compute the win rate defined in Equation 44.. Using Janus-Pro's SFT and C-SFT results an illustrative example, Figure 13 13 shows the outcomes obtained with Gemini the external evaluator. In most cases, the win rate exceeds 50%, indicating that Janus-Pro's understand- ing ability improves after self-improvement, reflected by its scores becoming closer to those of Gemini-Pro-2..
C ADDITIONAL DETAILS AND FULL RESULTS ON GURRICULUM-LEARNIIE BASED SELF- TELF-IMPROVEMEN
In this section, we present the training details and full experimental results of the curriculum learn- based self-improvement method.
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Improved Generation
Improved Understanding
Show-o
Self-Improved Show-o
Questioon: a hetyfti language and visie astiiustt You are undorstuud the contont we providas, assor the wor with varicty of tuaks asing natuaa language Does bagge Jeveiile (pooutt) descrilee
score y not fufly deseribe, souee Pinate antwor the fullonong forvat: The scove (your zcore).
Stameet Yes, the image depicts a persos on side of cow, The cow standing in field, and the person is standing nest to
Seffslopraned Shonost this does depict
showss
standing of dry
Prompt: person side of
Janus-Pro
Self-lmproved Janus-Pro
JunorsPrrs The image shows hrown cat with greon eves and malsinnng backgrmund The scmee inclules a grawey ance with sunligh Ellering thougg, crosing u warm and estuna setting The catss for is pecieminntly brown with while accents, and the redlurnge background is likely the surlight reflecting off the grass The some welkiit and vibront unggesiin sanmy day The wore is
Self-lmpoored
lighter be grany The sunlight. orange
backynound primarily with urange
describes the image
Prompt A
red orange.
Figure 14: Examples of co-improvements in generation and understanding of self-improved Janus- Pro and Show-o under SFT. We observe that, after ceff-improvement. Show-o and Janus-Pro gen- erate images that align prompts and accurately identify when images produced by the pre-trained MILMM are misaligned with the prompts.
Improved Generation
Improved Understanding
Self-improved Show-o
Duestion: You are hejyris laeguage and sision assisuntt You are able to undersiund the Vinsl conteut that the user provider, and asrist the oser will a variecy of tusks wing nuneral longunge Doer this iaage dorerite (yrospe? if it dereriles the scene. score 1: 8f does not fully denerile room Pirace auswer in the fullowing fiomart The scove is (your sowe).
Showess You, the depicto
side are. The cuw stamlingg fiokd, und
the pensn standing nodt io
Selfeimprored Shaweet No, image dace not depict parson un cow. shores con standing in fichd.
Prompt: a person side of cow:
Janus-Pre Pro
Self-Improved Janus-Pro
Jenas Phot image dhows clock with standing on grass resembiing sheep. top
legs desgned to shoep sumeal, hlending
part
The scoee
resembling clock postioned clear blue sky with clouds. does clock
Seff-inproned
with
stadillne cleck-iiee
Prompt: a clock on the top of a sheep.
Figure 15: Examples of co-improvements in generation and understanding of self-improved Janus- Pro and Show-o with DPO. We observe that, after self- improvement, Show-o and Janus-Pro generate images that align prompts and accurately identify when images produced by the pre-trained MLLM misaligned with the prompts.
C.1 ADDITIONAL DETAILS
Implementation Details. Following Section we leverage the improved genera- tion-understanding model to revisit prompts that were not utilized by the pre-trained MLLM due to weak generation or weak understanding capabilities (see details in 2). This process can be regarded as a form of curriculum learning based on prompt complexity (Li & Zhang 2025). We follow the training configurations in Table 4 and perform curriculum replay for both baT-based and DPO-based celf-improvemeet at epoch TO. In Appendix F.4, we conduct an ablation study to discuss the choice of epoch for curriculum learning. Table Subble/sste shows the data expansion for Janus-Pro and Show-o with curriculum learning, which increases sample size by up to 50%.
MILM
Self-improvement Strategy
Curriculum Epoch
Original Data Expansion Data
SFT
+1091
Jrous-Pro-718
2265
DPO
10
+359
SFT
10
+64
Show-o
226
DPO
10
+59
Table 9: Expansion of post-training data with introducing curriculum learning.
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Evaluation. Consistent with the evaluation in Appendix B.1 employ the same metrics to mea- sure MLLMs in generation, understanding and unification.
C.2 FULL RESULTS
As shown in Table 5 6 and Table 7 the self-improvement with curriculum learning (denoted as C-SFT and C-DPO) demonstrates that the increased post-training data benefiting from curriculum learning further enhances self- iel--veproeementt MLLMs' performance and unification, particularly in understanding and generation.
D UNDERSTANDING CO-IMPROVEMENT IN SELF-LMPROVEMENT
Section 5.2 explains why co-improvement occurs when self improveenttt is performed with SFT and provides empirical evidence based on Janus-Pro. In this section, we first detail the computation of the empirical evidence in Figure 7 then additionally present empirical evidence on Show-o with SFT to further support the theoretical analysis in Section 5.1
D.1 FULL THEORETICAL ANALYSIS UNDER SFT
a) explains that samples from the false positive correc- tion group (yo. Xo), i.e., the primary source of improvement in comprehension capability, exhibit higher similarity to their corresponding post-training samples (y.. X.). Specifically, we separately compute text similarity and image similarity as proxies for eNTK term K:: for each Yo. we first identify its nearest neighbor ya in the post-training data, then compute the similarity between the corresponding images Xy and X, For text similarity, we use pre-trained model all-MiniLMM-L6-v2 to encode each prompt into 384-dimensional vector and compute the cosine similarity between vector pairs. For image similarity, we use an equal-weighted combination of MSE and SSIM (Wang et al. 2004) to measure both pixel-level and structural similarity. To evaluate whether false positive correction group indeed exhibits higher similarity, we randomly sample random group (y, x) (with the same size false positive correction group) and calculate same. Figure ([) shows false positive correction group demonstrates significantly higher similarity in (y,, Xy), particularly in text. For Figure aa). the difference in text similarity between the False Positive Correction group and the ran- dom group becomes more pronounced: the prompt similarity between the False Positive Correction group (0,1,0) and the training data has a mean of approximately 0.85, whereas the random group has much lower mean of around 0.65, indicating clear distinction. The reason we focus more on prompt similarity is that the similarity shown Figure 7 in fact a proxy for eNTK (),, V.)) in Proposition D where y : [y/ x| is formed by concatenating the token embeddings of the prompt y and the image (see Appendix E for details). Since Proposition 1 shows that the learning dy- namics for both understanding and generation accumulate token by token (see Proposition [ the prompt tokens, which appear the beginning of the concatenated sequence, tend to contribute more substantially to the resulting eNTK than the later image tokens. Therefore, variations in prompt similarity can provide a more sensitive and reliable indicator of targeted eNTK.
