VideoZoomer: Reinforcement-Learned Temporal Focusing for Long Video Reasoning
Authors: Yang Ding
Xin Lai
Yizhen Zhang
Wei Li
Ruihang Chu
Yujiu Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Multimodal LLM
Reinforcement Learning
Video Understanding
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in vision-language tasks yet remain limited in long video understanding due to the limited context window. Consequently, prevailing approaches tend to rely on uniform frame sampling or static pre-selection, which might overlook critical evidence and unable to correct its initial selection error during its reasoning process. To overcome these limitations, we propose VideoZoomer, a novel agentic framework that enables MLLMs to dynamically control their visual focus during reasoning. Starting from a coarse low-frame-rate overview, VideoZoomer invokes a temporal zoom tool to obtain high-frame-rate clips at autonomously chosen moments, thereby progressively gathering fine-grained evidence in a multi-turn interactive manner. Accordingly, we adopt a two-stage training strategy: a cold-start supervised fine-tuning phase on a curated dataset of distilled exemplar and reflection trajectories, followed by reinforcement learning to further refine the agentic policy. Extensive experiments demonstrate that our 7B model demonstrates diverse and complex reasoning patterns, yielding strong results across a broad set of long video understanding and reasoning benchmarks. These emergent capabilities allow it to consistently surpass existing open-source models and even rival proprietary systems on challenging tasks, while achieving superior efficiency under reduced frame budgets.

Published as a conference paper at ICLR 2026
VIDEOZOOMER: REINFORGEMENT-LERRNED TEMPO- RAL FOCUSING FOR LONG VIDEO REASONING
Yang Ding Xin Lai Yizhen Zhang
Wei Li The Chinese University of Hong Kong $ByteDance
Ruihang Chu
Yujiu Yang
Tsinghua University
ABSTRACT
Multimodal Large Language Models (MLLMs) have achieved remarkable progress in vision-language tasks yet remain limited in long video understand- ing due to the limited context window. Consequently, prevailing approaches tend to rely on uniform frame sampling or static pre-selection, which might over- look critical evidence and unable to correct its initial selection error during its reasoning process. To overcome these limitations, novel agentic framework that enables MLLMs to dynamically control their vi- sual focus during reasoning. Starting from coarse low-frame-re overview, VideoZoomer invokes a temporal zoom tool to obtain high-frame-rate clips at autonomously chosen moments, thereby progressively gathering fine-grained ev- idence in a multi-turn interactive manner. Accordingly, we adopt a two-stage training strategy: cold-start supervised fine-tuning phase on curated dataset of distilled exemplar and reflection trajectories, followed by reinforcement learning further refine the agentic policy. Extensive experiments demonstrate that our 7B model delivers diverse and complex reasoning patterns, yielding strong perfor- mance across a broad set of long video understanding and reasoning benchmarks. These emergent capabilities allow it to consistently surpass existing open-source models and even rival proprictary systems on challenging tasks, while achiev- ing superior efficiency under reduced frame budgets. The code are avaliable at https:/ htttthb.thuggggg com/ wtt.
propose VideoZoomer, a
INTRODUCTION
With clear task in mind, humans can efficiently navigate long and complex visual streams by dynamically allocating attention, selectively identifying salient events such as decisive actions in sports match key explanations in lengthy lecture, while filtering out redundancy. This goal- directed ability underlies effective and efficient visual reasoning. as widely documented cognitive science (Kietzmann et al., 2018), remains difficult achieve in artificial intelligence. Although MLLMs perform strongly on image (Bai et al., 2025; Chen et al., 2024) and short-video tasks (Zhang al., 2023), they remain constrained in long-video comprehension tasks mainly due their limited context window (OpenAI, 2024; Reid et al., 2024).
The most common strategy to address this challenge is uniform frame sampling (Zhang et al., 2024b;c), which selects frames at fixed intervals (e.g., two frames per second) to construct a sub- set that fits within context window. Nevertheless, this strategy is inherently limited, it assumes all moments are equally important and further risks overlooking short but critical events while al- locating context budget redundant clip segments. To address these limitations, prior work has investigated adaptive frame selection (Yu et al., 2024; Hu et al., 2025a; Tang et al., 2025), where a lightweight selector module, conditioned on the text query, identifies salient frames before reason- ing. While improving over uniform sampling, these methods are still inefficient because they are designed to select a fixed number of frames regradless of the problem's complexity. Second, the design remains static and non-interactive. If the initial choice is suboptimal or misses key details,
"Equal contribution.
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Published as a conference paper at ICLR 2026
Video
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Figure I: Left: Conceptual comparison of three long video reasoning frameworks: (a) uniform sampling, (b) with frame selector, and (c) VideoZoomer (Ours). Right: Performance comparison of VideoZoomer against various baseline models under different frame budgets on LSDBench.
the model has mechanism correct the revisit the video. This fundamentally limits its performance on complex tasks that require iterative evidence gathering.
To overcome the rigidity and inefficiency of prior methods, propose VideoZoomer, novel frame- work that empowers an MLLM to autonomously and dynamically control its visual focus during its reasoning process. As illustrated in Figure (Left), instead of being passive recipient of pre- selected frames, our model acts as an active agent.
