Empowering Small VLMs to Think with Dynamic Memorization and Exploration
Authors: Jiazhen Liu
Yuchuan Deng
Long Chen
Venue: ICLR 2026 Poster (2026)
Keywords: GRPO
SFT
Small-scale VLM
Computer Vision
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Small-scale Vision-Language Models (SVLMs) are exceptionally well-suited for proprietary tasks. Equipping them with thinking capabilities is a critical step to enhance their performance and reliability in these specific domains. However, existing training paradigms, including Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Reward (RLVR), impose substantial demands on the base VLM, exceeding the capacity of SVLMs. Consequently, directly applying these paradigms to SVLMs fails to instill the desired thinking abilities. A natural solution is to combine SFT and RLVR, leveraging their complementarity to reduce the dependence on model capacity. Yet the core challenge lies in managing the inherent trade-off: excessive reliance on SFT can force the model to memorize pseudo thinking traces, while over-emphasizing RLVR can lead to unstable exploration (i.e., advantage collapse). To address this, we propose DyME, a novel training paradigm that Dynamically selects between Memorization (via SFT) and Exploration (via RLVR) at each optimization step. By ensuring that every update contributes to the trade-off, DyME serves as a robust, standalone strategy that stabilizes SVLM learning. Complementing this paradigm, we further introduce a synergistic Visual Supervision mechanism (comprising a visual checker and refiner) designed to inject dynamically enhanced, image-grounded guidance during optimization. Extensive experiments across diverse domains demonstrate that DyME consistently achieves this balance, and thus delivers substantial performance improvements on specialized tasks. These results establish DyME as a practical and effective solution for empowering SVLMs with reliable thinking capabilities.

Published as a conference paper at ICLR 2026
EMPOWERING SMALL VLMS TO THINK WITH DY- NAMIC MEMORIZATION AND EXPLORATION
Jiazhen Liu Yuchuan Dengo, and Long Chen" The Hong Kong University of Science and Technology https: //g::///iii thub. com/ www/HKUST--eA MerussT--engGrouue /DyMR
ABSTRACT
Small-scale Vision Languae Models (SVLMs) are exceptionally well-suited for proprietary tasks. Equipping them with thinking capabilities is critical step to enhance their performance and reliability these specific domains. However, existing training paradigms, including Supervised Fine-TTuning (SFT) and Rein- forcement Learning with Verifiable Reward (RLVR), impose substantial demands on the base VLM, exceeding the capacity of SVLMs. Consequently, directly ap- plying these paradigms to SVLMs fails to instill the desired thinking abilities. A natural solution is combine SFT and RLVR, leveraging their complementar- ity to reduce the dependence on model capacity. Yet the core challenge lies in managing the inherent trade-off: excessive reliance on SFT can force the model to memorize pseudo thinking traces, while over-emphasizing RLVR can lead to unstable exploration (i.e. advantage collapse). To address this, we propose DyME, a novel training paradigm that Dynamically selects between Memorization (via SFT) and Exploration (via RLVR) at each optimization step. By ensuring that every update contributes to the trade-off, DyME serves as robust, standalone strategy that stabilizes SVLM learning. Complementing this paradigm, we further introduce synergistic Visual Supervision mechanism (comprising visual checker and refiner) designed to inject dynamically enhanced, image-gun pagee-grounde guidance during optimization. Extensive experiments across diverse domains demonstrate that DyME consistently achieves this balance, and thus delivers substantial per- formance improvements specialized tasks. These results establish DyME a practical and effective solution for empowering SVLMs with reliable thinking capabilities.
INTRODUCTION
Equipping Vision-Language Language Models (VLMs) with thinking capabilities is pivotal step that moves them beyond recognition toward reasoning. Recent studies have advanced this goal through spe- cialized training, achieving strong results on spectrum of visual tasks, from recognition-intensiee applications like grounding (Lai et al., 2025; Liu & Chen, 2025; Peng et al., 2025; Liu et al., 2025c;a) to -eatoning-intensivve challenges such as chart understanding (Zhang et al., 2025a; Xia et al., 2024) and geometric problem solving (Shen et al., 2025; Chen et al., 2025b; Xia et al., 2025). While this progress is significant, the success of these approaches is contingent upon the base VLM possessing strong foundational capabilities, namely, sufficient capacity and robust instruction adherence (Yang al., 2025a). In practice, only handful of VLMs meet these prerequisites, presenting significant challenge for Small-scale VLMs (SVLMs) which struggle to develop thinking capabilities under existing training paradigms.
To contextualize this limitation, we briefly review the two dominant paradigms, both of which are primarily tailored for Large-scale VLMs (LVLMs). 1) Supervised Fine-Tuning (SFT) on Chain-of- Thought (CoT) data (Xu et al., 2024; Li et al., 2024b; Xia et al., 2025; Gao et al., 2025): VLMs are supervised memorize predefined thinking patterns from large-scale CoT annotations. Since CoT data are often verbose and contain much vision- inreevaatteeea content, models must possess sufficient capacity absorb long textual content without compromising visual grounding (Marafioti al.,
"Corresponding author (longchen@ust.hk)

---

Published as a conference paper at ICLR 2026
After CoT SFT training
During RLVR training
Onguu Image)
Ropal irvodiie!
Which year shows grattr diveggenc opinions abont Rearil's rounany, 2010 or 2012? Oupu the thinking process and then give the final answrr in <anowero <lanswer? tag.
Which
aconunyy? Qutput
thinking process Eeet give finel mnswer in canswer <aanwwrr" tng
We locate 2010 and 2012.
To anower the questior, relevam the values each Compare the ditforences, the year with greater divergence 2010, <arswer 201 Caanswer
To answer the quesion, we clamine the chat and compane the value diflerences for each year. In 2015, the valurs are 87 and 13, showing the signtficant divegence.
Then, extract
first
and 36, hus
35. Comparing 62 26
<.65 35-35 35 - 30, the your with grate diveggence is 2012. Sanner-2012s/unne
c//anoerr
2015 has the greatest divesgence.
Large VLM
Small-scale VLM
LVLMs:
vision preserved
SVLMs: vision compromised
SVLMs poorly follow instructions Advantage collapsing (fail)
Grounded thinking traces + Pseudo thinking traces (lail)
a) SFT and RI. paradigms fail to enable SVLMMs to think.
Two-stage training
DyME training
Extent
1. Locate the year,
Constrained exploration
3. Get 2012-/anwwwrr
2. Extract the value:
RT.
safe to explore
RE
(hard to achirve)
(single step)
SFT
grounded value from the chart Afte thinking.
<anower:2211 2</anower>>
RL
Thinking capabllities
Switcher
Switcher
Thinking capabilities
(numerous steps)
2012 has the greatest div- orgence.
need to memorize
Advantage collapse
SFT
SFT
format answer)
RI
b) Two-stage training vs. DyME
Figure 1: Training paradigms for enabling VLM thinking. The LVLM is Qwen2.5-VL-322 (Bai al., 2025) and the SVLM is SmolVLM-500M (Marafioti et al., 2025). (a) Existing paradigms are effective for LVLMs but unsuitable for SVLMs. (b) The two-stage training paradigm (SFT - RL) faces challenging trade-off. Our proposed DyME dynamically balances this trade-off.
2025). This capability gap is illustrated in Fig. la: After SFT, LVLMs can generate grounded thinking traces with accurate intermediate values (in green), while SVLMs cannot. 2) Reinforcement Learning with Verifiable Reward (RLVR) (Zhang al., 2025a; Chen et al., 2025b; Peng et al., 2025; Shen et al., 2025): on the other hand, promotes exploration of thinking patterns rather than imitations. In this paradigm, VLMS are instructed to generate thought process followed by a strictly formatted answer (e.g., enclosed in tags). This format enables verifiable rewards to reinforce correct generations and penalize incorrect ones. Owing to its reliance on instruction adherence, this approach is practical primarily for strong VLMs that can reliably generate structured outputs.
Consequently, both established paradigms are inadequate for instilling thinking in SVLMs. The extremely limited capacity (e.g., under IB parameters) of SVLMs renders the SFT paradigm ineffective, as high volume of tex- tual information in CoT data can overwhelm the capac- ity (Marafioti et al., 2025; Chen al., 2025a). Moreover, the limited instruction adherence of SVLMs frequently results in unverifiable outputs (Chu et al., 2025; Guo al., 2025), precipitating advantage collapse during RLVR. We quantitatively verify these limitations (cf., Fig. 2): both SFT and RLVR paradigms indeed impair the performance.
Baanine
4CoT
+RLVR
Two--tage
40
75
Figure 2: Performance of SmoIVLM-
Considering that SVLMs offer high efficiency and are crucial for deployment on edge devices (Marafioti et al., 2025), enabling them to think addresses a strong prac- tical demand. Thinking enhances the reliability and performance of vision tasks (Zhang et al., 2025a), and task-specific SVLMs provide compelling alternative to LVLMs in resource-con comsurrcc-o settings. This motivates the development of a new training paradigm that empowers SVLMS with thinking capabilities, at least for specialized tasks.
500M (Marafioti et al., 2025)
ChartQA (Masry et al., 2022). Ex- isting paradigms degrade performance, whereas DyME yields improvements.
2

