Trinity: Self-Evolving Vision-Language Models with a Self-Verifier
Authors: Youngwan Lee, Yong-Ju Lee, Sung Ju Hwang
Organizations: Electronics and Telecommunications Research Institute (ETRI), South Korea · Korea Advanced Institute of Science and Technology (KAIST), South Korea · DeepAuto.ai, South Korea
Self-evolving vision-language models (VLMs), a form of self-improvement in which a model generates its own training data from unlabeled images, are a promising route toward agents that expand their reasoning capability in an unsupervised manner, without relying on ever-larger annotation budgets. Existing methods pair a Questioner that proposes problems with a Solver that answers them, but reward both roles mainly by agreement among sampled answers. Agreement is a weak proxy for truth: it cannot tell whether a question is grounded in the image, whether the proposed reference answer is right, or whether a confident majority is wrong in the same way. We present Trinity, in which one VLM plays three roles, Questioner, Solver, and Verifier, and the Verifier is a self-verifier: an exponential moving average (EMA) of the policy itself, requiring neither labels nor an external judge. The Verifier screens every generated question for image grounding and answer correctness before it becomes supervision, scores Solver reasoning against the image, and adjudicates disputes between the reference answer and a strong Solver consensus, correcting the reference and penalizing the Questioner when the consensus is right. Trained on images alone, Trinity improves Qwen3-VL-8B on mathematical visual reasoning and on science benchmarks with biology content, for example, +8.6 points on the biology split of SciVQR and +12.8 on MathVerse, and its reward dynamics behave as a healthy self-play curriculum should. These results suggest that a self-evolving multimodal agent can strengthen its scientific reasoning from unlabeled scientific images alone, with the model itself serving as the verifier.
Figures & tables
Figure 1: Trinity training loop. The policy πθ as Questioner proposes G candidate QAs {(q,a)} ; the EMA Verifier πϕ screens them and selects (q∗,a∗) ; the policy as Solver produces K completions {(τ,a^)} , which the Verifier scores. RQ and RS drive one joint update ∇θLtrinity , after which ϕ←αϕ+(1−α)θ .
Model
MathVision
MathVerse
MathVista
MMMU
EMVista
SciVQR-Bio
MME-SCI
Qwen3-VL-8B-Instruct
52.3
58.2
76.7
57.0
44.8
56.7
19.0
MM-Zero [ 10 ]
39.6
45.1
67.2
58.3
45.0
62.5
17.6
DPE [ 9 ]
53.9
57.2
76.2
69.1
44.1
60.8
20.4
Trinity (Vero-600K)
57.8
71.0
79.6
69.6
47.0
65.3
20.3
Trinity (OmniScience)
58.2
70.6
79.6
70.7
47.0
62.5
21.9
Table 1: Performance comparison with Self-evolving VLMs using Qwen3-VL-8B-Instruct. Trinity is trained on Vero-600K or OmniScience images.
Figure 2: Reward dynamics for Qwen3-VL-8B on OmniScience [ 18 ] (steps 0–299).
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Model
MathVision
MathVerse
MathVista
EMVista
SciVQR-Bio
MME-SCI
Qwen2.5-VL-7B-Instruct [ 2 ]
26.6
50.0
70.7
43.9
38.3
11.1
VisPlay [ 7 ]
31.2
39.1
–
–
–
–
iReasoner [ 17 ]
25.3
45.9
69.7
–
–
–
V-Zero [ 20 ]
27.0
42.6
69.2
–
–
–
EvoLMM [ 19 ]
24.8
44.9
70.5
46.3
38.3
12.0
Trinity (Vero-600K)
27.7
51.5
71.9
46.9
41.1
11.7
Appendix
Table 2: Qwen2.5-VL-7B-Instruct results (accuracy, %) against published self-evolving methods on the same backbone. Mathematical numbers for VisPlay [ 7 ] , iReasoner [ 17 ] , and V-Zero [ 20 ] are taken from the respective papers; all other values are measured by us. Science benchmarks use a single run with the same prompt for all three models and the judge scoring of Appendix A.4 . VisPlay, iReasoner, and V-Zero have not released checkpoints, so we could not evaluate them on the science benchmarks (‘–’).
Figure 3: Case A — a stated constraint that changes the answer. The largest ppr in the figure belongs to group 5 ( 1.33 ), which the question’s τF=12 ms condition excludes; the answer is group 4 ( 1.04 ). A model that ignores the constraint and reports the maximum is wrong, and the Solver’s transcript shows the exclusion being made explicitly. Panels b–g of the source figure are omitted here for space. Source figure: Nature Communications , 10.1038/s41467-022-35395-y , reproduced under CC BY 4.0. OmniScience subject annotation [ 18 ] : Biology .
Figure 4: Case B — cross-panel mapping under domain vocabulary. Panels d–f carry no titles of their own, so the mutant identity of each must be carried over from the trace panels a–c above them, and the question’s terms (PIP2 regulation, open probability, voltage dependence) are what make the relevant curves findable at all. Source figure: Nature Communications , 10.1038/s41467-024-52469-1 , reproduced under CC BY 4.0. OmniScience subject annotation [ 18 ] : Biology .