Details on Empirical Evidence. Figure 7
[
Figure b) shows the Frobenius norm ||x (D%. Dwll exceeds ||AC* ()) (2%, DWll This indicates that at iteration t, the training dynamics of the understanding branch are primarily driven by XC' ('), which tends to align AU, Equation (3) and AG, in Equation (2). To substantiate this, we use data similarity as proxy for the eNTK. Specifically, for each sample (y).Xo) in the false positive correction group, we first identify its closest (y.,, x,) based on the most similar prompt and compute text and image similarities using the same metrics in Figure 7(a); this serves the proxy for (C%. p- For ()), |] we compute the text and image similarity between each non- (Yo, X²) sample (ys, x:) and (yu, x,), and average these similarities over all x4) as the proxy.
Figure (cc) shows, for samples in the false positive correction group, the probability of prompt- misaligned generation, i.e., the prompt-misaligned probability xx(xo yo) decreases. To quantify this change, for each validation prompt yo. we first the pre-trained MLLM generate xo and record its image token sequence and the log-probability of that sequence as log Te, (Xo yo). We then use the self-improved MLLMs re-evaluate the conditional loo-brobayility of the same token
M--L6-22
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sequence, obtaining log Te, (xo yo). Following the definition of the generation branchh learning dynamics in Section 5.1 we compute AG; = log no, (Xo Yo) - log Te, (Xu Yo).
More Empirical Evidence Show-o. Section 5.2 explains why co-improvement occurs when post-seffimpprovement performed with SFT and provides empirical evidence based on Janus-Pro. In this section, additionally present empirical evidence Show-o with SFT to further support the theorical analysis in Section 5.1 Figure [o]a) shows that, for Show-o under supervised fine- tuning (SFT), the primary gains understanding still come from false positive correction, i.e., (Label, Pre-trained, Self-improved) = (0, 1, 0). Moreover, there exists post- sut-trriiing data similar the false positive correction group, with an average cosine similarity of 0.8 (see Figure [T6(b).. Figure 16[c) indicates that the high sample similarity makes [| V')ll the dominant term, encouraging alignment between the training dynamics of generation and understanding. Together with Figure d)) which shows AG, < 0, this suggests AU, O, meaning the model identifies false positives and achieves joint improvement. Empirical evidence for Show-o under SFT further corroborates the theoretical explanation in Section 5.2
ciaii4 Postus sall equeed 1E1/1
Take Povament Sécapeont
Aew Ku
Peo MYA.
(a) Currection
Dominant
False
Positive
(b) Similar 3l and V
(c) Proxy (Na >> (d) False Positive Correction Group: AG, <
Figure 16: Empirical Evidence from Seff-Improvement with Show-o and SFT. (a) On T2I- CompBench++, understanding gains primarily arise from the false positive correction group. (b) Compared random samples, those the false positive correction group are more likely to be matched with highly similar post-training pairs (y,, (average cosine similarity 0.8). (c) Such high similarity makes (o)..)) ). be the dominant term Equation (3), thereby promoting aligned learning dynamics between understanding in Equation (3) and generation in Equation ( (d) With aligned dynamics, AG, < O implies AU; < 0: both the probability of misaligned generation (Xo Yo) and misjudging Ty(Vo Xo), are reduced. This manifests as false positive correction and jointly co-improvement.
D.2 FULL THEORETICAL ANALYSIS UNDER DPO
Proposition. In this section, we discuss why DPO-based self-improvement also leads improvement (see Table 5 Table 6 Table 7 and Table 8). For DPO, we define a post-training preference pair (ya, , x,, where the chosen image x and the rejected image x,, share the same prompt y.. The DPO loss is
(x
vit)
3 log To (x, D
LDPO(Y X x B Byy. xx)) log (Blog Tree (x+ 2it)
Teaf (x Vir
(5)
where 4 denotes the concatenation obtained by appending the embedding of y.. to the embed- ding of x and V denotes the concatenation obtained by appending the embedding of yu the embedding of x, Then, we have the following proposition:
Proposition 2 (Learning Dynamies of Generation and Understanding under DPO). Consider self- improvement proposed in Section 4 with DPO.
At epoch the one-step learning dynamics of generation is
AG,(Xo 1%)
M
M
=-n8o(-at +0(17)
Oza.
(2o, (mrtt ext.)
(2.
r=
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(x. 13%.) and म.. softmax(z,; and a.++ = (No))) ))r are the logits at image position r obtained by running hg on Vt. The neural tangent kernel , Dit) V Vozh (Vez Vz and K2 (.0, Vx) == Vozg (ozz)
where the margin a = log ma [+)
nxct(x. [ log x 1
The one-step learning dynamics of understanding is
AUG(Yo (x))
Σ
wy. I
xo)
(Kk, D1 o20.) )- -Kg Db. (n,"" oz
(ema nl)""
(k. o,L.) 1T
O(1²)
where 344 denote the concatenation obtained by appending the embedding of Ug.