---

Published as a conference paper at ICLR 2026
A promising solution is to fuse SFT and RLVR, well-calibrated trade-off can lower the high demands on the base model (DeepSeck, Inc., 2025; Yan et al., 2025): SFT encourages the model to memorize verifiable thinking patterns to prevent advantage collapse, while RL forces exploration to prevent rigid templates from overwhelming the model's capacity. The central challenge, however, is that SVLMs struggle to achieve this balance. Existing hybrid methods, like two-stage training (Chen et al., 2025a; Chu et al., 2025) or annealed SFT losses' (Zhang et al., 2025b), rely on static trade-off' governed by hyperparameters set empirically. This rigidity is the critical flaw because the minimal capacity of SVLMs means the window for a successful static balance is incredibly narrow, making failure almost inevitable (ef. Fig. lb). Our repeated trials with two-stage training confirmed this issue, with performance often falling below the baseline (cf. Fig. 2).
SVLMS therefore require a more intelligent paradigm to navigate this trade-off. To this end, we propose DyME (Dynamic Memorize-Fxppen Explore), which integrates SFT and RLVR through a dynamic switching mechanism. As illustrated in Fig. lb, DyME assesses the model's generation each step and adapts its training mode accordingly. When the model fails to follow instructions, it switches to memorization mode (SFT) to guarantee stable optimization signals. Conversely, for valid generations, it engages an exploration mode (RLVR) to encourage diverse and grounded thinking. This state-driven approach ensures memorization and exploration are always complementary, dynamically maintaining the delicate trade-off. While this dynamic switching alone guarantees training stability, we further maximize the model's potential by incorporating a synergistic Visual Supervision mechanism. This module facilitates an adaptive interaction: the CoT ground-truth guides the scoring of exploration (via a visual checker), while successful exploration traces dynamically refine the CoT ground-truth (via a visual refiner).
The aforementioned design makes DyME a highly effective paradigm for empowering thinking in SVLMs for specific tasks. We validate this across three diverse domains, ranging from recognition- intensive tasks (medical VQA) to reasoning-itensive problem solving). Remarkably, using only a few thousand training samples, DyME achieves sub- stantial performance gains, enabling it to match or even surpass several LVLMs. Our primary contributions are as follows:
intensive challenges (chart understanding and geometric
1. We propose DyME, the first training paradigm that equips SVLMs with thinking capabilities, substantially reducing reliance on the base VLM's initial capacity.
2. Through dynamic switching and synergistic supervision, DyME alleviates pseudo thinking traces and advantage collapse in SVLMs, yielding amage-groundee thinking and consistent performance improvements.
3. We demonstrate the effectiveness and practicality of DyME across three diverse domains, each consistently showing substantial performance gains with only few thousand training samples.
2 RELATED WORK
Vision-Language Models. Modern VLMs, such LLaVA (Liu al., 2024a) and Qwen-VL. (Bai et al., 2023), have demonstrated remarkable capabilities across a wide array of vision tasks. How- ever, their substantial parameter counts and computational demands restrict their use in resource- constrained environments like edge devices. This has motivated a growing interest in SVLMs designed for efficiency (Zhou al., 2024; Marafioti al., 2025; Korrapati, 2024). Although works like TinyLLaVA (Zhou et al., 2024) and SmolVLM (Marafioti et al., 2025) have shown that carefully designed SVLMs can achieve competitive performance, they exhibit a critical weakness. Recent studies highlight that their performance degrades significantly on tasks requiring complex, multi-step instruction following, indicating a gap in their compositional understanding and general reasoning abilities (Albalak et al., 2022; Ghosh et al., 2024; Liu et al., 2025b).
Empowering Thinking Capabilities in VLMs. Recent advances LLM thinking (e.g., GPT- ol (OpenAI, 2024), DeepSeek-RI (Guo et al., 2025)) have motivated efforts to equip VLMs with similar capabilities via dedicated training paradigms.
SFT CoT data (Xu et al., 2024; Xia et al., 2024; 2025; Gao et al., 2025; Yang et al., 2025b). This paradigm leverages large-scale CoT supervision to teach models to memorize and generalize thinking patterns. Multimodal-CoT (Zhang et al., 2023) was an early attempt using fused visual-text
See the supplementary material for further comparison.
3

---

Published as a conference paper at ICLR 2026
inputs, but its small scale data limited genuine thinking. Subsequent works highlight the role of scale: G-LLaVA (Gao et al., 2025) constructs 170K seometry-specific CoT samples; ChartVLM (Xia et al., compiles large chart corpus; and LLaVA-CoTT (Xu et al., 2024) well as R1-One (Yang et al., 2025b) curate diverse, structured CoT data through large-scale prompt engineering. These approaches face long inputs, requiring large VLMs that can process rich textual information while preserving visual grounding (Marafioti et al., 2025; Zhai et al., 2023).
RL with Verifiable Reward (RLVR) (Zhang et al., 2025a; Chen et al., 2025b; Peng et al., 2025; Shen et al., 2025; Liu et al., 2025c). RLVR adopts a distinct paradigm that elicits thinking through autonomous exploration with minimal external supervision. The popularly used algorithm is Group Relative Policy Optimization (GRPO). introduced by DeepSeck-Matt (Shao et al., 2024), which exploits models' ability to produce structured outputs that separate thinking from final answers. leverages rule-verifiable data to optimize high-scoring generations, while light SFT is employed for cold-start when the output structure is unclear. This paradigm has been extended to VLMs in several works. RI-V (Chen et al., 2025b) applies GRPO to VLMs, enabling thinking in tasks such as counting and geometry. LMM-RI (Peng et al., 2025) introduces a two-stage pipeline that transfers textual thinking into multimodal learning. VisualRFT (Liu et al., 2025c) and RI-VL (Zhang et al., 2025a) incorporate vision- Since GRPO depends models' initial structured thinking ability, these methods typically build on strong VLMs, such as the Qwen- VI. series (Bai et al., 2025).
rewards to guide fine-graine mee-grained, visually grounded optimization.
Hybrid Training Paradigms (Chu al., 2025; Yan et al., 2025; Zhang al., 2025b). To harness the complementary strengths of SFT and RL, researchers have also investigated hybrid paradigms. A common approach is a two-stage training process (Chu et al., 2025) that first uses SFT to teach the model the desired output format, followed by RL for exploration. Although intuitive, this method is highly sensitive to the amount of SFT, a parameter that particularly challenging to tune for SVLMs, as these smaller models can easily become trapped in suboptimal states. Alternative strategies attempt to continuously blend SFT with RI., for instance, by incorporating SFT as an annealed auxiliary loss (Zhang et al., 2025b) or by managing its influence with an empirical shaping function (Yan et al., 2025). However, all these strategies ultimately rely on an empirically determined balance between the two paradigms. This rigidity represents critical flaw when applied to SVLMs. The absence of adaptive control over the SFT weight renders these methods brittle and unreliable.
Thus, existing paradigms not directly transferable to SVLMs due their inherent limitations in model capacity and instruction follloiinnn ability. This highlights the need for a novel training paradigm that imposes minimal requirements on the base VL.M.
APPROACH
3.1 PRELIMINARIES
We first briefly recap the two training paradigms (SFT and RLVR) that underlie our method. Let D {(x; ) be the training set, where x denotes the input (e.g. an image-instruction pair) and y the desired output. The model defines a conditional distribution po (y) x)) with parameters O.
Supervised Fine- Sine-Tuning (SFT). For each training pair (æ, y) in D, SFT updates the model by minimizing the negative log- likelihood (cross-entropy) of the desired output y under the conditional distribution (y r):
ESF (0) - BB ( Po x)].
This teacher-foren forcing loss allows models to memorize extensive training examples, compelling the model to absorb this knowledge.
Group Relative Policy Optimization (GRPO). GRPO is RL algorithm that explores open-ended generation by comparing candidate outputs within a group. For each input x, the policy pe samples a set (5/ | a reward function r. (T²) ) is computed based on the correctness of the output answer, and each sample's ' advantage A is measured relative to the other group members:
A(J*)
(g
o+E
Fa
Fo =
K
K Ta
==
K
M
K
(ra(i') - -)2,
(2)
j=1
4

---

Published as a conference paper at ICLR 2026
where is small constant for numerical stability. The policy then updates its parameters by minimizing the following loss, regularised by KL constraint:
CORPO (0) EDDD B
min (re(z, D) A()) clip(ro(z, 11:1-),111111 c)
po P
+ 3 DEL (po(11x) Prer(- [z)] where ro(a, 7)
Pota (ū x)
The clip and KL terms work together to keep each update close to safe regions of the parameter space: the clip gate limits step size around the rollout policy Pald, while the KI. term (8Dxt.) tethers the policy the reference Perr (typically the initial model).
Gradient Compatibility of SFT and GRPO. Below, we reveal that the optimization objectives of SFT and GRPO are formally equivalent, with the former targeting the ground-truth data distribution and the latter an internal one.
The gradient of the SFT loss is straightforward:
VoLsFT(0) -B(,.))> [V log (y x)].
(4)
Similarly, the GRPO gradient (ignoring clipping and any KL-penalty) can be written
V VeLGRPO(0) = pm Sr-pan
[re A Ve lo x)]-
(5)
This comparison shows that the SFT gradient is a special case the GRPO gradient, obtained when the ground-truth sample is used with unit advantage. This equivalence enables unified loss that balances external imitation (SFT) with internal refinement (GRPO). Achieving this fusion requires dynamically weighting the two signals ($3.2) and ensuring stylistic consistency between external ground-truth and self-generated outputs ($3.3).
3.2 DYNAMIC MEMORIZE-EXPLLE (DyME)
To realize this complementarity, we propose the Dynamic Memorize- Explore (DyMB) paradigm, which adaptively switches between SFT and GRPO each training step. In the following, first outline the overall pipeline and then elaborate on the optimization procedures for each mode.
Overall. As shown in Fig. 3a, each training step begins with an input (1.q). where I is the image and q is an instruction. The policy SVLM Pe generates responses {7 1 Each response is parsed into thinking trace and final answer, which is then verified for correctness using predefined rules. The verification results fall into two categories: either all responses are incorrect (including those that fail to parse), at least one is correct. The decision rule: if at least response is correct, the model proceeds with GRPO-based exploration; otherwise, it falls back to SFT-based memorization. Formally, the training mode is switched as:
mode(x)- GRPO, if max; (igh =
(6)
SFT, otherwise,
where (ith) E {0, 1} indicates whether ÿ passes rule-based verification. Though simple, this decision rule highly effective. When all responses are incorrect, the answer rewards are essentially all zero and the normalized advantages become dominated by noise, making GRPO updates for small SVLM unstable. In this regime, falling back to SFT provides low-variance, ground-truth guided gradient. Conversely, the appearance of at least one correct response indicates that the current policy has already discovered a feasible solution for this input, so GRPO can safely exploit the relative advantages to drive exploration.
GRPO Mode. DyME introduces a key refinement to the original GRPO: beyond the answer reward Ta. it incorporates auxiliary reward r, for thinking traces. This reward is computed by evaluating the generated traces against expected thinking patterns (e.g., via token-level F1 score ground-truth comparison), promoting structured thinking.
Given these rewards, we update the policy using a modified GRPO objective. Unlike the standard formulation (Eqs. 2 & 3), we omit the KL penalty and clipping terms, the dynamic integration of
5