This yields two primary advantages: (i) It is highly efficient: the agent begins with a coarse overview of low frame rates, only consuming a significant context budget when it decides to invoke a <video. zoom>> tool. This on-demand approach ensures that the model's 's context window used dynamically and judiciously. (ii) It is more performant: by learning a policy to request high-frame rate clips of specific moments, the agent can correct initial oversights and gather detailed evidence precisely when and where it is needed. This dynamic, iterative wridence-gathering process avoids the critical information loss inherent in static methods and raises the upper bound on reasoning per- formance. Figure (Right) demonstrates the practical benefit of this design LSDBench (Qu et al., 2025), a benchmark specifically designed to test a model's s ability to find short, critical events in long videos. Our method achieves better performance-effiiinng trade-off, achieving superior accuracy compared to open source baselines while operating on a flexible and smaller frame budget.
Notably, training such an agent faces several challenges, naive reinforcement learning approach would suffer from an inefficiently large action space and exhibit limited reasoning patterns. To ad- dress this, we introduce two-stage training strategy. First, cold-start Supervised Fine-Tuning (SFT) phase teaches the model the basics of tool using. Using a tailored dataset of exemplar trajec- tories, we train the model to understand the task format, master the syntax of tool calls, and develop a bascline reasoning capability. Crucially, to prevent the model from merely imitating a single, monotonous reasoning pattern, we enrich this dataset with reflection data, which exposes our model to more diverse and sophisticated moobleg- strategies. Second, with these foundational skills established, a Reinforcement Learning (RL) phase optimizes the model's tool interaction policy and reasoning capability, transforming it from a simple imitator into adaptive agent that can generalize its strategy to unseen videos and questions.
We summarize contributions follows:
We propose VideoZoomer. a novel framework that reframes long video understanding as a se- quential tool interaction task, enabling an MLLM to dynamically control its visual focus via multi-turn tool interaction.
We introduce robust, two-stage training strategy: cold-start phase using tailored dataset of exemplar and reflection trajectories, followed by a reinforcement learning phase to optimize an efficient and effective agentic policy.
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Published as a conference paper at ICLR 2026
We demonstrate through extensive experiments that model significantly outperforms existing open-son source models on a wide range of long video understanding and reasoning benchmarks, in some cases surpassing leading proprietary models with greater efficiency.
2 RELATED WORKS
Multimodal Reasoning Models. The remarkable success in LLMs (Guo et al., 2025; Team et al., 2025; Tan et al., 2025; Jaech al., 2024; Yang et al., 2025) has demonstrated that reinforcement learning (RL) is a powerful paradigm for enhancing the complex reasoning capabilities, Many works since then have tried to transfer this into multimodal domain. Methods such as MM-Eureka (Meng et al., 2025) and VI.-Rethinker (Wang et al., 2025) have successfully adapted RI. techniques to improve the vision-language reasoning abilities of MILLS (Zhang et al., 2025e;b; Luo al., 2025; Zhang et al., 2025d). More recently, Video-R1 (Feng et al., 2025) further validated the efficacy of this approach specifically within the video domain. Recently some works has tried further extend MLLMs with external tools like image cropping (Zheng al., 2025; Su et al., 2025; Ma et al., 2026), web search (Wu et al., 2025), segmentation (Liu et al., 2025b). However, most of these methods focus on image tasks and only interact with the environment for single turn, combining RL-driven reasoning and multi-turn tool use strategy for long video understanding is still underexplored.
Long Video Comprehension. Many works have tried to extend the ability of MLLMs in long video comprehension. A stream of research aims to reduce the number of visual tokens that need to be fed into the MLLM (Liu et al., 2025a; Yan et al., 2025) through compression or selection modules.
A second, related approach focuses on selecting a sparse subset of the most salient frames from the entire video. Unlike uniform sampling, these methods aim to identify moments of high impor- tance (Tang et al., 2025; Hu et 2025a;; Wang et al., 2024b). While effective, the primary limitation of these methods is that the frame selection process is decoupled with its reasoning process, hinder- ing it from learning more complex reasoning patterns. Methods like LongVILA-RI (Chen et al., 2025) focus on direct context extension by continuing training on long video datasets to handle longer video sequences. Recently, promising direction has emerged that leverages the powerful zero-shot capabilities of large proprictary models to act as agents. Frameworks like VideoDeepRe- search (Yuan et al., 2025) and Deep Video Discovery (Zhang al., 2025c) use prompting techniques to guide a strong L.MM like Deepseek-R1 -I (Guo et al., 2025) or GPT 4.1 (OpenAI, 2024) to itera- tively explore a video with external tools. These training-frre methods demonstrate the potential of agentic approach but rely on resource-intensive, closed-source models, making them difficult to optimize, reproduce, or deploy. In contrast, our work focuses on explicitly training relatively small 7B open-source model to learn an efficient, agentic policy for long video comprehension.
METHOD
3.1 OVERVIEW
To address the challenge of efficient long video understanding with a constrained frame budget, we propose a novel framework, VideoZoomer, which empowers a large multimodal model to actively seek highhtemporal-resotune
information by invoking an external tool. Rather than relying on fixed or uniform sampling strategies, our model learns to dynamically and adaptively allocate its frame budget during its reasoning process. The core idea is to train an agent that learns an optimal policy for when and where to request high-frame-rate video clips, a process we call "temporal zoom-in", to gather sufficient evidence for answering a given question.