We can interpret Proposition 2 by analogy with Proposition Specifically, when Vo is more similar the post-training data Vu than any other y/ that is, the Frobenius norm of Term I exceeds that of Term II, both the generation and understanding branches are dominated by the same alignment Term I, yielding consistent update signs.
Theoretical Analysis with Empirical Evidence. First, Figure a)) show that under DPO, gains understanding still primarily come from correcting false positives: across training steps, this accounts for roughly 60%- 100% of the gains. Hence, focus yo and its misaligned image Xo generated by pre-trained MILMS.
For self-improved Janus-Pro with DPO, by Proposition 2 co-improvement arise when the post- training data include pairs (yu, x.) whose prompt yu is more similar to Yo than any other prompt this case, the understanding update AU, in Equation (7) is dominated by Term I rather than Term II (empirical evidence in share
y; (empirical evidence in Figure [8()(c) and Figure I9))))))
Figure 18 (d) and Figure b)()). Note that, because
vtt and (), (V., the same prompt Yuu their Frobenius norms are both large, reflecting the high similarity between Yo and yu (empirical evidence in Figure T8(a)()) and Figure Figure(19ja)(c)) By contrast, IC,, (),,
and (). are significantly smaller due to the lower similarity between y; and y. (also in Figure (d) and Figure (9])))) The same Term therefore aligns the learning dynamics of generation (Equation (6)) and understanding (Equation 77). yielding consistent update signs AG,
and AU;
Moreover, such similar post-training pairs (y,,, x.) improve generation by lowering the probability of misaligned outputs, (x yo), leading to AG, < 0 (empirical evidence in Figure 17 (d)). Due to the aligned dynamics, AU,- < as well, meaning the probability of misjudging, T xo). is reduced. Consequently, false positive correction emerges, manifesting as co-improvement.
Lube, Editepenccee HMME!
(a)
Positive (b)
Dominant
Positive
Positive Correction (d) False
Positive Conection
Correction, Jamus-Pro
Correction Show
Group Janus-Pro: AG, 0 Group for Show-o: AG; <
Figure 17: Empirical Evidence from DPO-based lel--Improvement with Janus-Pro and Show-o. (a)(b) On CenphhhBennhh+ understanding gains primarily arise from the false positive correction group. (c)(d) For prompts Yo in the false positive correction group, the self-improved MILMM also reduces the probability of generating the prompt-misaligned image Xo, i.c., AG, < 0.
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Libd hoealed appond
(a) Simmlar 3o and Vd
(b) Proxy |JA (36, Dil Dllle ll
(c) Simalar Yo and V
(d) Prexy (IA' a [1A He
(K DaDl )l
Figure 18: Empirical Evidence from Self- lef--Improvement with Janus-Pro and DPO. (a)(c) Compared to random samples, those in the false positive correction group are more likely to be matched with highly similar post-training pairs (Y,,X., (average cosine similarity 0.8). (b)(d) Such high sim- ilarity makes Term I be the dominant term in Equation (7). thereby promoting aligned learning dynamics between understanding in Equation 7 and generation Equation (6)
620-221
LiM Nomire selcageon
(a) Similar D/ and y
(b) Proxy ||A (0., Ct [KC" >T ll
>
(c) Similar Vo and D
(d) Proxy O1. Il (0%
Figure 19: Empirical Evidence from Self-Imprrvemmnt with Show-o and DPO. (a)(c) Compared to random samples, those in the false positive correction group are more likely to be matched with highly similar post-training pairs (yu, x.) (average cosine similarity 0.8). (b)(d) Such high sim- ilarity makes Term I be the dominant term in Equation 7 thereby promoting aligned learning dynamics between understanding Equation (7) and generation Equation (6).
E DERIVATIONS AND PROOF DETAILS
Preliminaries. We define the unified vocabulary V of discrete text and image tokens, with size [l. Since fine-tuning only updates the LLM part Ty of the MLLM, we work directly in the LLM input space. Let denote the input embedding dimension.
We consider the setting where both image generation and image understanding share the same to- kenizer the default Show-o and EMU3. This contrasts with decoupled designs such Janus- Pro, where generation and understanding use separate tokenizers. Nevertheless, our analysis shows that results derived under the shared-tokenizer assumption continue to hold for decoupled archi- tectures like Janus-Pro. Specifically, at inference time, for each sequence of image token IDs Xo (Zo.1..... To.M) and text token IDs yo 1... Mo.L), we encode them as sequences of embeddings as the inputs of LLM. The image sequence is represented by embeddings
Uo U0o1
uo ]
dx
and the evaluation prompt is represented by
Vo [Vo,1 Vo,z gdxL
where usually |V] max(L, L, .). Similarly, the fine-tuning data pair (u,, vu) yields U, E MdxM and V. E paxxpp
We consider the typical causal-maskinn mechanism applied in MLLMS Wu
2024a Wang
et al. 2024 Wu et al., (0255). Under this mechanism, Ta takes the full concatenation of image and text embeddings as input and predicts the next token(s) (Ren & Sutherland 2025). We denote the concatenated inputs by
Uo
Vo] (
(Understanding),
3/1 = Vo Un E Rdx(L+M)
(Generation).
AAcross datapoints, the image length M is fixed while the text length I. may vary; common symbol for simplicity.
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Symbol
Definition
Data-related notation
Yo (y),,, 3),
Tokenized text prompt (index form), length Tokenized image (index form), length M
Xo = (Io.1,....
F0,M)
Uo |u,11 U0
Image token embedding matrix
Vo [o,1 vo,L
Text token embedding matrix
X = [ Vol
Input to understanding branch
Vo Vo Up]
Input to generation branch
X₂ U Vu]
Post-training sample (image) for SFT/DPO updates Post-training sample (prompt) for SFT/DPO updates
V , U..]