---

Published as a conference paper at ICLR 2026
Given visual fucts
Any correct
GRPO
From the figure, values represented by the three bars can directly observed. Taking their mean yiclds an overall average approximatell 8.3, which provides concise summury of the distribution reflected Visual Checker:
responses?
Switcher
or
SVLM
D Overall pipeline
Visual Refiner
x
Answer Reward
A1
Visual Refiner
To answer the question, step1 I is to extract the values for Latvia, Portugal, and Romunia: 3.0, 8.9, and 13.0. respectively. Step 2 is to calculate the
1,
A2
3
Visual Checker
As
average these values: (3.0 8.9 13.0) 3 8.3, Therefore, the average is 8.3.
(0, Hor. 10r2. 0)
thinking
A4
logits
Visual Checker: 1.0
GRPO mode
SFT mode
(a) The pipeline for DyME.
(b) Visual refiner and checker.
Figure 3: Workflow and module components of DyME. At each training step, DyME' dynamically switches between memorization (via SFT) and exploration (via GRPO) modes based on its generations. Visual supervision is introduced through the visual refiner and visual checker. The refiner enhances the targets for memorization by incorporating richer visual elements (green), while the checker rewards the thinking context generated based on their visual relevance.
SFT already stabilizes training. This avoids overly conservative updates and yields cleaner gradient form, enabling smoother alignment between SFT and GRPO:
(o) EE-D Eg>po(-la>) [ra(z, 5) A(7),
where A(j*) is the group-normalized advantage calculated from the combined answer (ra) and thinking (r:) rewards, and r2 8*))
po (f)=) is the importance sampling ratio.
(=]()
SFT Mode. When training falls back to SFT, the model is optimized toward the ground-truth response using the standard supervised loss (Eq. 1). This ensures that whenever the model fails to explore effectively, it receives a stable, ground-truth- gruth--uided gradient update correct its behavior.
DyME Objective. The final loss dynamically combines the two objectives based on response correct- ness:
LDyME max (max).(00)= - (GRrO(()) + (1-1 max max ) =1)) Lsrr(f), (8)
where 1[:] the indicator function, returning if the condition holds, otherwise.
3.3 VISION SUPERVISION
DyME with Visual Supervision. While the aforementioned Pure DyME (using standard ra and static ground-truth) already guarantees training stability through its dynamic switching mechanism, we can further exploit this dynamic nature to maximize performance. Specifically, the switching mechanism allows us to tailor the supervision signals at each optimization step: refining the reward during exploration and enhancing the ground-truth during memorization. To this end, we introduce checker-refiner framework (cf. Fig. 3b), which constitutes the Full DyME.
This framework reorganizes the ground-truth to adhere predefined structure, crucially transform- ing it into a grounded thinking trace. The refiner restructures the external ground-truth into structured, visually grounded responses, while the checker evaluates self- generated outputs for their structural organization and coverage of visual content. We refer to the resulting supervision signals collectively vision supervision. The implementation is carried out via LLM-based prompt engineering.
Visual Facts I, are central to realizing vision supervision. They are defined as fine- aine-grained visual components extracted from each image, including objects, attributes, and states. These elements play

---

Published as a conference paper at ICLR 2026
dual role: blocks for constructing complete ground truth responses.
they provide evidence for evaluating generations against the image and serve building
Visual Checker. The visual checker evaluates responses along two dimensions: (i) whether the output contains sufficient correct visual elements compared to L., and (ii) whether it aligns stylistically with provided examples. These examples may be manually defined extracted from the SFT ground-truth.
The refiner produces visually grounded responses for SFT by leveraging the model's validated explorations. High-scoring traces identified by the visual checker are stored in dynamic example pool. An LLM then draws from this pool to generate ground-truth responses, integrating structural templates with visual facts from I. and referencing the collected examples.
Visual Refiner.
In essence, the acquisition of Visual Facts, the evaluation by the Visual Checker, and the synthesis by the Visual Refiner are all implemented via structured prompt engineering using Qwen2.5-14B. Please refer to the Supplementary Materials for the full prompts used in our pipeline.
4 EXPERIMENTS
To rigorously evaluate DyME, we structure our experiments into two parts: (1) Algorithmic Val- idation, where we evaluate "Pure DyMB" in a controlled setting using offline data to isolate the contribution of our dynamic switching mechanism; and (2) System Effectiveness, where we evaluate the full DyME pipeline (with Visual Supervision) across diverse domains to demonstrate its practical capability in empowering SVLMs.
4.1 PART I: ALGORITHMIC VALIDATION (PURE DYME)
Setup. Since SVLMs lack intrinsic reasoning capabilities and cannot autonomously discover complex reasoning paths, pre-constructed CoT data is mandatory prerequisite for all training paradigms. We therefore evaluated all methods on ChartQA (Masry et al., 2022) using L.L.V. -LLVA--V-S et al., 2024a), the 0.5B variant, with three pre-constructed CoT datasets of varying qualities: Low (Undesigned) containing unstructured traces (~880 words); Medium (Standard) consisting of semi-structured traces (~~99 words) from Qwen2.5-14B: and High (Premium) comprising highly structured traces 1~142 words) from GPT-4o. Following established protocols (Liu et al., 2023; Masry et al., 2022), we report relaxed correctness, which allows 5% tolerance for numerical answers.
We present a threefold evaluation to validate data robustness, design optimality, and generalization:
(1) Robustness to Data Quality. Table (a) demonstrated DyME's superiority. On Low quality data, Pure DyME (61.9%) significantly outperforms the unstable Two-stage baseline (57.6%). Remarkably, using only Medium data, it surpasses the SFT baseline trained on premium High (GPT-40) data (61.6%). This confirms that DyME acts as robust student, effectively maximizing data efficiency.
(2) Optimality of Binary Switching. To validate our binary design, compared it against three alternative switching heuristics in Table (b); (i) Reward Thresholding, which switches to RL only the batch average reward exceeds a threshold t; (ii) SFT Annealing, which applies a weighted SFT loss alongside RL at every step: and (iii) SFT Budget, which performs focused SFT updates on accumulated failure cases (hard mining).
Reward Thresholding proves brittle, collapsing at suboptimal thresholds (t = 0.5, 52.4%). SFT Annealing incurs a heavy computational tax (+25%) due to the auxiliary SFT gradient calculation. SFT Budget yields inferior results (59.6%) as overwhelming the model with concentrated failures destabilizes learning. In contrast, DyME's 's binary switch is parameter- efficient, and empirically optimal (64.9%).
Results:
(3) Mechanism Generality. Going beyond the primary setup, while DyME is primarily tailored for SVLMs, we verify the universality of its core switching mechanism (see Supplementary). In the text-only domain, it boosts the small-scale Qwen2.5-0.5B on GSM8K (Cobbe et al., 2021) to 55.3% (+5.8% over GRPO), confirming DyME is an effective paradigm for empowering thinking in small- parameer models regardless of modality. Moreover, the paradigm scales effectively: on the stronger Dwe.2.5-VI.-7B. it further improves ChartQA performance to 89.6% (+2.3%).
7

---

Published as a conference paper at ICLR 2026
Table Algorithmic Validation of Pure DyME. (a) DyME outperforms SFT and Two-stage variants (w/ and w/o KL penalty) across all data qualities. (b) The binary switch is more robust and efficient than soft or hard-mining alternatives (evaluated on Medium data).
(a) Robustness across
Data Quality
(b) Switching Strategy Ablation
Method
Low
Medium
High
Strategy
Hy Hyperparam.
Acc.
Cost
SFT
50.5
57.8
61.6
Reward Threshold
0.5/0.8/0.9
52.4/64.1/63.
Nonc
Two-stage
$7.6
59.9
54.5
SFT Annealing
Cosine
64.0
+25%
Two-stage (w/ KI.)
55.4
60.8
62.7
SFT Budget
Hard Mining
59.6
Budget-dep.
Pure DyME
61.9
64.9
68.5
Binary Switch (Ours)
64.9
Baseline
4.2 PART II: SYSTEM EFFECTIVENESS (FULL. DYME)
Having validated the algorithmic core, we now evaluate the Full DyME pipeline, augmented with Visual Supervision, across three diverse domains: Medical VQA, Chart Understanding. and Geometry. Each followed the evaluation protocols of prior work (Zong et al., 2024).
Setup & Source of L. Unlike Part I, here we activate the Visual Supervision module to enable the full online loop. Crucially, to demonstrate DyME's capability to bootstrap from raw signals, we utilize the "Undesigned" CoT data (defined in $4.1) derived from SLAKE (Liu et al., 202 I), ChartQA (Masry et al., 2022), and Geo170K (Gao et al., 2025) as the common training source for all methods. Acquiring the necessary visual facts (I,) is a fully automated process: we leverage standard domain tools (e.g., BiomedGPT (Zhang et al., 2024a) for medical, DePlot (Liu al., 2023) for charts) or prompt generalist LLMs (e.g., Qwen2.5 (Team, 2024)) to parse images into structured textual descriptions. The automated pipeline and prompts are included in the supplementary.
Evaluation Protocol. We used official train-test splits for SLAKE (Accuracy/Recall) and ChartQA (Relaxed correctness). For Geometry, since Gco170K (Gao al., 2025) provides no test set, we evaluated Accuracy on Math Verse (Zhang et al., 2024b), consistent with Zong et al. (2024).
4.2.1 MAIN RESULTS
DyME vs. Existing Training Paradigms. The comprehen- sive results in Table show that DyME consistently deliy- ers substantial gains. Notably, after training with DyME, SmolVLM improves from 49.9 to 55.6 (+5.7), LLaVA- OV-S from 50.7 to 55.4 (+4.7), and InternVL2-S from 56.3 to 58.1 (+1.8). In contrast, cxisting paradigms tend to degrade performance (e.g., SFT lowers SmolVLM to 44.1), validating our analysis that SFT yields pseudo think- ing traces and GRPO faces advantage collapse (cf. Fig. 4).
3.50
3.23
1.00
0.30
GAPO
0.00
326
400
300
100
2:0
DyeE
Reward
DyME effectively mitigates these issues. It promotes grounded traces that concise yet informative (ef. Fig. 5), aligning well with the limited capacity of SVLMs. Importantly, DyME places minimal demands on the base model: even SmoIVLM (0.5B) achieves substantial gains, and it still delivers improvements (+2.6%) on extensively pretrained models like Intern VL2-S. We further corrobo- rated these findings through manual inspection, as detailed in the Supplementary Material.
Figure 4: Training rewards. GRPO and two-stage training suffer from severe advantage collapse.
Matching the Efficacy of GPT-4o Supervision with Open-Source Models. Comparing results between Part I and Part II reveals crucial finding: ALavA-OV- trained with the full DyME pipeline (using the accessible Qwen2.5-144B achieves 67.5% (Table 2). This effectively matches the performance of Pure DyME trained expensive GPT-4oo data (68.5%, cf. Table 1). This proves that full DyME allows open- source supervision to achieve training outcomes comparable to those derived from top-tier proprietary models, eliminating the need for expensive data annotation.
DyME-trained SVLMs Can Be Competitive with LVLMs. We ensured fairness by exposing all baselines to training data. As shown in Table 2, SVLMs trained with DyME can surpass stronger
8