As illustrated in Figure 2, the strategy is summarized model only has access to the query prompt Q and a relatively low frame rate version of the video Viow. uniformly sampled as a default frame rate flows which provides coarse, computationally inexpensive overview of the entire video. To answer the question accurately, especially when pertains to fine- the-grained temporal events rapid motions, the model may require more detailed visual information. We introduce a <video 2oom> tool, which allows the model to request a specific time segment [[start; lend] from the original video at a higher frame rate, Shigh- Upon
"first glance, then zoom": initially the
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Figure 2: VideoZoomer framework for long video reasoning. The process begins with a "Glance" where the model obtains a coarse overview of the video. It then enters an iterative "Zoom" phase, where it can invoke <vide evideo zoom> tool request high-fps clips and perform multi-turn reason- ing. This process continues until the model procudes a final answer or reaches max turn limit.
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Figure 3: Diverse reasoning patterns demonstrated by our model. (a) Direct-hit Reasoning, (b)
Progressive Reasoning, and (c) Self-refine Reasoning.
invoking this tool, the environment returns high-resolution clip Valip T(V, Extart fends fhigh) ) The agent's objective is interact with the environment by iteratively calling the tool to gather visual evidence, this process continues until the agent determines it has sufficient information to produce final answer. The agentic approach enables the model to develop diverse and complex reasoning strategies, demonstrated in Figure 3. Each tool calling is constrained by frame budget B (i.e. fhigh X (tend" - tstare) < B). thus the total number of frames that can be requested from the high-resolution clips is limited by B N, where N is the maximum number of interaction rounds. The environment returns an error message if the model makes an invalid request or exceeds the frame budget. The goal is learn a policy that maximizes the quality of the final answer while adhering to the frame budget and tool call number constraints.
3.2 COLD-START INITIALIZATION
Reinforcement learning from scratch on a complex, high-dimensional action space, such as gen- erating structured tool calls, is often sample-inefficient and prone instability. To mitigate these challenges, we precede the RL phase with a supervised fine-tuning (SFT) stage designed to "cold- start" agent. The primary objective of this stage is twofold: first. to equip base multimodal model with the fundamental capability of understanding and invoking the <video zoom> tool in the correct format; and second, expose it diverse range of reasoning patterns, which is crucial

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Published as a conference paper at ICLR 2026
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Figure 4: The pipeline for curating cold-start dataset. We first distill exemplar trajectories, then generate reflection data by having an expert model correct the failures of an initial agent. The final dataset combines both verified exemplar and reflection trajectories.
for effective exploration during subsequent RL training. To achieve this, construct specialized SET dataset by curating nigh-quality, multi-turn interaction trajectories as illustrated in Figure 4.
Distillation of Exemplar Trajectories. The initial step is to generate a set of "golden" tool-use trajectories. We leverage state-of-the-art proprietary models, such GPT-4o (OpenAI, 2024) and Gemini-2.5-pro (Comanici et al., 2025), as expert demonstrators. For each video-question pair in our training set, we prompt the expert model with the same system prompt and initial low-frame- rate video provided to our agent. The model then engages in multi-turn interaction, iteratively calling the <video_2e dom= tool until gathers sufficient information to answer the question. This process yields a collection of complete trajectories, each containing the initial prompt, sequence of tool calls, the corresponding high-frame-rate clip observations, and the final answer. These expert- generated trajectories serve as ideal examples of effective tool invocation and reasoning.
Augmentation with Reflection Data. While SFT on only exemplar trajectories effectively teaches the model the format of tool use, we observed a significant limitation: the resulting model tends to overfit the expert model's dominant reasoning patterns. This often leads to "shallow" policy, where the model learns to call the tool at most once and then immediately outputs an answer, regardless of whether the retrieved clip was actually helpful contained errors. This lack of perseverance and adaptability would severely hinder its ability to solve more complex problems requiring deeper, iterative reasoning.
To address this and introduce more diverse and complex reasoning patterns, we generate reflection data. As shown in Figure 4. The process begins by using our initial model trained only on exemplar data, to produce its own rollouts. We then identify trajectories where the model failed to answer correctly. These incorrect rollouts are subsequently fed back the expert model, which is prompted to reflect on the flawed reasoning. The model then identifies the mistake and generates a corrected, more robust reasoning path. This corrected path might involve additional tool calls or a different line of reasoning. This reflection process creates valuable training instances that explicitly teach the model how to recover from errors, critically evaluate the information returned by a tool, and when to persist with further investigation. Furthermore, this on-policy-lite like data generation strategy ensures that the new trajectories are challenging yet achievable, mitigating distribution shift and stabilizing the transition from SFT to RL.
The final cold-start dataset is a carefully curated combination of the distilled exemplar trajectories and reflection trajectories. Before inclusion, all candidate trajectories are passed through verifiers to
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Published as a conference paper at ICLR 2026
ensure quality. This resulted composite dataset, approximately 11,000 trajectories in total, provides a rich and balanced foundation for our base model.
3.3 MULTI-TURN TOOL-INTEGRATED REINFORCEMENT LEARNING
We employ GRPO (Shao et al., 2024) for RL training due to its demonstrated efficacy in enhanc- ing multimodal reasoning capabilities, and extend its original formulation multi turn tool-calling scenarios by introducing a token-level loss mask over the tool-call trajectory that ignores text and image tokens not generated by the model.