Model-related notation
Unified MLLM parameterized by 0
h-)-)
Logit network producing token-wise logits before softmax Unified vocabulary size for text and image tokens
V
(5)
Logits at position k on sequence S at epoch
z softmax(2)
Token distribution at position k
Cr
One-hot vector corresponding to token Generation likelihood of image tokens
(x (b)
To Xg)
Understanding likelihood of text tokens
Learning dynamic-related notation
(G, 1%)
One-step update of generation log-likelihood
(yo ())
One-step update of understanding log- oog-likelhhod
Kg (Yo: V.)
Empirical NTK: (Vo, oz (Vo,
Table 10: Key Notations used the learning dynamics analysis of unified MLLM.
where we omit potential special tokens (e.g.. [SCI]) for simplicity.
Let hy denote the logits network with causal mask implemented. For understanding,
and = ho (Xo) M+1:++1 E VxL,
Innd == softmax.al22.. E Vxt.
and for generation 10
gen ho (20) L+1.+MM E RVM.
II geen softmax.al (z.. ER
Let you E V and Fo. V denote the scalar ground-truth token ids at text position i and image position hi respectively. Then the modeling understanding and generation can be factorized
log Tygo 2o) = Σ log To(Do xo. No,et)
log [und 20.
M
log (xo Vo) =
Σ log ne (2o,k xo,
Σ
log [Igeen] so.
At epoch i, we define the one-step learning dynamics of evaluation data pair (o) after training one-step on fine-tuning data (Xu: as
AG Vb) = log To (Xo
Vo) log (x Vo)
(Generation)
AUdyo Xx) = log no (Yo Xn) log (y, X6)
(Understanding)
Typicallyy generation branch includes projector generation head. For example, Janus-Pro uses 2-layer MI.P to map I.I.M outputs to generation tokenizer's codebook. In setting, the generation head frozen.
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It is worth noting that Section 4.2.2 evaluates understanding improvement in terms of binary classi-
fication (-) whereas the theory focuses on log- seg-likelihood log (Yoo Xo). We introduce decision rule bridge the continuous likeliikl with the discrete binary score:
folyo (o) (yo X)) > >T}
where 7 is a threshold. When AU (yo Xi) increases, the understanding branch is encouraged to raise the pog-liiklihood, making it more likely yield score of 1 under the decision rule.
We first show the connection between the learning dynamics of generation and understanding. First, obtain
To (Xo 12)
II mo( Yo, Xo,ck) = To (xo yu), k=1
no (Yo (6)
II ne( Vo. Xo, Yo,s!) To xo).
Given the prompts follows a Uniform distribution, Bayes' rule yields log my xo) log yo) - log (x())) + C.
where C == log Pyyo) constant under the uniform prompt prior. Therefore,
AUdyo ()) = log mB, iyo X%) - log (yo X))
(log To.+1 (Xu yo) log Te, (Xp Yo)) (log no,_, (Xo) log no, (xo))
AG (b) A log ne (xo).
Equation implies that the learning dynamics of understanding (U,(yo X)) and those of genera- tion AG, X Vo) differ only in the change of the marginal distribution गy (xo) between consecutive steps. We next consider the training dynamics the generation and understanding branches under different post-training strategies, SFT and DPO. Table T0 summarizes the key notation used in the learning dynamics analysis for reference.
E. LEARNING DYNAMICS UNDER SFT
Following equation 8 first discuss the training dynamics of the generation branch, i.e,AA.((o (o), and then provide an indirect estimation for the understanding branch
AUlyo (g).
Lemma (Learning Dynamics of Generation under SFT). Consider self inproveerv proposed in Section with SFT. At epoch t, the one-step learning dynamics of generation is
M M
AG,(x0 13)) 11
ΣΣ
(ero.a - T KC (m) - Cr.,.) + O
x=1+=1
where n.e = softmax(z") and n.- = [ho().)] are the logits at image position r obtained by
running he on Yn and 1ot (Jh, Dw) = (Vo.2))(Vo.z) E VxV is empirical neural tangent kernel (eNTK).
We first show the learning dynamic of generation, i.c., AG(x0 (1) under the SFT setting. Consider the k-th image token
Proof.
(x. Do)) = log no,_, (xo [o)] à log mo, (xu Do)l
Vo log me, (xo Doo] (0t+1 0e) + 0110041 - -6, 0.1)
(10)
where [log (x [b)] log कd Fo.
Yo, xo.<k).
Given the post-training data (Xu, Yu), for generation, the negative log likeliholdood loss of SFT is
M
Csrr())))
log no (zr Fu.r 11a)
2 log x Za.r
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where n. softmax x.) and z [ ()a)), 1r are the logits at image position obtained by running he on Vu [Vu U... One-step SGD yields
01+1 0/
= =1 Σ
Vozz)
gr,
r=1
where 9, == T. Cau,e E R.V.
Then, obtain
Ve log no, (xo {6)] (Vo,2)) (exo, -m))
Therefore, Equation 100 be rewritten as
M
(xG,(xo 12)) = -n Σ
Cro. - xk) x (V -
r=1
M
Σ
(ero - x K2, (Jo. (TT - -z..,) + 0(7²)
where (C,, = (Vo,2)) VVVV
Finally, we have the sequence-level one-step change as:
(G, (b)
Σ
log To. (xo 1b)]
Σ
log Te, (xo Dol]
M
(xo 12))
M
M
Cro.
Vol - Cx., +
1=1
The proof is complete.
Lemma (Learning Dynamics of Understanding under SFT). Consider relf-mmprveeeett proposed in Section 4 with SFT. At epoch the one-step learning dynamics of understanding is
AUsyyo X1) =
Σ
Σ
(y, | xo) (1eD - --) =D]TK]t K.t, Do) - (exo. - Kh. esu..)