Figure 5: Case C — normalization reverses the visible ordering. Kelly is the taller bar in both charts, so comparing bar heights gives the wrong cell line; the two charts also differ tenfold in axis range (ITCH 0 – 1.5 , TP73 0 – 15 ), which is only apparent from the tick labels. Forming the ratio inverts the ordering ( 0.10 for Kelly against 0.49 for BE2). Remaining weakness: with two candidate cell lines, guessing succeeds half the time, so this episode is better read as an illustration than as evidence of reasoning on its own. Source figure: Scientific Reports , 10.1038/s41598-020-57854-6 , reproduced under CC BY 4.0. OmniScience subject annotation [ 18 ] : Medicine .
Figure 6: Case D — a shared visual scale over different numerical ranges. The four boxes occupy similar heights on the page, but only MYH3 is plotted on an axis spanning 10−3 to 1.1×106 while the other three span roughly 0.03 to 8 ; the five-order-of-magnitude difference is legible only from the tick labels. The canonical differentiation marker among these genes is MyoG, whose median sits more than five orders of magnitude below that of MYH3 once the axes are read. Source figure: Scientific Reports , 10.1038/s41598-018-30123-3 , reproduced under CC BY 4.0. OmniScience subject annotation [ 18 ] : Pharmacy .
Self-evolving vision-language models train on questions they generate from unlabeled images. Since these questions have no gold answers, prior methods label them by majority vote over sampled answers or by a model judge. In a human evaluation, we find that 24% of majority-vote labels and 18% of model-judge labels produced during self-evolution are wrong. To address this problem, we present Verifiable QA Generation for Self-Evolving Models (VQS), which changes how the model judges answers. Instead of voting on an answer, the model parses each image into a structured record, such as a scene graph, a chart table, or a diagram graph. Fixed programs then write a question from the record and compute its answer. The model still acts as a visual checker, but it only confirms the individual facts the program reads, one short claim at a time. These claim-level checks select the parser's training targets, so the parser also improves without labels. Human raters find 94% of VQS answers correct, against 76% for majority voting. Across ten benchmarks, VQS improves Qwen3-VL by up to 3.18 points at the 2B, 4B, and 8B scales and outperforms the strongest self-evolving baseline at each. Gains keep growing over three training rounds, reaching 3.84 points at 2B. Code is released at https://github.com/ahmedheakl/VQS
Ahmed Heakl, Sungik Choi, Moontae Lee +1
LG AI Research · MBZUAI · University of Illinois at Chicago +1
Vision-language models (VLMs) have achieved strong multimodal reasoning capabilities, but further improving them still relies heavily on large-scale human-constructed supervision for post-training. Such supervision is costly to obtain, especially for reasoning-intensive multimodal tasks where questions, answers, and feedback signals must be carefully designed. This motivates self-evolving learning, where a model improves itself through a dual-role closed loop: a questioner autonomously poses questions and a solver learns to solve them. However, we observe that current VLM self-evolving methods still face three major challenges: coarse-grained role alternation delays the interaction between question generation and solver adaptation; generated questions can progressively degrade in quality; and question types may collapse toward a narrow distribution. These issues limit the efficiency and reliability of self-evolution. Thus, we propose RISE, a reliable self-evolving framework for vision-language models. RISE is built on three complementary designs: fine-grained role alternation, which shortens the feedback loop between the questioner and the solver to improve efficiency; a quality supervisor, which improves question validity and pseudo-label reliability; and skill-aware dynamic balancing, which mitigates mode collapse and maintains broad skill coverage during evolution. Together, these components enable more reliable and effective self-evolution from unlabeled images. Experiments on two VLM backbones across seven benchmarks show that RISE consistently improves the base models, yielding broad and sustained gains. Our code is publicly available at https://github.com/AMAP-ML/RISE.
Vision-language models (VLMs) have achieved strong performance in multimodal reasoning, yet they remain prone to generating plausible but incorrect answers. Self-verification offers a practical way to improve answer reliability without relying on external judges, but existing methods typically depend on a single verification criterion or fixed prompt, resulting in incomplete and unstable reliability estimates. We first systematically analyze how verifier capability and prompt design affect verification performance. Our findings show that stronger verifiers provide more reliable judgments, while verification performance is highly sensitive to prompt choice, with no single prompt consistently dominating across tasks. Guided by these findings, we propose MOTIVE, a Multi-View Self-VerificatiOn wiTh RelIability-Guided SelectiVE Rethinking framework for reliable multimodal reasoning. MOTIVE evaluates each candidate answer from complementary verification perspectives and learns a correctness-aligned reliability score through correctness-grounded multi-view verification learning. During inference, this score governs an accept-or-rethink decision, allowing reliable answers to be returned directly while uncertain ones trigger history-guided rethinking. Extensive experiments across diverse multimodal benchmarks and VLM backbones demonstrate that MOTIVE consistently outperforms strong self-verification and self-correction baselines. Further results show that reliable verification improves accept-or-rethink decisions and reduces unnecessary reasoning turns, enabling more reliable and efficient self-verification without an external judge.
Ziquan Zhu, Hanruo Zhu, Si-Yuan Lu +10
University of Leicester · Nanjing University of Posts and Telecommunications · University of North Carolina at Chapel Hill +5