Reward Design, The design of the reward function is essential to guide the agent toward the desired behavior. Our reward is assigned at the end of each trajectory and is composed of three distinct components designed promote accuracy, valid format, and exploration:
[ Race(E, y) + Rformat (v) Reool
The accuracy reward Race is the primary trientrde reward, it provides a strong positive signal the agent's final answer correct. The format reward Rformot validates the structure of the agent's response at each turn. This reward is set to a positive value if the model's output strictly adheres to the predefined format, and zero otherwise. Specifically, the agent receives positive reward if every intermediate step correctly wraps its reasoning in <think></thing be followed by either a valid <video zoom></vil </videovvieo zoom> or a final answer enclosed in <answer></ answer>>>> tags. A key challenge during early training is that model unfamiliar with the <video_zor _zoom> tool may be hesitant to use it, often preferring to guess an answer di- rectly. To solve this and encourage exploration, prevent the agent from learning to make redundant or unhelpful tool calls, this bonus is conditional: is only awarded if the final answer is correct.
tags and
introduce a bonus Rtoot for using the tool. To
EXPERIMENT
4.1 EXPERIMENTAL SETUP
Implementation Details. We initialize our model from Qwen-2.5-VL--7-Innnmn (Bai et al., 2025) for its strong foundational capabilities and amenability to reinforcement learning. For cold- start initialization, we adapt the LLaMA-Factory (Zheng et al., 2024) framework. Our RL training and evaluation framework is based on verl (Sheng et al., 2024), which we extended to support multi- turn tool-calling tasks and optimized for efficiency in long video training scenario.
For training data, we use Long VideoReesnnn (Chen et al., 2025), a long video QA dataset comprised of 52K high-quality mnestion-reasonnng answer pairs. cold start stage, we trained our base model with a learning rate of 5 x 10 for I epoch on dataset we construct as described in Section 3.2. During RL stage, we use a learning rate of 10-6, rollout number of 16 and batchsize of 128. The model is initialized with 64 uniformly sampled frames. It can then perform up to 4 subsequent tool calls, each retrieving up to 16 frames of high-resolution clip from a segment of interest, before providing a final answer. To improve training effectiveness and stability of RL training process, we also adapt clip-higher and dynamic sampling from DAPO (Yu et al., 2025). Further details are provided in the appendix.
Benchmarks. To comprchensively evaluate the capabilities of our model, we conducted tests on two distinct categories of benchmarks: long video understanding and long video reason- ing. For long video understanding, we utilized four benchmarks: MLVU (Zhou et al., 2024), LongVideoBench (Wu et al., 2024), VideoMME (Fu et al., 2024), and LVBench (Wang et al., 2024a). These benchmarks encompass variety of tasks designed to assess the model's 's general video com- prehension abilities. For long video reasoning, we employed three benchmarks that require more than superficial visual analysis: VideoMMLU (Song et al., 2025), VideoMMMU (Hu et al., 2025b), and LongVideo VideoRcason (Chen et al., 2025). These chanllenging benchmarks are specifically designed to evaluate the model's integrated perception and reasoning capabilities.

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Published as a conference paper at ICLR 2026
Table 1: Results on long video benchmarks. denotes evaluation results using our own evaluation protocol under max frames of 128. For fair comparison, model is evaluated with maximum of 64 frames in the first round, followed by up to turns requesting a maximum of 16 frames per turn, yielding total of 128 frames.
Long Video Understunding
Long Video Reasoning
Model
Size
Long VidenBench
VideoMME averall long
LVBench
VideoMMLL
VideoMMMU
Long RingRooonRe
dev
trdt
val
eval
Proprictary Models
GPT-4o
64.6
54.9
66.7
71.9
65.3
48.9
44.9
61.2
60.7
Gemini-
75.0
Sonn-S VLMs
Video-LLaVA
37.6
39,9
7B
64.7
47.2
56.4
58.3
46.7
33.4
33.9
LLaWA-NeXT-Vide
Video-XL
7B
64.9
45.5
50.7
55.5
Kangaroo
8B
61.0
54.8
56.0
39.4
LongVU
60.6
LongVA
7B
56.3
41.1
$2.6
24.0
LongVILA
7B
57.1
60.1
LongVILA-RI
7B
57.6
62.4
53.3
67.9
Video-R1"
7B
65.0
52.0
61.1
51.4
38.7
61.3
49.8
22.8
Qwe12.5.V1.
7B
58.3
45.5
51.0
63.5
$3.9
36.9
61.0
48.1
70.8
VideoZoomer'
7B
68.8
55.8
57.7
65.2
55.8
41.5
67.9
52.2
80.3
over base model
10.5
+10.3
+6.7
+.77
+1.9
+4.6
+6.9
+4.1
+9.5
Table 2: Detailed result on MLVU. ER: Ego Reasoning. NQA: Needle QA, PQA: Plot QA, SQA: Sport QA, AO: Action Order, AC: Action Count, TQA: Tutorial QA, AR: Anomaly Recognition, TR: Topic Reasoning.
Single Detail
Multi detail
Holistic
Split
Model
ER
NQA
PQA
SQA
AO
AC
TQA
AR
TR
Avg.