+ O19))
(11)
To, To, (xaly)
where Wa, (y) xo)
Ey ne, (X and Y& denotes the concatenation of prompt y: + yo and Xo-
Proof. We then analyze the learning dynamics of the understanding branch. By Equation 8 and first-order log-sum-exxy sum-exp expansion, we obtain
log Te (xo) log ni.+ (xo) log To, (xo)
log Σ To._ (xo ly) log Σ To, (xo ly)
Σ (y Xo) Vo log no. (xo 1y) Ot+1 - 0.) + a(1l0++1 - -x.13)
where the posterior weight is
To. (xo y)
wo, (y xo)
Ey To, (xo y²)
30

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Published as a conference paper at ICLR 2026
Following Lemma] and Equation 8
obtain
AU,(Yo (o)
[ (6) A log (x()
Volog To, (Xo Do) (0c11 1 0c) Σ wa, wa, Xo) Vo log mx D2) (0r 0e) 0110211 - 0ell3)
M M
Σ
W9 (y, xo) (lexo. [ KG -esro mk) (2, -c...)
k= yi
+ (17))
(12)
where }/ denote the concatenation obtained by appending the embedding of Un-
The proof is complete.
E.2 LEARNING DYNAMICS UNDER DPO
Lemma (Learning Dynamics of Generation under DPO). Consider self-improvement proposed in Section with DPO. At epoch the one-step learning dynamics of generation is
(x, 13)
--n8o
eza.k =H)T K",,
(TT exl. (%, (T.
+ +112)
(13)
where the margin a = 3 log m B log
(x. 1V:e) and ग... = coftmax(zk;+ and
x 1
meat 1
2. = [ho (>o))))) }r are the logits at image position r obtained by rumning hg on Vi- The neural
tangent kernel K (i) Fezl(Vez"" and K2 Vo. Vezy (or2((Vr (Voz),
Proof. Following equation 10 one-step SGD yields
Oc+1 0, V
=
V (Z 11 +) V + LDoO + (Voz, a. LDPO
= Σ (Voz (x (TaniitTTTxtttttt
(Vozz--) (T c,-,
c xxt,)
where the margin a = B log mo(x!1> Blog xx D (x, ym And ा. = softmax(z" and
n.at (x 1
2,t [ho())))rr are the logits at image position r obtained by running hg on y [V. U
Then, have
(AG,(Xo (o))
--130
Σ (ero. mh) (Voz (Voz,it)) ( ee,t))=
(Voz,, ( e +0(1²)
M
--m8o(-a)
Σ (exo. - #b) [kt, (16, DIt (T nit - e e - (3', Vi. (a - - +0
1=1
where the neural tangent kernel K (Jo, (t) = (eez and K (3,, (.) = Vazg( (Vokk-)
31

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Published as a conference paper at ICLR 2026
Finally, we have the sequence-level one-step change as: (x, 13)
M
M
= =-180(-ay
Zza.k
m
(Vo, (TT
-
1=1==
+0
The proof is complete.
L
Lemma (Learning Dynamics of Understanding under DPO). Consider self- inffffeprooveettt proposed in Section 4 with DPO. At epoch I, the one-step learning dynamics of understanding
AUs(yo (.6)
=nn8o(-o
(ys (xo) (ema= ~x2) (K2,)(K),, (b. Dt -o,
Virto
OL. -o2t))
061)
(14)
where V4 denote the concatenation obtained by appending the embedding ys to Uo.
Proof. Following 2 for the learning dynamics of understanding under DPO, have
AUdlo (6)
ACC(xo (b) log (x(o)
Vo M6) (0++1 0+) Σ 10, (v, xx) Vo (X. [D)) (0e+1 0,) O(110++ 0,ll*)
Σ my. xo) Vo log mo. (xo Yo) Fog 22) (0e+1 - (4) 01100+1 0cll")
M V
(y: xx) (Iem.. m88) 10: (erar -xx) K2,, (o.
=nnga(-a)
VirAoo
- -(em " (io. - KL.,,, (tt -ac.))
+0(1))
wa
(x0) 1xo)
(ems # (D. o.t.) O6.061
)..)))
VirAo
-c..)) 067)
(e m
(15)
where }; denote the concatenation obtained by appending the embedding of y; to Up.
ABLATION STUDY AND MORE EXPLORATIONS
F.1 SELF-IMPROVEMENTS ON ADDITIONAL MODELS
We conduct SFT-based self-improvement experiments on an additional model, Janus-Pro-IB (Chen al., 2025b), where the training data, training pipeline, hyperparameters and evaluation metries follow Section 1 Table 17 shows that the self- self-improved model exhibits improvements in gen- eration, understanding, and unification, further confirming the effectiveness of ool--improvement.
Additionally, we conducted the same self-improvement analysis on the BAGEL model (Deng al., 2025) in Table [2] and Table 13] However, we found that improvements in generation capability provide only limited gains to BAGEL's understanding ability. This may be because BAGEL adopts Trriurre----Tannffomme------
(MoT) architecture (Deng et al.) 2025), where the interaction between generation and understanding occurs only through shared self-attention operations, rather than through a shared LLM in Janus- which unifies the integration of LLM and diffusion models within a single transformer. This observation may suggest that architectural choices lead to differences in the interaction of generation and understanding.
32

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Published as a conference paper at ICLR 2026
Mold
Color
Spatial
Nos
Comples
Cen.7
Nov.]
(Gen.) Und
Non.
[Gen.]
Und
Nen.
(Gen."
Und Non.
Und.7
Non. [Genn] Und
Und Noo.