---

Published as a conference paper at ICLR 2026
Table Comparisons across three domains: medical VQA, chart understanding, and geometry solving. The evaluation follows the VLMEvalKit framework (Duan et al., 2024). For SVLMs, existing training paradigms degrade their performance, whereas DyME consistently brings improvements. The best performance achieved by each SVLM is highlighted in bold, with the relative improvement also indicated. Notably, after being trained with DyME, SVLMs achieve performance comparable that of MoVA (underlined).
Model
ViT
LLM
Medical Chart
Geometry Avg.
IVLMs
LLaVA-Med (Li ct al., 2023)
CLIP-VIT-L/I4
Vicuna-78
64.3
Cambrian-1 (Tong et 2024)
Hybrid-3B
Llama3-8F
72.6
22.0
LLaVA-1.5 al. 2024a)
-LI--VYT-L/14
Vicuna-7B
69.4
LLaVA-1.6 al., 2024b)
CLIP-VIT-L/14
Vicuna-7B
78.2
49.2
13.4
47.0
MoVA (Zong al., 2024)
Hyhrid-3B
Vicuna-7B
74.5
68.3
19.7
S4.2
LLaVA-OV-L (Li al., 2024a)
SigLIP-SO400M
Qwen2-7B
75.7
80.9
24.5
60.4
Intem VL-2-L,Chhn ct al,, 2024)
Intern VTT33VTT-300M
MnternL.2.55-71
80.2
82.1
37.3
66.5
SVLMs
SmolVLM (Marafioti et al., 2025)
SigLIP-93M
Mmm3LM2-330M
72.1
63.2
14.6
49.9
CoT SFT
SigLIP-93M
SmoILL2-3600M
60.1
$7.7
14.5
44.1
GRPO
SigLIP-93M
M2-36MM
61.1
53.8
17.1
44.0
Two-stage
SigLIP-93M
MM-6LLM2-360M
59.4
60.1
16.7
45.4
DyME
SigLIP-93M
Smm1LLM2-360M
78.1
69.7
18.9
55.6
(46.0%)
(46.5%)
(45.7%)
LLaVA-OV-S 2024a)
SigLIP-400M
Qwen2-0.5B
74.9
61.4
15.9
50.7
Two-stage
SigLIP-400M
Qwen2-0.5B
74.5
$2.9
16.5
48.0
DyME
SigI.IP-400M
Qwen2-0.5B
78.3
67.5
20.4
55.4
(+6.1%)
(44.7%)
VT.2 (Chen et al,, 2024)
Intern VIT-300M
Qwen2-0.5B
78.3
71.9
18.7
$6.3
Two-stage
Intern ViT-300M
Qwen2-0.5B
73.6
55.7
17.1
48.8
DyME
Intern ViT-300M
Qwen2-0.5B
80.0
74.5
19.8
58.1
(+1.7%)
(+2.6%
(+1.1%)
(+1.8%
LVLMSs like MoVA (54.2) on these specialized domains, with SmolVLM reaching 55.6 and LLaVV- OV-S 55.4. As a result, DyME-trained SVLMMs become reliable options for task-specific applications on resource-constraaine edge devices.
4.2.2 ABLATION STUDY
To dissect the source of these gains, we conducted an ablation study to analyze the contribution of DyME's four core components within the full pipeline: the memorization mode, exploration mode, visual refiner, and visual checker. Table 3 shows the performance impact.
Dynamic Switching Mechanism. The results confirm that Memorization and Exploration are symbiotic. Disabling memorization causes
Table Ablation study. Model: LLaVA-OV-S.
catastrophic drop (55.4 + 43.9), effectively re- verting to unconstrained, unstable exploration. Conversely, removing exploration (50.4) re- stricts the model the static imitation of sub- optimal data. As shown in Fig. 4, their dynamic interplay prevents the advantage collapse ob- served in basclines, ensuring optimization stability throughout the learning process.
DyME Variant
Medical
Geometry
Average
DyNE (full)
78.3
67.5
20.4
$5.4
wio memorization
wio exploration
75.5
61.3
14.5
50.4 pos
visual reliner
75.6
wro visual checker
76.9
64.3
17.1
52.8 (479
Visual Supervision. Removing the visual checker and refiner drops performance by 4.7% and 6.9%, respectively. This validates the pivotal role of visual supervision in bootstrapping from noisy, undesigned data. Given the limited capacity of SVLMs, they are easily prone to hallucination when trained on low-quality traces. The visual components imperfect data is filtered and refined into grounded visual facts (I.) before optimization, thus enabling robust learning from weak supervision.
dynamic denoiser. ensuring that raw,

---

Published as a conference paper at ICLR 2026
8
difference the values of 2017 and 2016?
The difference between
values 2017 2016
Given AB / CD, ingle 50.0, then degree of anglo
Angle equal angle angle 2 is also 50 degrees.
(Aggns Ameggry)
19000.
2006
(Origizal)
Extraction:
Extraction:
data is value 26700 for 2017
AB parallel to CD. =0".
31th
2016
Calculation:
Calculation:
36700 29000 7700
ingle 2- 180" angle
Conctasion:
1807 130".
difTrrence between the 2017 and 2016 valuees is 7700.
Conclasion:
(DyM))
The degres of ofangle 2 130"
X13
1006
3007
1313
201
201
7700
Answer: 130"
Figure 5: Showcases chart understanding and geometry solving, We use LLaVA-OV-S to demonstrate the results. The SVLM originally produces hallucinated answers (red), while the DyME-trained model generates structured thinking traces (green) that incorporate grounded values, effectively improving the performance.
4.3 TRAINING EFFICIENCY & DISCUSSION
We analyze the computational efficiency and performance trade-offs associated with different config- urations of DyME. The comparative results are detailed in Table 4.
Computational Efficiency vs. Data Cost. The frame- work offers two distinct operating regimes catering to different resource profiles. Pure DyME represents the high-efficiency regime: when offline CoT data is pre- constructed, it maintains training throughput comparable to standard GRPO (~ 1s//stpp) while delivering superior performance. In contrast, Full DyME (with Visual Supervi- sion) prioritizes data autonomy. While the online interac- tion introduces a computational overhead (~1.66 training time), it enables the model bootstrap pegfopeefore reasoning solely from open-source models, bypassing the dependency on expensive, proprietary data annotation (e.g., GPT-4o).
Table 4: Cost-Renefit Analysis. Time measured sec/step. Run on 8x H800.
Method
Ext. Model
Time
GRPO (Baseline)
Qwen2.5 14BB
14.8s
60.8
Pure DyMR
Qwen2.5-
14.0s
64.9
DyME
GFT-4ol
19.1s
Full DyME
Qwen2.5- 5-7B
21.2s
66.8
Full DyME
Qwen2.55
23.4s
67.5
Used for offine data construction
Sensitivity to External Model Capacity. For Full DyME, we further examine the impact of the external helper's s size on system performance. As shown in Table 4, replacing the Qwen2.5-14P helper with the smaller 7B variant results in negligible performance variation (67.5% - 66.8%). This indicates that our structured prompt engineering effectively decomposes complex reasoning tasks, allowing necessitating heavy-weight models.
smaller external models to provide sufficient guidance for SVLMs without
Applicability of Visual Supervision. The effectiveness of the Visual Supervision module relies the explicit extraction of Visual Facts (T.). This process creates specific applicability boundaries. For domains involving abstract semantics (e.g., irony in memes) or wnstructured perception (e.g., dense crowds), converting holistic visual signals into discrete text may result in information loss. In such scenarios, reverting the Pure DyME paradigm serves more robust alternative.
5 CONCLUSION
In this work, we introduced DyME, novel training paradigm designed to empower thinking capa- bilities within SVLMs. At its core, DyME combines memorization (via SFT) mode and exploration (via RLVR) mode through a dynamic switching mechanism. Our experiments demonstrate that this approach not only resolves the critical trade-off between these two modes but also yields substantial performance gains on wide spectrum of vision tasks, from recognition-intensi intensive scenarios. The success of DyME is attributed to its carefully designed components: the dynamic switching mechanism addresses pseudo thinking traces and advantage collapse, while the visual checker and refiner provide coordinated, high-qualit, visual supervision. imposes minimal requirements the base VLM, making it broadly applicable to wide range of models. including extremely lightweight SVLMs. Therefore, DyME serves as the practical solution for empowering SVLMS to think.
to reasoning-
10

---

Published as a conference paper at ICLR 2026
ACKNOWLEDGMENT
This work was supported by the Hong Kong SAR RGC General Research Fund (16219025), National Natural Science Foundation of China Young Scholar Fund Category B (62522216), National Natural Science Foundation of China Young Scholar Fund Category C (62402408), and Hong Kong SAR RGC Early Career Scheme (26208924).
REFERENCES
Alon Albalak, Akshat Shrivastava, Chinnadhurai Sankar, Adithya Sagar, and Mike Ross. Data- efficiency with a single gpu: An exploration of transfer methods for small language models. arXiv preprint arXiv:2210.038711 2022.
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang. Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-VL: A versatile vision-language model for understanding, localization, text reading, and beyond. arXiv preprint arXiv:2308. 12966, 2023.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang. Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li. Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang. Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-VL. technical report. arXiv preprint arXiv:2502. 13923, 2025.
Hardy Chen, Haoqin Tu, Fali Wang, Hui Liu, Xianfeng Tang, Xinya Du, Yuyin Zhou, and Cihang Xie. SFT or RL? an early investigation into training R R1-like reasoning large vision-language models. arXiv preprint arXiv:2504. /468, 2025a.
Liang Chen, Lei Li, Haozhe Zhao, Yifan Song, and Vinci. R1-V: Reinforcing super generalization ability in vision-language models with less than $3. https: /g ctwpg//o..ooooooo/eeooooooooooot www.ooo/eeeeeeeeeeeoooooooeeeeee Detp//Deeee--ee/////ee R1-V. 2025b. Accessed: 2025-02-02.
Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.0522 2024.
Tianzhe Chu, Yuexiang Zhai, Jihan Yang, Shengbang Tong, Saining Xie, Dale Schuurmans, Quoc Le, Sergey Levine, and Yi Ma. SFT memorizes, RL generalizes: A comparative study of foundation model post-trane training. arXiv preprint arXiv:2501.17161, 2025.
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Hcewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reichhrr Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14/60 14168, 2021.
DeepSeck, Inc. DeepSeek-RI Release. https: / //////aaa apaippi--ii apc--ocs..... wwwpsee..com/eeeeeeeeeee news250120, January 2025. Accessed: Jun. 21, 2025.
Haodong Duan, Junming Yang. Yuxuan Qiao, Xinyu Fang, Lin Chen, Yuan Liu, Xiaoyi Dong, Yuhang Zang, Pan Zhang, Jiaqi Wang, et al. VLMEvalKit: An open-source toolkit for evaluating large multi-modality models. In ACM MM, 2024.
Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye, Wanjun Zhong, Yufei Wang, Lanqing Hong, Jianhua Han, Hang Xu, Zhenguo Li, et al. G-LLaVA: Solving geometric problem with multi-modal large language model. In ICLR, 2025.
Akash Ghosh, Arkadeep Acharya, Sriparna Saha, Vinija Jain, and Aman Chadha. Exploring the frontier of dision-language models: A survey of current methodologies and future directions. arXiv preprint arXiv:2404.072 2024.
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-R11 Incentivizing reasoning capability LLMs via reinforcement learning. arXiv preprint arXiv:2501.12943 2025.
Vik Korrapati. Moondream. httpps:/mmooddee ai 2024. Accessed: 2025-03-27.
11