Qwen2.5-VL
47.7
65.1
65.9
50.2
13.6
65.5
85.6
58.3
Dev
VideoZoomer
66.8
80.3
72.9
59.8
52.5
83.3
68.8
Qwen2.5-VL
32.1
53.3
54.0
44.4
32.9
15.0
37.2
38.5
80.2
45.5
Test
VidenZooomrr
58.5
63.3
64.0
44.4
42.9
28.3
39.5
46.2
89.0
55.8
4.2
MAIN RESULT
Baselines. We compare VideoZoomer against a wide range video understanding models, in-
cluding (1) Proprietary models: GPT-4o (OpenAI, 2024) and Gemini-1.5-Pro (Reid et al., 2024); (2) Open-source VLMs: Vidco-LLaVA (Lin et al., 2023), LLaVA-NeXT-Video (Zhang et al., 2024b), Video-XL (Shu et al., 2024), VILA-1.5 (Lin al., 2024), Kangaroo (Liu al., 2024a), VU (Shen et al., 2024), Long (Zhang et al., 2024a), LongVILA (Xue et al., 2024), LongVILA-RI (Chen al., 2025), Video-RI (Feng al., 2025) and Qwen2.5-VL. (Bai al., 2025).
Long Video Understanding. Our model demonstrates marked improvements across range of long video understanding benchmarks, as shown in Table 1. On MLVU, it achieves scores of 66.8 (dev) and 55.8 (test), yielding substantial gains of +10.5 and +10.3 points over its base model, Qwen2.5-VI. VL. This performance is further validated on Long VideoBench and LVBench, where our model scores 57.7 and 41.5, respectively, outperforming all listed open-source baselines. These results collectively underscore the effectiveness of our adaptive temporal zoom mechanism. No- tably, even on benchmarks not exclusively focused on extremely long durations like VideoMME, our method provides clear performance boost (65.2 overall, 55.8 long-sed set) over already strong baseline. This demonstrates that the learned policy to dynamically "zoom" in relevant segments is beneficial across various video length.

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Published as a conference paper at ICLR 2026
Avg.
Validation
20
0.5
Tool Call Count
w m
Accuras 8.70
65
oun wio rellection
refection
13
20
20
40
68
30
40
Stop
Stop
Figure 5: Training dynamies of ablation baselines. The left panel shows the average number of tool calls per sample during training. The right panel displays the model performance (e.g., accuracy) on the validation set over the course of training.
Table 3: Evaluation of Ablation Baselines.
Long Video Understanding
Long Video Reasoning
Model
MLVU
VongNiideeench
VideoMME overall long
LVBench
VidenMMIJU
VideoMMMU
Long Reaeo
dey
test
val
quiz
eval
VideoZoomer
68.8
55.8
57.7
65.2
55.8
41.5
67.9
52.2
80.3
w/o RL
56.4
45.6
42.0
54,4
44,2
26.0
63.5
46.6
63.3
w/o Roout
67.5
52.2
56.2
62.5
52,5
40.6
63.6
53.8
79.9
w/o cold-start
57.0
42.8
43.5
53.5
46.6
35.5
63.9
43.6
59.6
w/o reflection
67.0
53.2
54.8
58.7
47.4
40.9
70.1
52.2
75.1
We present a detailed analysis of our model's performance on the MLVU benchmark Table 2. The results clearly show that our method's improvements are most significant on tasks requiring detailed perception. For instance, the "Single Detail" category of the dev set, our model shows massive gains in Ego Reasoning (ER, +19.1). 1), Needle QA (NQA, +15 +15.2), and Plot QA (PQA,+7.0) The most significant improvement is seen in the "Multi-detail" task of Action Count (AC), where model's score increases from 13.6 to 50.5. This task, which requires counting specific, often rapid actions, directly benefits from the ability to ra-sample critical moments at a higher frame rate. Similar substantial gains observed the test set in ER (+26.4), NQA (+10.0), and AC (+13.3).
Long Video Reasoning. On VideoMMLU and VideoMMMU, our model scores 67.9 and 52.2 respectively, achieving the highest among all open-source models. On Long eadeoRRason-eval. our model achieves highest score of 80.3, surpassing the performance powerful proprietary models like GPT-4o (60.7) and Gemini- 1.5ini--155PPpp 1r5- (67.3). Notably, our model also outperforms LongVILA-RO which trained on same dataset but with a larger frame budget, highlighting the superior effi- ciency of our agentic strategy. This indicates that the iterative, evidence-gad evidence-gathering process enabled by our agentic strategy allows the model to construct more robust and accurate reasoning chain, which is crucial for tackling complex, knowledge intensive video reasoning tasks.