Gmn and Unal
51.33
43:11
55.81
50.00
50.00
42.67
21.34
50.00
9:00
50.00
16.00
33.70
32.47
41,00
59.34
10,40 53,87
9.33
15.00
38.43
Table 11: Self-improvement results of Lanus-Pro-1 IB on T21-CompBench++ where the understand-
ing score is evaluated using Gemini-Pro-2.5 as the external evaluator.
Stape
Coler
Spacial
Non-Sporial
Comples
Gea.1 Und
Non.J Geen"" Geen"
Und1
Noo...
[Gen. Und.1
Gent Und
Non.
Gen.t
Und Non. Gen.
Und1t Noo., (Gen Und.7 Non...
Gen and Und
103110 5000
50,00
52.09
71.12 66.67
58.33
20.33
23.00
34.33
22.00
33.33
37.25 50.00
13,00
52.33
Table 12: Self-improvement results of BAGEL on I22-CoomBenchh+
F.2 ABLATION UPDATED MODEL COMPONENTS
we update only the parameters of the LLM component during self-im elf-improvement, while keeping all other components frozen. This design aligns with prior work on MILMS (focused solely image understanding), which suggests that optimizing the LLM alone is sufficient to im- prove MLLM performance, while updating other components yields limited gains (Verma et al. 2024 Table 14 supports setting: fine-tuning only the LLM already enables the self- self-improved Janus-Pro-7B to achieve improvements in generation, understanding and unification. However, ex- panding the parameter updates include the image aligner (a two-layer MLP projector that maps image tokens to the L.MM input space), the generation head (a two-layer MLP that projects LLM output into tokenizer's codebook space), even the vision tower, did not lead to significant perfor- mance gains in generation and slight declines were observed in both understanding and unification.
In Section 4.2
F.3 ABLATION ON IMAGE CANDIDATES
Table 15
reports the number of post-training samples produced under different values of image candidate N (see details in Alg. I.. We observe that as N increases, the number of constructed samples gradually saturates. In this paper, we adopt large value of N 10 for data construction.
F.4 ABLATION CURRICULUM LEARNING
We introduced curriculum learning at different training epochs (4 and 10). Curriculum replay at both epochs improved self- self-improvement performance, though performance was better when replay was applied at epoch 10. This is likely because the model's 's generative and understanding capabilities had improved by that stage, enabling a more effective use of earlier samples for expanding post-training data. Accordingly, we use epoch 10 for curriculum replay in all experiments.
F.5 IMPROVEMENT WITH EXTERNAL REWARD
We construct post-training data using external Qwen2.5- Dwen2.5-VLL-7BB -L-.----LL77B-Bntttree For Janus-Pro-78 with the SFT strategy, Table compares Qwen-based alignment with self-improvement. Qwen enables Janus-Pro-7B to achieve better generation and unification. Self-improvement yields slightly weaker alignment, likely due Nevertheless, without introducing any external signals, the self-improvement method achieves re- sults close to those obtained with Qwen-based alignment.
ProuusPrr--BB- inferior image understanding capability compared to Qwen.
G THE USE OF LARGE LANGUAGE MODELS (LLMs)
In writing, we used LLMs (ChatGPT-5) for manuscript-wide grammar checking and sentence pol- ishing. In coding, we leveraged LLMs (ChatGPT-5) for debugging our self-improvemmnt pipeline and assisting with visualiisuiii scripts.
33

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Published as a conference paper at ICLR 2026
Model
POPE1
MMB 1
GQAT
MMMU
BAGEL
100.00
88.05
52.56
64.03
SFT
100.00
88.14
52.44
64.10
Table 13: Improving BAGEL's generation leads to limited improvements in its understanding.
Testors
Stape
Userr
Spstal
Nem-ppaiss
Cimplec
Unenal
Usii
Usd
Nee.
Usd-f
Nov.
Und
Nes.
Gee.) Usd
Son
1
Dndl Nro.
Gea. and
Lanz- Jano-Pro-BBaan
38.83
54,00
43.33
35.49
50.0
43,00
53.22
5000
27.33
16.11
5000
31000
31,00
30,01
2:33
37.23
90.00
10.38
35.21
5010
26
5462
16.37
I1M and Projewe and Voice Rrwer
15.17 18.0
19.19
35,00
18.7
31-31
31000
10000
61M
566
17:02
Table 14: Based on Janus-Pro-7B, we conducted self-improvement via SFT and observed that only fine-tuning the LLM was sufficient to achieve improvements in both performance and unification. Updating other components, such as the vision tower and projectors, yielded no significant gains.
MILM
N=2
N=4
N=6
N=8
N=10
Janus-Pro-7B
254
1338
1823
2088
2265
Show-o
80
160
192
208
226
Table 15: Data expansion slows down as N increases.
Special
Nus-Spatial Und.7
Cumples
Gen.7 Nos.] (Gest Undt Non. Und1
Non.
Und."
Non.)
Non. Geent Una1
Gen.t Und Non..
Gat and Uni.
Jam--PPoTTRouotee
43.33
50.00
2.33
10.33
26.7
75.00
28.33
75.00
2.33
Table 16: Curriculum learning at different epochs consistently leads to better self-improvement, and consistently apply it at later epoch (epoch 10).
Model
Texture
Shape
Coler
Spatial
Non
Cusplex
Ovenil
Nun.] [aan.
Und.
Nin.
[aan
Und
Non.
Gas.11
Und.1
Nin.]
Gan! Und
Non.]
[enn
Und Nov.]
Gan.1
Men.]
Janus BBa
38.63
50000
43.35
33.09
5000
4300
5522
5000
27.33
1881
5000
3100
31.00
3000
233
37.23
5000
10.35
26.22 43.29
33.21
5000
Seffllpmmed 93M1
65.22
2907
5988
1600
7541
5682
1088
2573
2007
2200
9830
75,00
4,81
1699.