---

Published as a conference paper at ICLR 2026
Yuxiang Lai, Jike Zhong, Ming Li, Shitian Zhao, and Xiaofeng Yang. Med-RI: Reinforce- ment learning for generalizable medical reasoning in vision-language models. arXiv preprint arXiv:2503.13999 2025.
Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. ALaVA-OneVision: Easy visual task transfer. arXiv preprint arXiv:2408.0m 03326, 2024a.
Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, and Jianfeng Gao. LLaVA-Med: Training a large language-and-vision assistant for biomedicine in one day. Advances in Neural Information Processing Systems, 36: 25541-28564, 2023.
Zhuowan Li, Bhavan Jasani, Peng Tang, and Shabnam Ghadar. Synthesize step-by-step: Tools templates and LLMs data generators for reasoning-based chart VQA. In CVPR, 2024b.
Bo Liu, Li-Ming Zhan, Li Xu, Lin Ma, Yan Yang, and Xiao-Ming Wu. SLAKE: A semantically- labeled knowledge-enhaneed dataset for medical visual question answering. In ISBI, 2021.
Fangyu Liu, Julian Eisenschlos, Francesco Piceinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier. and Yasemin Altun. DePlot: One-shot visual language reasoning by plot-to-table translation. In Findings of the ACL, 2023.
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee, Improved baselines with visual instruction tuning. In CVPR, 2024a.
Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. LLaVA-NeXT: Improved reasoning, OCR, and world knowledge, January 2024b. URL https: //1lava gi wwt.....ttt.....o. httblo//b////o//
01 00-0001--00000000000000001000011 11 lavaavvai -nxtt.
Jiazhen Liu and Long Chen. Segmentation as a plug-and-play capability for frozen multimodal Ilms. arXiv preprint ar arXiv:2510.16785, 2025.
Jiazhen Liu, Mingkuan Feng, and Long Chen. Better, stronger, faster: Tackling the trilemma in milm- based segmentation with simultancous textual mask prediction. arXiv preprint arXiv:2512.00395,
2025a.
Jiazhen Liu, Yuhan Fu, Ruobing Xie, Runquan Xie, Xingwu Sun, Fengzong Lian, Zhanhui Kang, and Xirong Li. PhD: A chatspt-prompted visual hallucination evaluation dataset. In CVPR, 2025b.
Ziyu Liu, Zeyi Sun, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Haodong Duan, Dahua Lin, and Jiaqi Wang. Visual-RFT: Visual reinforcement fine-tuning, arXiv preprint arXiv:2503.0/785 2025c.
Andrés Marafioti, Orr Zohar, Miquel Farré, Merve Noyan, Elie Bakouch, Pedro Cuenca, Cyril Zakka, Loubna Ben Allal, Anton Lozhkov, Nouamane Tazi, et al. SmolVLM: Redefining small and efficient multimodal models. arXiv preprint arXiv:2504.055: 000299, 2025.
Ahmed Masry, Do Xuan Long. Jia Qing Tan, Shafiq Joty, and Enamul Hoque. ChartQA: A benchmark for question answering about charts with visual and logical reasoning. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (eds.), Findings of the ACI. May 2022.
OpenAl. Introducing OpenAl ol. https: //tp:://opence hwtp/i.ppp/aaa.///////////...... com/ o1/, December 2024. Accessed: Jun. 21, 2025.
Yingzhe Peng. Gongrui Zhang, Miaosen Zhang. Zhiyuan You, Jie Liu, Qipeng Zhu, Kai Yang, Xingzhong Xu, Xin Geng, and Xu Yang. LMM-RI: Empowering 3B LMMs with strong reasoning abilities through two-stage rule-based rl. arXiv preprint arXiv:2503.073. 075366, 2025.
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. DeepSeekMath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint 240.:2400.0300000 2024.
Haozhan Shen, Peng Liu, Jingcheng Li, Chunxin Fang, Yibo Ma, Jiajia Liao, Qiaoli Shen, Zilun Zhang, Kangjia Zhao, Qianqian Zhang, Ruochen Xu, and Tiancheng Zhao. VLM-RI: A stable and generalizable R1 -syyl large vision-language model. arXiv preprint arXiv:2504.076155 2025.
12

---

Published as a conference paper at ICLR 2026
Qwen Team. Qwen2.5: A party of foundation models, September 2024. URL qwen2.
Peter Tong, Ellis Brown, Penghao Wu, Sanghyun Woo, Adithya Jairam Vedagiri IYER, Sai Charitha Akula, Shusheng Yang, Jihan Yang, Manoj Middepogu. Ziteng Wang, al. Cambrian-1: A fully open, vision-centric exploration of multimodal LLMs. Advances in Neural Information Processing Systems, 1187310-87355 2024.
Renqiu Xia, Bo Zhang. Hancheng Ye, Xiangchao Yan, Qi Liu, Hongbin Zhou, Zijun Chen, Peng Ye, Min Dou, Botian Shi, et al. ChartX & ChartVLM: A versatile benchmark and foundation model for complicated chart reasoning. arXiv preprint arXiv:2402. 12185, 2024.
Renqiu Xia, Mingsheng Li, Hancheng Ye, Wenjie Wu, Hongbin Zhou, Jiakang Yuan, Tianshuo Peng, Xinyu Cai, Xiangchao Yan, Bin Wang, et al. GeoX: Geometric problem solving through unified formalized vision-language pre-training. In ICLR, 2025.
Guowei Xu, Peng Jin, Hao Li, Yibing Song, Lichao Sun, and Li Yuan. LLaVA-CoT: Let vision language models reason step-by-step. arXiv preprint arXiv:2411. 2024.
Jianhao Yan, Yafu Li, Zican Hu, Zhi Wang, Ganqu Cui, Xiaoye Qu, Yu Cheng, and Yue Zhang. Learning to reason under off- off-policy guidance. arXiv preprint arXiv:2504.14945. 2025.
Lele Yang, Muxi Diao, Kongming Liang, and Zhanyu Ma. GRPO for LLaVA. https://githu: Com/PRISE CV CRT.://RPOOFFOO for- ava, 2025a.
Yi Yang. Xiaoxuan He, Hongkun Pan, Xiyan Jiang, Yan Deng, Xingtao Yang, Haoyu Lu, Dacheng RI-One Advancing generalized multimodal reasoning through cross-modal modal formalization. arXiv preprint 100.1 2025b.
Yin, Fengyun Rao, Minfeng Zhu,
Yuexiang Zhai, Shengbang Tong, Xiao Li, Mu Cai, Qing Qu, Yong Jae Lee, and Yi Ma. Investigating the catastrophic forgetting multimodal large language model fine-tuning. In CPAL, 2023.
Jingyi Zhang, Jiaxing Huang, Huanjin Yao, Shunyu Liu, Xikun Zhang, Shijian Lu, and Dacheng Tao. R1-VL: Learning reason with multimodal large language models via step-wise group relative policy optimization. arXiv preprint arXiv:2503. 12937, 2025a
Kai Zhang, Rong Zhou, Eashan Adhikarla, Zhiling Yan, Yixin Liu, Jun Yu, Zhengliang Liu, Xun Chen, Brian D Davison, Hui Ren, et generalist vision- laaggage foundation model for diverse biomedical tasks. Nature Medicine, pp. 1 1-13, 2024a.
Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-We Wei Chang. Peng Gao, et al. Matheerse: Does your multi-moda I1m truly see the diagrams in visual math problems? 2024b.
Wenhao Zhang, Yuexiang Xie, Yuchang Sun, Yanxi Chen, Guoyin Wang, Yaliang Li, Bolin Ding, and Jingren Zhou. On-policy RL meets off off-po experts: Harmonizing supervised fine-tuning and reinforcement learning via dynamic weighting, arXiv preprint arXiv:2508. 1408.. 2025b.
Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. Multimodal chain-of-thought reasoning in language models. arXiv preprini arXiv:2302.00993, 2023.
Baichuan Zhou, Ying Hu, Xi Weng, Junlong Jia, Jie Luo, Xien Liu, Ji Wu, and Lei Huang. TinyLLaVA: A framework of small-scale large multimodal models. arXiv preprint 2402 2024.
Zhuofan Zong, Bingqi Ma. Dazhong Shen, Guanglu Song, Hao Shao, Dongzhi Jiang, Hongsheng Li, and Yu Liu. MoVA: Adapting mixture of vision experts to multimodal context. In NeurlPS, 2024.
13

---

model Deplot. Prompt SI is employed to extract visual elements from Published as a conference paper at ICLR 2026
Empowering Small VLMs to Think with Dynamic Memorization and Exploration
Supplementary Material
In the supplementary materials, report:
LLM instructions used for constructing vision supervision ($S1);
Detailed experimental setup and additional experimental results ($S2);
Showcases of SVLMs trained via DyME performing on medical VQA, chart understanding, and geometry problem solving ($S3);
$1
LLM INSTRUCTIONS FOR VISION SUPERVISION
The instructions for constructing Lap the visual refiner, and the visual checker are listed as follows.
S1.1 INSTRUCTIONS FOR EXTRACTING VISUAL ELEMENTS
le is primarily derived from sources: ground truth captions, and the outputs from specialized tools such as the captions.
You are helpful assistant that analyzes images and provides visual facts.
Your response MUST be single, valid JSON object. The JSON object should contain:
1. "descriptien"; detailed and accurate description of the image.
2. "objects"; list of key objects, including their nane, attributes, and approximate position in the image.
Example format:
"description"; "A person riding bicycle on a city street.... detailed description here)",
10
"objects": [
("name": "person", "attributes": ["wearing helmet", "blue shirt"), position"; "center") "naame: "bicycle", "attributes"; ["red", "mountain bike"), "position
": "center"),
("name": "street", "attributes"; ["asphalt", "wet"I, "position"; bottor")
15
16
Analyze the attached image and provide the visual facts in the required JSON format.
For context, the will be asked this question about the image (do answer the question, just use for context): "[question]"
18
Prompt S1: Automated Visual Fact Extraction
S1.2 INSTRUCTIONS FOR VISUAL REFINER
The visual refiner primarily takes thinking templates from a template pool and I, as input to construct the ground truth for SFT.
Given:
<IC>: the data of image
<Q>: a question

---

Published as a conference paper at ICLR 2026
<A>: reference
<T>: a writing template
Goal:
Transform the visual information in <IC> into textualized data description and incorporate it into smooth, natural explanation that reasons why the correct answer to <Q> <A>, using the format and tone defined by <T>.
Exampie:
<IC>: [
12 { ("object"; "bar", "attributes": [""120k", "Q4"1, "label": "Product A"),
13 { ("object"; "bar", "attributes": [""150k", "Q4"], "label": "Product B"],
("object": "bar", "attributes": [""90k", "Q4"], "label": "Product C*),
("title": "Quarterly Revenue")
17
<Q>: which product has the highest revenue in Q4?
<A>: product b
<T>:
[Extion Extract
relevant data points from <IC> that are needed to answer <Q>.
21
[Calculation Analyze and compare the extracted data to determine the correct resuit.
[Conclusien] Based the analysis, state why <>> is the correct answer to <Q>.
23
24
<utt
[Extraction] The image shows chart comparing three items-Product A, Product B, and Product C-in The bar heights indicate their revenues: Product A at about 120k, Product at about 150k, and Product C at about 90k.
26
[Calculation] Comparing these values, Product 3's revenue (150k) is greater than both Product A's (120k) and Product C' (90k). [Conelusion] Since Product has the highest revenue among the three, the correct answer to the question "Whi ch product has the highest revenue in Q4?" is product b.
27
Now, according to the requirements rements and the examples above, convert my input inte the target reasoning text. Please give me the result directly without any explanation or description.
<IC>: $s
32
<Q>: $s
33
<A>: $5
34
<T>: 3s
<Output>:
Prompt S2: Ground-truth construction for Chart Understanding SFT
Prompts for the other domains follow similar design.
$1.3 INSTRUCTIONS FOR VISUAL CHECKER
The visual checker is primarily responsible for scoring the thinking trace of responses generated in the GRPO process. It evaluates these traces with reference to exemplars, based on their fluency and the degree to which the mentioned visual elements align with T.. Prompts for the other domains follow similar design.
Given
<IC>: the data of an image
<Q>: a question
<A>: a reference answer
<R>: reasoning text
2