4.3 ABLATION STUDY
Effectiveness of Key Components. To validate the contribution of each key component our framework, we conduct comprehensive ablation study, with results summarized in Table 3 and training dynamics shown in Figure 5. For a fair comparison, all ablated models except "w/o cold- start" were trained using the same amount of SFT data trained only via supervised fine-tun tuning, suffers catastrophic performance drop across all bench- marks (e.g., -17.0 on Long ReaeoRe, confirming that RL is essential for learning effective
final model. The w/o RL model,
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MLVU
LVBench
LongVileeRReson
0.44
0.83
0.42
0./1
Acourac
Acourao
0.02
0:34
Darn
0.30
0.04
S-V-7B
0.72
Dars
0.32
0.92
-W- VLL-7
0.30
Qwenn2 VBB
0.73
129
52
64
12e
256
64
120
299
Number of Frames
Number of Frames
Number of Frames
Figure 6: Performance comparison with varying frame budgets. We compare our model against the Qwen2.5-VL-7B baseline. The x-axis(log scale) represents the fixed frame budget for the base- line and the average number of frames actually used by VideoZoomer on each benchmark.
tool-use policy. Similarly, the w/o cold-start model, which skips our curated SFT stage, fails to converge meaningful policy, highlighting the necessity of strong initialization. Within the cold-start process, removing reflection data (w/ reflection) causes the model to adopt a shallow, simple strategy, where the average tool call count stabilizes at about 1.0, limiting its ability tackle complex problems. In contrast, our full method learns to make nearly two calls on average, enabling deeper investigation and achieving higher accuracy in the validation set. Finally, removing the con- ditional tool-use bonus (w/o Rtoot) leads to "policy collapse". where the agent's tool usage trends towards zero during training, as it fails to discover the tool's ss utility without explicit encouragement. Each ablation results in significantly lower performance on various benchmarks, demonstrating that all components are necessary to achieve the final performance.
Performance Across Various Frame Budgets. To further investigate the efficiency of Video- Zoomer, we analyze the performance of our model and the base model under various frame budgets. As illustrated in Figure 6, we plot the accuracy of the model against the number of frames pro- cessed. For the baseline model, this x-axis represents a fixed, uniformly sampled frame budget. For our model, it represents the actual average number of frames consumed per dataset, result of its dynamic ciency. On MLVU, our model achieves 0.64 accuracy using only 48 frames on average, surpassing the baseline's ' 0.581 accuracy at a much larger 128 frame budget. This trend holds on LVBench, where our model using 77 frames outperforms the baseline using 256 frames. Furthermore, on the LongVideo VedeoReason benchmark, our model and the baseline model both peaks at around 64 frames, suggesting that complex reasoning tasks may not benefit from increasing visual information, which can introduce noise. However, within this optimal frame window, our model's 's peak accuracy of 0.803 significantly surpasses the base model's peak of 0.718. This performance gap underscores our model's 's stronger reasoning capability enabled by its agentic policy.
seceisoonmakinng process. The results clearly demonstrate our model's 's superior effi
Combining with a Frame Selector. Our pri- mary method uses uniform sampling for the initial overview to ensure a global and unbi- ased starting point, we also investigate whether our agentic framework can be combined with more sophisticated frame selectors. To test this, we replace the initial uniformly sampled frames with the output of the output of TSPO- 0.4B (Tang et al., 2025). The results presented in Table shows that providing a more intelli- gently selected initial overview further boosts our model's 's performance by +2.0 on MLVU and +3.0 on VongeedeoB...h. This demonstrates the flexibility and transferability of our approach; the learned policy effectively leverages the improved starting point to conduct an even more efficient and accurate investigation of the video.
Table 4: Performance comparison when com- bining with an external frame selector. Results evaluated using our protocol under consis- tent setting.
Model
MLVU
Long LongVideoBench
Qwen2.5VL
58.1
51.0
+ispo
68.1
54.9
VideoZoomer
68.8
57.7
+tspo
70.8
60.7
9

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5 CONCLUSION
In this work, we propose VideoZoomer to address the critical challenge of long video understanding MLLMs, We empower the MLLM to become an active agent capable of utilizing external tool to investigate long videos more effectively and efficiently through a carefully designed two-stage training process.
Our experimental results robustly validated approach. The ablation studies confirmed that each component-tth cold-start initialization, the reflection data, the RL optimization, and the condi- tional reward bonus- indispensable for achieving final performance. Our model not only achieves strong performance across numerous long video benchmarks, but also demonstrated su- perior frame efficiency, outperforming stronger baselines while using significantly fewer frames. This demonstrates the effectiveness of our agentic strategy in enhancing the perception and reason- ing capabilities of MLLMs for long videos.
ACKNOWLEDGMENTS
This work partly supported by the National Natural Science Foundation of China (Grant No. 62576191) and the Shenzhen Science and Technology Program (ZDCY20250901 103533010).
ETHICS STATEMENT
Our research adheres the ICLR Code of Ethics. This work aims to advance the efficiency of video understanding for positive applications, ensuring transparency, reproducibility, and faimness in all experiments. All datasets used are publicly available, and our use of proprietary models for data distillation complies with their terms of service. We acknowledge that our model, like other large language models, may inherit and reflect biases from its training data. While our method improves reasoning, is not designed to mitigate social biases, and advocate for responsible downstream use and further research into fairness. The intended application is for research purposes, and we do not foresee direct dual-use concerns from our proposed framework.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we provide a comprehensive overview of our methodol- ogy, implementation, and evaluation. Further implementation details, including training hyperpa- rameters and specific prompts used for training and data generation, are described in Appendix A. To facilitate direct replication and further research, we will release our codebase, datasets and model weight upon acceptance of this paper.
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A MORE IMPLEMENT DETAILS
Evaluation Details. We evaluate our model and baselines under a consistent setting with a max- imum of 128 frames and resolution corresponding to 100,352 pixels per frame. For inference, we employed the vLLMM framework (Kwon et al., 2023) with the temperature parameter set to 0 to ensure deterministic outputs.