(frot-atoiied SF7' 36.94
56.00
25/00
41.5%
19.66
76.18
4983
24
M.28
1938
7000
9651
6687
5.3
1664
Table 17: Constructing post-training samples with Qwen also enhances the generation, understand- ing, and unification MLLMs. Without any external rewards, the self-improvement method yields slightly lower performance and unification than Qwen-based MLLMs.
34


Reviews:
[{"review_id": "D4bwmSD1g0", "reviewer": "Reviewer_iJU7", "summary": "The authors let the understanding branch of MLLMs judge the correctness of images generated by the generation branch, as a reflection of the model's internal gap, across multiple models and tasks. They leverage this gap to improve the models' generation ability using SFT and DPO with data generated by the generation branch and verified by the understanding branch, resulting in improved generation, understanding, and internal unification. They explain the enhancement of the understanding ability with a learning dynamics theory and support it with empirical analysis. They further propose to incorporate data that could not be utilized initially in their data-collection pipeline into the post-training process, after the model's ability has improved, thereby further enhancing the training outcome.", "questions": "1. What was the detailed setup of the human evaluation in Fig. 9? The appendix mentions the human check but omits details on the number of annotators, the interface used, the specific instructions given. \n2. Could the authors evaluate the self-improved Show-o model on the understanding benchmarks, similar to Janus-Pro-7B in Tab. 8?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The self-improvement approach, which uses the understanding ability to guide the generation branch, is intuitive and intriguing in concept and effective in practice.\n2. The work is generally solid and logically rigorous, with a motivation validated across multiple models, comprehensive experiments conducted on two models evaluated by both the authors’ proposed metrics and existing benchmarks, theoretical interpretation, and empirical validation. One of the relatively unconvincing and inelegant aspects—using Qwen as an external judge—is compensated for by a human verification experiment.\n3. The learning dynamics analysis provides theoretical insights into the mechanism of the co-improvement effect.\n4. The paper is generally well written, and most details are explained clearly.", "weaknesses": "The empirical evidence for some of the paper's claims is less conclusive than stated, which lacks further clarification:\n\n1. In Fig.2(c), the claimed \"trend of increasing with task difficulty\" for the non-unification score is not obvious or monotonic. The variation between models seems to dominate any difficulty-based trend. \n2. In Fig.7, the difference in similarity between improved samples and random samples is also not clear, especially for image pairs.", "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": "B1DAYg0PkB", "reviewer": "Reviewer_fxim", "summary": "This paper systematically investigates the prevalent \"generation-understanding non-unification\" phenomenon in Multimodal Large Language Models (MLLMs), where understanding capabilities typically outperform generation capabilities. The authors propose an internal gap-based self-improvement framework that leverages the model's own stronger understanding branch to guide its weaker generation branch. Without relying on external signals, this approach significantly enhances generation quality and reduces the internal gap through post-training methods like SFT or DPO. Experiments reveal that this method also induces a \"co-improvement\" effect, where improvements in generation simultaneously enhance understanding, particularly in identifying misaligned generated samples. Furthermore, drawing from learning dynamics theory, the paper identifies the shared empirical Neural Tangent Kernel (eNTK) between generation and understanding as the key mechanism behind co-improvement. Based on this, a curriculum learning strategy is proposed to dynamically expand the training data, further boosting model performance and unification.", "questions": "A critical ambiguity exists regarding potential data leakage in the T2I-CompBench++ experiments. The authors state in Section 4.2.1 that post-training data was constructed using ``about 6000 text prompts as post-training candidates'' from T2I-CompBench++. Subsequently, the model's performance is evaluated on the T2I-CompBench++ evaluation set (as shown in Table 1 and Table 5) . However, the paper fails to explicitly state whether the 6000 prompts used for post-training (SFT/DPO) were drawn from the benchmark's designatedtraining split. If any overlap exists between these post-training prompts and the prompts in the evaluation set, the reported performance gains on this benchmark would be invalid due to data contamination. This lack of clarity undermines the reliability of these key experimental results.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Rigorous Problem Verification: The paper first confirms the internal gap in MLLMs where \"generation is weaker than understanding\" through large-scale evaluation (across 6 models and tasks of varying difficulty). To achieve this, the authors innovatively propose a \"non-unification score\" that does not rely on external evaluators. \n- Simple and Effective Solution: An \"internal gap-based self-improvement\" framework is proposed, which leverages the model's own stronger understanding capability to filter generated data (for SFT or DPO). This method requires no external reward models, achieving closed-loop self-improvement.\n- Discovery and Explanation of the \"Co-improvement\" Effect: this paper introduces that \"generation-targeted training\" also enhances \"understanding capability\" (particularly in correcting false positive samples). From a learning dynamics perspective, the paper attributes this to the shared eNTK (empirical Neural Tangent Kernel) between the generation and understanding branches.\n- Clear Logical Structure Throughout the Paper: The paper exhibits a clear and coherent logical structure from beginning to end.", "weaknesses": "- Experimental Results Heavily Rely on a Single, Insufficiently Strong Judge Model:Two of the paper's key conclusions—that the \"internal gap stems mainly from weak generation\" and the \"co-improvement effect\"—rely heavily on using Qwen2.5-VL-72B-Instruct as the sole external judge.\n    However, Qwen2.5-VL-72B is not a strong enough multimodal model to serve as a reliable evaluator, especially when dealing with complex or \"hard tasks.\" To enhance the credibility of the experimental results, it is recommended to adopt more advanced SOTA models or incorporate multiple models as evaluators.\n- Limited Innovation and Effectiveness of the Curriculum Learning Part:\n    The curriculum learning (C-SFT/C-DPO) strategy proposed in Section 6, whose core idea is to reuse \"difficult\" samples discarded earlier due to the model's limited capability, is lacking innovation and Effectiveness.