---

Published as a conference paper at ICLR 2026
Goal:
Assess whether <R> correctly and reasonably uses visible data in <IC> to justify that the correct answer to <Q> is <>>, Rate the quality as low / medium / high according to:
(a) low: Does not use data from <IC> at all, or the language is not fluent/natural
it fails (b) medium: Uses data from <IC> and is written fluently, but the
indicate the answer to <Q> is <A>.
10
reasoning is overly brief or insufficiently clear.
(c) high: Uses data from <IC> and is written fluently; the reasoning progresses step by step with depth, each step is correct and reasonable; the data from <IC> appears exactly where it should; overall, the reasoning text provides very strong support that the answer to <q> is <A>.
Example:
<IC>: [
15
{ ("object"; "bar", "attributes"; [""120k", "Q4"], "label": "Product A"),
16
{ ("object"; "bar", "attributes": [""150k", "Q4"], "label": "Product B"),
("object"; "bar", "attributes"; [* ["~90k", "Q4"], "label": "Product )*
("title": "Quarterly Revenue")
19
30
<Q>: Which product has the highest revenue in Q4?
21
<A>: product b
<R>
23
[Extioni Reads Q4 par heights: A "120k, "150k, -90k. [Calculation] Compares values: B A and > C. [Conclusion] Therefore, Product B is highest, matching the answer product b".
24
25
27
<Output>: medium
28
29
According to the requirements and examples above, score the input into three categories. Please give the result directly without any explanation or description.
31
<IC>: : 8s
<Q> A
<A>: 8
34 <R>: 8s
35
<Output>:
Prompt S3: Scoring generations during GRPO for Chart Understanding
S2
EXPERIMENTAL DETAILS AND EXTRA RESULTS
$2.1 TRAINING AND TEST SETTING
First, we provide the statistical information for the training and testing phases of experiments in the Tab. S1. The training dataset for each domain consists of only a few thousand samples. In addition, Fig. S1 visualizes comparison between the ground-truth responses produced by the refiner and the original ground-truth, showing that the refined versions are noticeably more structured and place greater emphasis on intermediate values.
Table $1: Training and testing setup. DyME empowers thinking capabilities based on small training sets.
Domain
Training set
#Training samples
Source of I.
Testset
Medical VQA
SLAKE-Train
4,919
BionedGPT
SLAKE-Test
Chart Understanding
ChartQA-Train
4,576
DePlot
ChartQA-Test
Geometry Solving
Geo 17K
6,417
Collected
Math

---

Published as a conference paper at ICLR 2026
Medical (SLAKE).
Chart (ChariQA).
Math (Geo170k).
Medicane Believe Government Progress Against
Drug Trafficnnng Declining
The Mitttr counr co tighedero-
D
Q: angle 35%, what angle
2 Choices: A: 55", B: 135", C:
Q: What is the sum of making
145 D: 155".
progress, losing ground, and same 2011?
w/o refiner:
Q: What is the MR weighting in this image?
Since angle 35 degree, angle 180
w/o refiner:
degree us-
w/o refiner:
To answer the question, we first lo- cate the relevant part of the chart for 2011, then extract the values three series and sum them up to obtain 99.
ing a supplementary relationship.
Based on signal variations typical of this image, the most plausible answer is T2.
Choose C.
w/ refiner:
Extraction: Two parallel lines CD and AB with a transversal; angle is 35 degree, and angle 2 is on the same side.
w/ refiner:
Analysis: On T2, fluid is bright; bone/air and fast flow are dark. Extraction: In this axial slice, intraluminal fluid is bright; vessels low signal; ribs/wall dark. Conclusion: Pattern matches T2.
w/ refiner: Extraction: Read 2011 values: Making progress 45, Losing ground 29, Same
Calculation: Same-side interior gles are supplementary, so angle 180 35 = 145 degree.
Calculation: 45 29 25 = 99.
Conclusion: The sum is 99.
Conclusion: Answer: C.
Figure S1: Comparison of ground tttun responses before and after refinement. Compared to the original ground-truth, the refiner injects richer visual elements and enforces a more structured organization, thereby reducing the learning burden for SVLMs.
S2.2
EXTRA RESULTS
We also report additional experimental content, including the discussion on training strategies and data organization formats, well as a comparative analysis with other similar methods that integrate SFT and RI..
Specifically, (1) we first demonstrate the importance of constructing vision supervision, which proves essential for training SVLMs to produce grounded thinking traces. (2) We then examine the impact of structured versus open- ended output formats on thinking performance. (3) Fur- thermore, to validate our earlier observation that SVLMs prone converging local optima, present perfor- mance across different training epochs, showing that SFT training saturates after only one epoch. (4) We provide a detailed comparison with alternative methods that inte- grate SFT and RL. (5) Finally, we extend our evaluation to stronger base models and pure textual domains, and (6) validate the quality of generated thinking traces through human evaluation.
Table S2: ChartQA. Rel-corr denotes the metrie. indicates whether an explicit image-content field is supervised yes; X no).
Two-stage training on
Model
Je
Rel-corr
SmolVLM
/
64.32
SmoIVLM
60.09
LLaVA-OV-S
63.62
LLaVA-OV-S
52.90
(1) Intermediate values matter, As shown in Table S2, we report the effect of applying two-stage training with visual supervision SmolVLM and LLaVA-OV-S, Incorporating visual supervision significantly improves the best performance achieved during training. despite certain instabilities, thereby validating its critical role for SVLMs. This effect is further illustrated in Fig. $1, where visual supervision compels SVLMs to generate intermediate reasoning enriched with visual elements, which make clear contribution to the final answer.
(2) Structured thinking alleviates the learning burden of SVLMs. Table S3 reports the perfor- mance gap between training with structured thinking ground-truth and with unconstrained ground-
4

---

Published as a conference paper at ICLR 2026
1.0
DyME CHORD-y
0.8
LUFTY
proportion poliyy
nWW
0.2
0.0
500 1000
1500
2000
2500
Figure S2: Relative off-policy influence during training. Each curve is normalized its initial value for comparability. DyME measures SFT/(SF)) RT.) (raw in lighter tone, Gaussian-smoothed darker tone), CHORD-j tracks the global weight ju(t), and L. LUFFY adopts policy-shaping proxy (To(()) with f() optimization, albeit with distinct decay dynamics.
. All methods reveal the shift from off- off-policy guidance to on-policy
Table S3: Effect of templated output across models and tasks. v denotes fixed-templan output, whereas denotes free-form generation.
Model
Template
Chart
Medical
SmolVLM
60.10
$9.38
SmoIVLM
59.24
56.13
LLaVA-OV-S
V
52.87
74.52
LLaVA-OV-S
X 50.86
72.64
truth. While open-ended exploration is often beneficial for LVLMs, the limited capacity of SVLMs makes unconstrained exploration less effective, as tends to be aimless and increases the learning burden. Given that SVLMs are designed for task task-specific rather than general-purpose purpose scenarios, em- ploying tailored thinking templates for each task proves more suitable and yields better performance. For instance, SLo.VLM achieves 60.10 vs. 59.24 on ChartQA and 59.38 vs. 5s.56.13 13 on Medical VQA, with LLaVA-OV-S exhibiting similar gains.
(3) Comparison between annealed SFT loss and DyME. shown Fig. S2, we compare the relative SFT (off- policy) influence across training steps for three approaches: DyME, CHORD (Zhang et al., 2025b), and LUFFY (Yan al., 2025). For DyME and CHORD, the curves represent the normalized weight of the SFT loss at each step. while for LUFFY the curve reflects the trajectory of SFT gradi- ent shaping as a function of prediction probability (which generally correlates with training steps). These curves highlight the dynamic nature of DyME. Because of the ex- tremely limited capacity of SVLMs, their learning patterns can shift significantly even between adjacent steps, leading to rapid forgetting of previously acquired modes. Unlike CHORD, which relies on smooth annealing schedule that decays quickly and ill-suited to such small models, DyME assigns weights directly based model outputs.
Table S4: SVLM performance satu- rates after a single training epoch. Score domain chart domain uses Rel-corr, while the medical do- main uses the average of accuracy and recall values.
Model
Domain
Epoch
Score
60.70
LLaVA-OV-S
Chart
60.44
10
60.12
60.22
Chart
63.21
10
62.22
SmolVLM
71.73
Medical
71.80
This produces a highly dynamic and irregular decay, bet- ter accommodating the instability of SVLMs. LUFFY adopts a shaping function fxx = . (7=0.1), which also induces a dynamic decay with probability but remains heuristic and may not align well with the local-optimum tendency of SVLMss. Overall, DyME is explicitly tailored for SVLMs, whereas
10
72.05
5