For the VideoMMLU benchmark, answers are scored by GPT-4o using the official prompt and the final score computed the average score of three disciplines.
Training Details.
We show the key training hyperparameters in Table
Table 5: Key Hyperparameters
(a) SFT stage
(b) RL stage
Hyperparameter
Value
Hyperparameter
Value
Train epochs
Max total response length
32768
Train batch size
64
Rollout temperature
1.0
Learning rate
5e-5
Max interaction turns
Learning rate scheluder
cosine 0.1
Train batch size
128
Warmup ratio
PPO mini batch size
32
Freeze vision encoder
tuue
Rollouts per prompt (n)
16
Clip ratio (low / high) Entropy coefficient
0.2 0.27
0.001
KL coefficient (B)
0.001
Learning rate
1e-6
Reward weight (acc/format/tol))
0. 0.9/0.1/0.5
The SFT training is conducted on 8xH100 GPUs for ~-h, RL training is conducted on 16xH100 GPUs for 4-5h.
Figure shows key statistics of our cold-start dataset. The left panel shows the distribution of total token lengths per trajectory, indicating wide variety of response lengths that cover both simple and complex reasoning chains. The right panel illustrates the distribution interaction rounds (i.c., the number of tool calls), showing that the dataset contains significant number of multi-step examples.
Prompt Template. We provide the detailed prompt (Ma et al.; Zhang et al., 2025a) used for train- ing and cold-start data synthesization as follows:
token length distribution
round number distribution
expert trajectory
trijedory refectin Irajecory
2500
trajectroy
2000
4,000
LOunt
1500
Count
3,000
1000
500
1,000
5000
10000
15000
20000
25000
Langth
Round Numbor
Figure 7: Statistics of the cold start dataset.
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Reasoning Prompt
System Prompt: You are a helpful assistant. You will receive a low-frame-rate vidco and related questions. You can analyze the video content answer the question and trigger high-frame frame-rate inspections when finer temporal resolution is needed. When you detect ambiguous motion/objects that require closer inspection, wrap your request in <video_ze wocoo//ccc////deoo/ooooooo
tags and provide the exact time segment and target frame rate in ISON format: wrideo_zoom. (" [start.sec, end_sec], "fps": n} </video.zorrr zoomm>, it will return the video clip the target fis to help you better answer the question. Note that the total frames num of the request clip cannot exceed 16 (e.g. (end_sec start_sec) fps I 16) and NOT include <answer> tags in this round. Example usage: <video_zoom> ("segment": </vi deo_zoom>. If the initial tool response does not provide sufficient information to answer the question, you may continue to request additional video zoom inspections needed, until you either (1) gather enough information form complete answer, tool. Output the thinking process within <think> </think> tags, once you confirm your final answer place the final answer inside <answer> and </answer>,
14.0, 6.0), "ips": 2}
explicitly instructed stop using
User:...<f wtt.::faaaei..immtteeeeeeemmmee.
Question
Prompt for Constructing Reflection Trajectory
You are expert video understanding model with access to video zoom tool that allows you to request high-frame frame-rate rate clips for temporal inspection. Your task is to correct a flawed analysis of a low-frame rate video by using a video. deo.-ooom tool. Your workflow is multi-turn process: Turn 1: Reflection and Tool Call
Analyze the Error: You will given question, choices, a previous, incorrect attempt. First, you must reflect on why the previous v deo zoom tool call was flawed. Was the time segment wrong? Was the prames-per-scoond (fps) too low? Was the focus of the analysis misaligned with the question?
Formulate a Correction:
Based on your analysis, decide on new, corrected video.zor zzomm request. This request should target the precise moment of interest and use appropriate fps capture the fine-grained detail.
Output the Tool Call: Generate your reflection and the new tool call the specified format. Your output for this turn MUST end immediately after the </video_zon zoom> tag, Do not generate anything further. The system will then execute this call and provide you with the result.
Constraint for the tool call: The total number of frames requested must not exceed 16. That is: (end_sec start_sed Turn 2: Analysis and Final Answer
fps <= 16.
1. Receive Tool Response: The system will provide the high-frame-rate video clip from your corrected tool call.
Analyze the New Clip: Carefully examine the new clip. Describe what you can now clearly see that resolves the question.
Provide the Final Answer: Based your observation, the correct from the choices, enclosed [boxed]].
Output Format Structure:
[FIRST TURN OUTPUT]
<think>
The previous tool call was incorrect because (explain the flaw in the tool use, c.g., wrong segment, wrong fps, or misaligned focus).
Now will zoom in to inspect the motion of '(target object/action)* between (start and (end_sec): with higher temporal resolution.
</think><vvider de...iooo>o.>>>> zoom> ("segment": [start.see, end.sea), "fps": n)
</video. zcom>
[YOUR TURN OUTPUT STOPS HEREJ
[SECOND TURN OUTPUT] (after you receive the tool response)
<think> corrected high-frame-rate clip, [describe what clearly observed now]. </think> <answer> \ booxed(corred answer) </answer>
Example to follow:
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Question: Which hand did the woman use to pick up the cup?