\n    This method is a conventional application of curriculum learning and lacks significant methodological innovation.Based on the experimental results (Table 1 and Table 5), the improvement of C-SFT over standard SFT is very marginal. For example, on the \"Overall\" metric for T2I-CompBench++ (Table 5), the generation score for Janus-Pro only increased from 43.29 to 44.18, and for Show-o, only from 52.67 to 52.82. This marginal gain seems insufficient to justify the introduction of this strategy.\n- Insufficient Experimental Evidence and Lack of Generalization for \"Co-Improvement Effect\":The \"co-improvement effect\" (Finding 2), a key contribution, claims that self-improvement targeted at generation also enhances understanding. As mentioned, the effect is primarily shown via the custom, Qwen-dependent \"win rate\" metric.The improvement on standard benchmarks is Negligible(Table 8). For example, after SFT, Janus-Pro-7B's score on POPE \\textit{decreased} from 89.04 to 88.45, the improvement on MMB was only 0.74 (76.23 $\\rightarrow$ 76.97), and on GQA, only 0.1 (56.02 $\\rightarrow$ 56.12).This significant disconnect between the custom metric (Win Rate) and standard benchmarks (Table 8) suggests that the so-called \"co-improvement\" might just be overfitting to the internal understanding task or the Qwen judge's preferences, rather than a genuine, generalizable improvement in understanding ability for standard VQA or hallucination detection tasks.", "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": "KtrPQVy61J", "reviewer": "Reviewer_Qp5p", "summary": "This paper investigates the generation–understanding internal gap in unified multimodal large language models (MLLMs), where the understanding branch consistently outperforms the generation branch.\nTo address this, the authors propose a simple internal gap–based self-improvement framework that leverages the model’s own understanding capability to guide and enhance generation, without any external rewards or supervision.Key contributions include:\n\n1. Quantitative diagnosis of generation–understanding non-unification, introducing an internal Non-Unification Score to measure intra-model consistency and empirically verifying the widespread internal gap across six MLLMs (Sec. 3).\n\n2. Internal gap–based self-improvement framework, leveraging the stronger understanding branch to score and filter generations for post-training (SFT/DPO), effectively improving generation quality and reducing non-unification without external signals (Sec. 4.1–4.2).\n\n3. Theoretical analysis of the co-improvement effect, extending learning dynamics to multimodal models and revealing that shared empirical neural tangent kernels (eNTKs) drive aligned updates between generation and understanding (Sec. 5).\n\n4. Curriculum-based self-improvement strategy, progressively reintroducing previously underutilized or difficult samples through curriculum replay to further enhance both branches and surpass external-reward baselines (Sec. 6).", "questions": "1. How stable is the self-improvement process when the understanding branch provides noisy or incorrect judgments?\n\n2. Can the proposed framework generalize to other modalities (e.g., video or audio) or to non-generative MLLM tasks?\n\n3. How sensitive are the results to the number of generated candidates N and the selection threshold in the understanding branch?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper propose an internal gap–based self-improvement framework, which is conceptually simple but novel, requiring no external supervision or reward models.  The authors also introduce a new internal evaluation metric (Non-Unification Score) to quantify intra-model consistency.\n\n2. The work provides strong empirical evidence through large-scale experiments on six unified MLLMs and three task difficulty levels (Figure 2).\n\n3. Figures and algorithms are clearly presented — e.g., Algorithm 1 succinctly formalizes the self-improvement process, and Figure 5 (a) intuitively illustrates the co-improvement effect with side-by-side visual examples.\n\n4. The discovery of a shared empirical NTK between generation and understanding offers a novel theoretical perspective on multimodal model coupling ( Eq. 2–3). The curriculum-based self-improvement strategy demonstrates a scalable path to strengthen MLLMs without external data or rewards, surpassing reward-model–based baselines such as T2I-R1 and HermesFlow.", "weaknesses": "1. Theory–practice gap: While the shared eNTK explanation is conceptually interesting, its empirical validation remains limited, and the theoretical section is notation-heavy, reducing accessibility for non-theoretical readers.\n\n2. Limited model diversity: Main experiments focus on Janus-Pro and Show-o. Although six models were initially analyzed, most in-depth post-training results come from only two, limiting the generality of conclusions.", "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": "oOl2OPoEIh", "reviewer": "Reviewer_fCxY", "summary": "This paper investigates the \"internal gap\" in unified Multimodal Large Language Models (MLLMs), where understanding consistently outperforms generation. The authors propose a self-improvement framework that leverages stronger understanding to guide weaker generation through standard post-training (SFT/DPO), achieving up to 20% generation improvement without external signals.", "questions": "no more.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper provides systematic empirical validation of the generation-understanding gap across multiple MLLMs with clear methodology and reproducible experiments. The theoretical framework extending learning dynamics to multimodal settings offers mathematical formalization, though it essentially explains an expected outcome—that shared parameters naturally lead to correlated improvements. The work is technically sound with detailed ablations.", "weaknesses": "The work is technically sound with detailed ablations, but the core insight that two capabilities in a closed system can mutually improve through iterative training is conceptually straightforward, limiting originality. The self-improvement approach applies standard techniques (SFT/DPO) without novel algorithmic contributions, and still underperforms methods using external rewards, constraining its practical significance. Critically, the theory fails to address fundamental limitations: in a closed system without external supervision, two learners cannot indefinitely improve through mutual training—the paper lacks analysis of performance ceilings or convergence bounds.  Overall, the paper competently documents a predictable phenomenon rather than introducing fundamentally new concepts.\nMore specifically, the theoretical analysis (Propositions 1-2) provides mathematical formalization but offers limited conceptual depth beyond expected outcomes. Experimental scope is narrow: only two models tested, consistently underperforming external reward baselines, undermining practical value. The non-unification metric relies on potentially unreliable binary judgments, and using Qwen as ground truth introduces unexamined biases. Curriculum learning adds minimal gains without principled design.", "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)