---

Published as a conference paper at ICLR 2026
Table S5: Detailed learning trajectories demonstrating rigorous tuning. We report the perfor- mance across multiple settings to show their full learning trajectories. Two-stage baselines include variants with and without KL penalties ensure optimal performance captured.
Data Quality
Method
Performance across epochs 31, 10)
Best perf.
DyME (ours, pure)
Report final score directly
61.9
SFT
43.1 + 47.9 - 50.0 - 50.5
50.5
Low
Two-stage
57.6 - 52.7 + 50.8 - 50.7
57.6
Two-stage (w/ KL)
54.2 - 55.4 + 52.6 - 54.2
55.4
DyME (ours, pure)
Report final score directly
64.9
SFT
53.6 - 56.5 + 57.8 + 56.4
57.8
Medium
Two-stage
59.9 > 52.8 + 53.0 + 53.1
59.9
Two-stage (w/ KL)
59.0 - 60.6 + 60.6 60.8
60.8
DyME (ours, pure)
Report final score directly
68.5
SFT
58.2 7 59.1 + 61.0 61.6
61.6
High
Two-stage
51.6 - 54.0 > 54.5 - 54.4
54.5
Two-stage (w/ KL)
61.7 - 60.9 - 62.7 7 61.8
62.7
CHORD and LUFFY may be more appropriate for stronger base models, reflecting complementary strengths.
(4) SVLMs converge rapidly. Table S4 shows that SVLMs converge extremely quickly: performance after only one epoch comparable to, or even exceeds, that after ten epochs (e.g., VA--V-- achieves 60.70 vs. 60.13 12 on the Chart domain). This indicates that the very limited capacity of SVLMs makes them prone to overfitting to local optima. It also substantiates our earlier claim that such rapid convergence leaves only narrow window for balancing SFT and RL, making it difficultt to achieve the trade-off through empirical hyperparameter tuning. Consequently. static fusion methods unsuitable for SVLMs.
To ensure a rigorous comparison, we further report the full learning trajectories of baselines in Table $5. We evaluated the Two-stage baseline (with and without KI. penalty) and SFT across multiple epochs (1, 3, 10) to capture their peak performance. The results confirm that even with optimal stopping, the baselines consistently underperform DyME, which achieves superior results in single training run without the need for epoch selection.
(5) Generality across complex reasoning and pure text. To demonstrate the scalability of DyME, we applied it to two new domains without modifying the core algorithm: Physical Reasoning (A-OKVQA) and Pure Text Reasoning (GSMSK).
Physical Reasoning (A-OKYQA): We addressed the challenge of open-ended visual reasoning by testing A-OKVQA. We used Qwen2.5-VL-7B to automatically generate Visual Facts using the prompt defined in $S1 (e.g.. "man, wearing a light blue and white shirt..."). As shown in Table $6, DyME achieved massive gain of +18.8% (54.2% 73.0%), proving that the method scales effortlessly to tasks requiring world knowledge and commonsense.
Pure Text Reasoning (GSM8K): In pure text domains, the "Visual Fact" extraction step is naturally skipped. On the GSM8K math benchmark, DyME improved Qwen2.5-0.5B from 49.5% 55.3%, demonstrating that the paradigm generalizes even when "vision" is absent.
These results, combined with the ChartQA improvements on the stronger Qwen2.5-VL-7B model, confirm that DyME is not limited by the extraction step. By leveraging off-the-shelf LVLMs to automate visual fact generation, the framework is immediately applicable diverse visual and textual domains.
Limitations Abstract Visuals. We acknowledge that the VS module may face challenges in scenarios where "Visual Facts" " are intrinsically difficult to define or extract, such as memes (relying irony cultural context) highly abstract non-commonsense reasoning. However,

---

Published as a conference paper at ICLR 2026
Table S6: Generality DyME across New Domains. We demonstrate performance gains on Complex Scenes (A-OKVQA), Pure Text (GSM8K), and with stronger base models (Qwen2.5-VL. 7B). Baselines for text use standard GRPO.
Domain
Task
Base Model
Method
Baseline (%)
DyME (%)
World Knowledge
A-OKVQA
LLaVA-OV-S
Tto-stage
54.2
73.0 (+18.8)
Pure Text
GSM8K
Qwen2.5-0.5B
GRPO
49.5
55.3 (+5,8)
New LVLM
ChartQA
Qwen2.5-VL-7B
SFT
87.3
89.6 (+2.3)
our primary objective to empower SVLMs for practical, real-world production tasks (e.g., chart processing, medical diagnostics, geometric solving). In these structured and semi-structured domains where SVLMs are most commonly deployed, Visual Facts are well-defined and DyME proves highly effective.
(6) Human evaluation of CoT quality. Automatic metrics like relaxed accuracy do not fully reflect the quality of the reasoning process. To verify whether DyME generates genuinely better thinking traces, we conducted human evaluation on 100 randomly sampled instances from ChartQA. Annotators judged the validity of the generated CoT based on its logical coherence and grounding. As shown in Table S7, DyME produces traces that are slightly more concise (shorter length) but significantly more valid (validity rate 770%) compared to the Two-stage baseline (~30-40%) This confirms that DyME effectively mitigates the generation of "pseudo thinking traces" that plague standard SFT/Two-stage training.
Table S7: Human evaluation of CoT quality on ChartQA.
Base Model
Method
Avg. CoT Length
Human Eval (Valid %)
Two-stage DyME
76.3 Words
31 %
LLaVA-Ov-S
699.7 Words
68 %
Two-stage
~ 84.5 Words
40%
SmolVLM
DyME
75.4 Words
72%
S3 SHOWCASES
Before presenting the model outputs, we first illustrate the data quality definitions used in our Algorithmic Validation (Section 4.1 of the main paper). Table S8 showcases examples of Low (Undesigned), Medium (Standard), and High (Premium) quality Chain-of-Thougee the same question. This visualizes the significant gap in structure and detail that DyME must bridge when trained on non-premium data. Furthermore, Table $9 illustrates the comprehensive format of our supervision data, encompassing the input image, the associated question, the extracted visual facts, and the ground-truth response.
supervision for
We present dialogue instances of SmoIVLM, LLaVA-OV-S, and Intern VL.2-S, which were trained with DyME in our experiments, tasks in the domains of medical VQA, chart understanding, and geometry. As shown in Fig S3 to Fig S5, all models trained with DyME demonstrate the ability to generate effective thinking traces with accurate intermediate values (in green), which play crucial role reaching the final correct answer.
The thinking processes of these models are relatively fixed and template- based. This is result of our vision supervision module taking effect, as SVLMs should not be overly broad and general; otherwise, their exploration can easily diverge instead of converging.
7

---

Published as a conference paper at ICLR 2026
Table S8: Showcase of chain-of-thought (CoT) supervision with different quality for ChartQA.
Type
Content
Question
does unfavorable view peak?
Very unfavorable views Pakistan
increasing India
Verg argeoraalt strsrg Poitsten
100%
55
54
2015
2014
2015
2018
2017
Source 205 Sarvey
PGw CENTEN
High-quality CoT
<SUMMARY> inspect the line graph showing unfavorable views of Pakistan India over time, and identify the year where the percentage peaks by locating the maximum point on the curve, </ SUMMARY>>
<CAPTION> Line graph titled "Very unfavorable views of Pakistan increasing in India". The y-axis shows the share of respondents with a very unfavorable view (0-100%) 100%). The x-axis shows years 2013-2017. Marked values: 54% (2013), 49% (2014), 51% (2015), 55% (2016), and 64%; (2017). The series exhibits overall upward trend and peuks 2017, </CAPTION> <REASON NGG> The peak corresponds to the highest percentage on the y-axis. Among the annotated yeurs, 2017 has the largest value (64%), higher than 2013-2016. Therefore, the peak occurs 2017. </RRASONT NG2
<CONCT.DS T TON> 2017 </CONCLUSTOO
Medium quality
Goal: Determine the when the unfavorable view reaches its maximum.
CoT
Observation: The values for cach year are: 2013: 0, 2014: 0, 2015: 0, 2016: 55, and 2017: 64. Reasoning: Comparing these values, the largest value 64, which appears in 2017.
Conclusion: The unfavorable view reaches peak 2017.
Low-quality
I'm trying figure out the when the unfavorable view reaches its highest point. The values stay low until 2016, where they rise 55, and then climb further to 64 in 2017. This makes 2017 the year when unfavorable view reaches its maximum.
Coll
Table S9: Showcase of a complete CoT training example.
Type
Content
Question
What is the by bags awaiting?
Visual Fact
{ "'descripton". "A man stands in middle a residential street, facing away from the camera. He holds red bag and pulls wheeled black suitcuse, with another black suitcuse placed nearhy. Houses, parked cars, and trees line the hackground, suggesting he is waiting to cross or be picked up.". "objects"" "name": "man". "attributes": ('light blue and white shirt". "blue jeans". "carrying
bag". "pulling wheeled suitcase"] "position": "center" "name": "red bag". "atuributes": ['beld by the man"]. "position": "left of the man" "name": "black suitcuse", , "attributes": ; ["wheeled". "being pulled"]. J. "position": "neur the man's 's feet" suitcase", "attributes": ["lying on ground""] "position": "hear "name": "street", "attributes": l"asphalt", "urban residential"). "position": "foreground" "name": "houses". "attributes": ("residential homes"), ). "position": "hackgrund left". "name": "parked cars", "attributes": ['red SUV", "other vehicles"]. "position": "background center and left". "name": "trees". "attribtes": I'green foliuge"]. J. "position": "buckground right" 1}
"poosiion"""
"name":
GT Response
A train would not appear for transportation rather than a delivery. The nearby skateboarder is not interacting with him, leaving a cab as the most plausible option.
residential street, and the man's luggage suggests he is waiting
8

---

Published as a conference paper at ICLR 2026
What the mr weighting in this image?
(Ipput Image)
The MR weighting in this image is FLAIR.
Analysis: Based on the knowledge that 12- weighted MRI images highlight tissues with high water content as dark and those with low water content as bright, we can infer the MR weighting used in the image. Extraction:
The image demonstrates the liver, spleen, and lung. The liver appears normal and the largest organ visible. The spleen is also visible, and the lung is not present the image.
Conclusion: The MR weighting this image T2.
Answer: T2
(a) Showcase of Medical VQA
What the difference between the values of 2017 and 2016?
8
(Inpul Image)
The difference between the values of 2017 and 2016 is 19000.
Extraction: data value 36700 for 2017 and 29000 for 2016
109000
Calculation: 36700 29000 7700 Conclusion: The difference between the values of 2017 2016 7700.
CO
Answer: 7700
2017
2013
2014
(b) Showcase of Chart Understanding
Given AB // CD, angle 50.0, then what the degree angle
0
Angle 2 is equal to angle 3, angle also 50 degrees.
F
Extraction: AB is parallel to CD, angle 1 = 50%. Calculation: angle 2 180 angle 1 = 180 50 130". Conclusion: The degree of angle is 130".
2
E
Answer: 1300
(c) Showcase of Geometry Solving
Figure S3: Showcases SmolVLM. The SVLM originally produces hallucinated answers (red), while the DyME -taiind model generates structured thinking traces (green) that incorporate grounded values, effectively improving the performance.

---

Published as a conference paper at ICLR 2026
8
diseuses ure included in the picture?
Cmput Imagee)
The disease included in the picture cardiomegaly Atelectasis.
Analysis:Bssdd on the knowledge that nodules in the lungs can indicate various respiratory discases, including benign conditions, infections, or malignancies, it is crucial to ideatify assess these abnormalities medical images. Extraction: image demonstrates chest X-ray with visible nodules both the right and left lungs. These nodules are indicative of abnommal growths or lesions within the lung tissue.
Conclusion: The presence nodules both lungs indicates disease included the picture nodule.
Answer: Nodule
(a) Showcase of Medical VQA
What's the difference in value of biggest and smallest segment?
8
(Input Image)
The difference is 14%.
Extraction: data is Support 80%, Oppose = 17,, DK 33.
Support 80%
Calculation: 80%-3% 77%
Conclusion: The sum of the values for girls who think it takes too much time and those who think it takes about the right time is 53.
Oppose 17%
Answer: 77%
3%
(b) Showcase of Chart Understanding
As shown in the figure, BC and BA intersect at point B, BD bisects angle ABC, CD parallel AB, if angle BCD 70.0, then the degree angle CDB
g
(npuu Image)
D
The angle CBD 75.
Extraction: CD AB; BD bisects
LABC.
Calculation: LABC -80 110% ZCDB = ZADB=70%22 = 35".
Conclusion: The degree of angle CDB
C
B
Answer: 55
Showcase Geometry Solving
Figure S4: Showcases of Intern VL2-S. The SVLM originally produces hallucinated answers (red). while the DyMEntrained model generates structured thinking traces (green) that incorporate grounded values, effectively improving the performance.
10

---

Published as a conference paper at ICLR 2026
What of the values girls who takes too much time and those think it takes about the right time?
(Input Image)
sum the values 73.
41% of foenage boys say they spend too much time playing video
of USS axp rhyy spend playing video pamer
Extraction: Value for girls Too little
About time
Toa
time: 11 About the right time: 42 Calculation: 11 + 42 53
Conclusion: The sum of the values for girls who think takes too much time those who think takes about the right time is 53. Answer: 53
13
30
notatove.
Suaee Buren un aoeate Morih Hopel 11,, 2011
VWw WASLMAN canrae
(a) Showcase of Chart Understanding
g
What diseases are included the picture?
Anput Image)
The disease included in the picture is Mass.
Analysis: Based the knowledge that cardiomegaly is characterized by an enlarged heart, which can be identified through chest X-rays.
Extraction: The image demonstrates an enlarged heart, which is clear sign of cardiomegaly.
Conclusion: The disease included in the picture cardiomegaly.
Answer: Cardiomegaly
(b) Showcase of Medical VQA
Given that <A - 80", <B - 60", and DE is parallel to BC, what is the measure of CCED?
8
(Input Image)
The angle CED is 40%.
Extraction: angle A = 80%, angle B 60%, DE parallel BC. Calculation: angle CED angle A angle B = 800 + 60% 140%. Conclusion: The measure of the angle CED is 140%.
E
D
B
Answer: 140%
(c) Showcase of Geometry Solving
Figure S5: Showcases LLaVA-OV-s. The SVLM originally produces hallucinated answers (red), while the DyME-traines model generates structured thinking traces (green) that incorporate grounded values, effectively improving the performance.
11


Reviews:
[{"review_id": "10e0Yg56Pj", "reviewer": "Reviewer_y5pj", "summary": "The paper proposes a new dynamic training paradigm that can switch between the supervised finetuning and the group relative policy optimization for a vision-language model. Specifically, the paper first  asks the small vision language model to generate the responses. When at least one is correct, the model will choose GRPO, otherwise it will use the SFT-based model. For GRPO reward, apart from the binary correctness, the paper also introduces a visual checker to evaluate the image grounding. The proposed method is evaluated on SLAKE, ChartQA, and MathVerse. The paper uses several VLM baselines to show its performance. The experiment results show that the small model can achieve comparable or even better performance on multiple tasks, when comparing with LVLMs. The experiment also includes ablation study,", "questions": "What is the performance gain of SVLM on pure text tasks?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed new training procedures seem to be simple yet effective. DyME can be applied to SVLM and can achieve significant performance gains across different domains. The method can also reduce the advantage collapse and constrained exploration.\n2. The experiment is comprehensive. The paper compares the proposed training strategy with two-stage, GRPO, and SFT on three different models with 0.5-1B parameters. The ablation study shows the importance of the proposed training strategy and visual rewards.\n3. The paper provides additional training details and examples in the appendix. The illustrative figures help readers to understand the paper better.", "weaknesses": "1. Some baselines are pretty old. The paper needs to include some newer LVLMs such as QWen-2.5VL, etc. The experiment section is also purely a quantitative evaluation. Some qualitative evaluation or human evaluation can help readers to understand the quality of the chain better. For example, the length of COT after using DyME compared to the two-stage. Adding additional experiments, such as pure textual or pure vision tasks, can help readers understand the performance gain better. The current evaluation focus on the VQA tasks, which are a bit limited.\n2. Some parts of the paper are not clearly written. For example, why use Geo170k for training but evaluate on MathVerse? The paper also mentioned the chartqa used relaxed correctness. What is the approximation used for the evaluation?\n3. The paper fails to show any code and model, making it hard for readers to reproduce results. The paper did not include reproducibility statement. The paper fails to include a use of LLMs section.", "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": "vSvJYUOvx6", "reviewer": "Reviewer_6oCC", "summary": "This paper addresses the challenge of instilling reasoning (\"thinking\") capabilities in SVLMs. SFT overwhelms the models' limited capacity, leading to \"pseudo thinking traces,\" while RLHR fails due to poor instruction adherence, causing \"advantage collapse.\"\n\nThus, the authors propose DyME (Dynamic Memorize-Explore), a novel training paradigm. DyME dynamically switches between an exploration (RLVR) mode and a memorization (SFT) mode at each optimization step. The switch is governed by a simple rule: if the model fails to produce any correct response in a batch, it falls back to SFT mode. If at least one response is correct, it uses RLVR mode.\n\nFurthermore, DyME introduces a \"vision supervision\" mechanism, composed of a \"Visual Checker\" and \"Visual Refiner\", which use an external LLM to (1) provide a more nuanced \"thinking reward\" for the RLVR mode and (2) dynamically refine the SFT ground-truth targets to be more visually grounded and consistent with successful exploration traces. \n\nExperiments across three domains show that DyME substantially improves the performance of SVLMs, whereas the SFT and RLVR baselines are shown to degrade performance.", "questions": "Please see the comments above regarding the weaknesses. I have written how each concern can be discussed and addressed in the rebuttal/revision.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper tackles a practical and important problem of enabling complex reasoning on small, efficient models.\n\n2. The paper is generally well-written, and the problem is clearly motivated. Figure 1, in particular, provides a good illustration of why existing paradigms might fail on SVLMs and how DyME tries to solve this.\n\n3. Strong empirical results. The authors show in Tab. 1 that standard SFT, RLVR, and a two-stage approach degrade the performance of SVLMs on these tasks, while DyME consistently provides significant gains.\n\n4. Effective ablation study. The ablation in Table 2 does a good job of validating the key components of DyME. It shows that removing either the memorization or exploration mode is catastrophic, supporting the need for a hybrid approach. It also demonstrates the effectiveness of the visual supervision modules.", "weaknesses": "1. The \"vision supervision\" modules (Visual Checker and Refiner) involves additional dependencies. The modules are critical to the method's performance (as shown in Tab. 2), but they are implemented via prompting an external Qwen2.5-14B. This involves additional knowledges and causes unfair comparisons.\n\n2. Question regarding novelty of this work. Compared with existing hybrid SFT+RL methods, the authors claim the main novelty is the dynamic switching criterion. However, the criterion used (fall back to SFT if all $K$ samples are incorrect) is a relatively simple and heuristic. The paper note that prior hybrid methods are \"static,\" but this might be an oversimplification. A deeper comparison to other dynamic weighting schemes (e.g., PPO-SFT hybrids) may be helpful to fully position the novelty of this specific heuristic.\n\n3. The fact that all baselines (SFT, GRPO, Two-stage) fail so catastrophically (e.g., dropping average performance from 49.9 to 44.1 or 44.0 for SmolVLM) is surprising. While I understand the  limited capacity of SVLMs can lead to underperforming results, I wonder if this could be caused by under-optimized tuning. For example, SFT's failure is attributed to overwhelming the modell, but could a simpler baseline (e.g., SFT on far less data or shorter CoT) have been more effective? I would appreciate any deeper discussion into this concern.\n\n4. Clarity of \"Vision Supervision\" and lack of details. While the ablation shows the visual checker/refiner are important, their description in the main paper is high-level. More detail on the prompts, the failure modes of this LLM-based pipeline, and its reliability would be \nnecessary to make the method truly reproducible.\n\n5. The visual refiner/checker requires additional inference of a LVLM during the training of a LVLM. This seems time consuming and may harm the effectiveness of the proposed training pipeline. Could you quantify the computational cost of this pipeline? How critical is the choice of Qwen2.5-14B? What happens if a weaker/stronger model is used for the checker and refiner? Does the method still work?\n\n6. More investigation into the switching criterion. The binary switch (all fail vs. $\\ge 1$ success) is simple and effective. Did you experiment with other criteria (e.g., switching if the average reward of the batch is below a threshold, or using a \"budget\" for SFT steps) that might be more robust?\n\n7. It seems the authors omit the visual checker/refiner in the abstract. To fully reflect the contribution of this paper, they may consider adding this part into the text.", "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": "sVfH0s7hrW", "reviewer": "Reviewer_mKuR", "summary": "This paper tackles a critical and practical challenge: enabling reasoning (“thinking”) in Small-scale Vision-Language Models (SVLMs). The authors argue that existing training paradigms for Large VLMs—Supervised Fine-Tuning (SFT) on CoT data and Reinforcement Learning with Verifiable Reward (RLVR)—are ill-suited for SVLMs. SFT overwhelms small models, harming visual grounding, while RLVR often collapses due to poor instruction-following and unstable training.\nTo address this, the authors propose DyME (Dynamic Memorize–Explore), a novel training paradigm that dynamically switches between SFT and RLVR at each optimization step:\n- Dynamic Switching: If at least one of the multiple generated responses is correct, DyME enters Exploration mode (RLVR) to encourage diverse reasoning. If all fails, it reverts to Memorization mode (SFT) to learn from ground-truth traces.\n- Complementary Supervision: A visual checker rewards visually grounded reasoning, while a visual refiner enhances SFT targets using successful exploration traces.\nExperiments on three domains—medical VQA, chart understanding, and geometry reasoning—show that DyME yields substantial and consistent gains across multiple SVLMs, often matching or surpassing larger models.", "questions": "1. External LLM Sensitivity: How would performance change if smaller or open-source models were used for the visual checker/refiner?\n2. Training Overhead: What is the computational and time cost of DyME relative to standard SFT and RLVR?\n3. Effect of K: How does the number of generated responses per step (K) affect stability, performance, and cost?\n4. Visual Fact Extraction: For novel tasks not covered in the paper (e.g., complex scene understanding  or physical reasoning), what is the anticipated process for extracting the Visual Facts? Does this step require significant manual design, or can it be automated?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Timely and Meaningful Problem: Focuses on reasoning for small, efficient VLMs—highly relevant for real-world deployment on edge devices.\n2. Elegant and Effective Approach: The dynamic “memorize–explore” mechanism intuitively balances stability and exploration, well-suited to SVLM limitations.\n3. Strong Experimental Evidence:\n  - Baselines clearly show SFT/RLVR failures, motivating DyME.\n  - Consistent, significant improvements across all domains.\n  - Ablation studies confirm each component’s necessity and synergy.\n4. Excellent Clarity: The paper is clearly written, with strong visuals (notably Fig. 1) and a logical, accessible presentation.", "weaknesses": "1. Reliance on External LLM: The visual checker and refiner rely on a large external model (Qwen2.5-14B), introducing extra complexity, cost, and dependency. This makes performance partly contingent on the external LLM’s capability, slightly undermining the goal of a self-contained small-model framework.\n2. Rigid Switching Heuristic: The binary rule (“if one correct → RLVR, else → SFT”) may cause abrupt shifts; a softer, reward-based switch could yield smoother training.\n3. Limited Task Generality: The method requires domain-specific Visual Fact extraction, which may hinder scalability to new, open-ended 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": {}}]

Author rebuttal:

Decision: Accept (Poster)