Choices: A: Left hand B: Right hand C: Both hands D: Neither
Previous Trajectory (Wrong): Tool call: <video_zoom
("segment": [0.0, 2.0],
"fps": 2)</video.zoom
(Your First Turn Output Should Look Like This):
<think> The previous tool call was incorrect because it focused on the wrong time segment. The woman only reaches for the cup between 3.0s and 5.0s. Additionally, the low fps of might not be sufficient to clearly distinguish the hand's motion.
Now will zoom in to inspect the motion of "the woman's hand reaching the cup" between 3.0s and 5.0s with a higher temporal resolution, </think>><n <eo_oooommm>>> ("segment":
[3.0,
10.01, " "fps" : }} </video zoom>
(System provides tool response, then you start your Second Turn) (Your Second Turn Output Should Look Like This):
<think>ln In the corrected high-frame-rate clip, the woman's right hand is clearly seen moving towards and gripping the cup handle between 4s and 4.8s, while her left hand remains on her lap. The motion now unambiguous. </think> <answer> B. </answer>
B
MORE EXPERIMENT RESULTS
B.1 DIFFERENT EXPERT MODEL. FOR COLD-START DATA CONSTRUCTION.
In our main experiments, we utilized data distilled from Gemini 2.5 Pro (Comanici et al., 2025) to generate the cold-start SFT dataset. To justift this choice, conduct comparative analysis of data distilled from Gemini 2.5 pro versus data from GPT-4o. We conducted two identical training runs of our model. The only difference was the source of the cold start dataset used in the SFT stage: one model was trained data distilled from GPT-4o, and the other on data from Gemini 2.5 Pro. Both models then underwent the same reinforcement learning phase.
evaluated the final performance of both models on our key benchmarks. The results of this comparison are presented in Table 6. As the result shows, the model trained using data from Gemini 2.5 pro achieved slightly better results most benchmarks. Through qualitative analysis of the generated data, we observed that the trajectories from Gemini exhibited greater diversity in their reasoning patterns and tool-use strategies.
Table 6: Performance comparison using different expert models for cold-start data construction.
MLNU
Long Veneeeeeeeencch
VideoMME long
LVBench
VideoMMLU
VideoMMMU
LongVideoReason
Model
Size
qui:
Qwen2.5-VL
7B
58.1
45.4
$1.0
63.5
53.9
36.9
61.0
48.1
70,8
VidenZoommeggmeee
55.8
57.7
67.9
80.3
VideoZoomee
7B
69.5
54.6
55.5
61.6
51.0
64.1
51.2
78.4
B.2 RESULTS ON OOD TASKS
To assess the generalizability and robustness of VideoZoomer, we evaluated its performance on two distinct out-o--distribution (OOD) task categories: short video captioning and logical reasoning on synthetic data. These experiments were designed to verify that our training process enhances long- video capabilities without degrading the model's foundational abilities.
While our primary focus is on long videos, we tested VideaZoomeron several short video captioning benchmarks TemporalBencher
al., 2 TempCompass(LLin al., 2024b) and VDC(Chai et al., 2024) to ensure its core descriptive capabilities maintained. The results, summarized in Table 7, show that our model not only preserves but significantly improves upon the baseline's performance across all tested benchmarks.
We further tested the model's robustness (Li et al., 2021) on subset of the CLEVRER dataset( Yi et al., 2019), which evaluates causal and logical reasoning synthetic videos. This domain is significantly different from the real-world (Kang et al., 2025; Li et al., 2025), long-form videos used in our training.
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Table Short Video Captioning Benchmark Results
TemporalBench
TempCompass
VDC
Model
(Short Caption Score)
(Captioning Acc)
(Short Ace/ / Score)
QwenVL-22.57B
40.9
52.1
37.8 / 1.98
VideoZoomer
56.4
65.3
49.2 / 2.51
As shown in Table 8, the comparable performance to the baseline model demonstrates that our two- stage training process does not degrade the model's foundational reasoning abilities. The minimal gain is expected, as the glance-and zoozoom mechanism is not designed for the abstract, logical puzzles presented by CLEVRER. This result confirms that our method successfully retains the model's 's core competencies tasks that do not require our agentic framework.
Table 8: Performance on CLEVRER
Model
CLEVRER Accuracy
VL-2.5-5
67.3
VideoZoomer
68.0
B.3 IMPACT OF SFT DATA QUANTITY
We investigated whether the effectiveness of our Supervised Fine-Tuning (SFT) phase stems from the quantity of data. We compared our model, trained on our curated 11k trajectory dataset, against model trained on a dataset of the same composition but with double the quantity (~20k samples).
Table 9: Impact of SFT Data Quantity
Training Dataset
MIVU (dev)
MIVU (test)
L.Bench
LongVideoBench
LongViideoRensoo-evvll
Ours (~11Ik)
68.8
55.8
41.5
57.7
80.3
Scaled Dataset (~20kk)
66.4
56.0
41.4
55.6
80.3
As shown in Table 9, simply doubling the data quantity did not lead to better overall performance. While there was a marginal improvement on MLVU (dev), the model trained on the larger dataset performed worse on all other benchmarks. This result strongly suggests that the effectiveness of our dataset comes from the high-quality, diverse reasoning patterns it contains, rather than its sheer size. This "less is more" philosophy aligns with recent findings from works like DeepSeek-RI -Ineck-RIGGGA R al., 2025) and LIMO(Ye et al., 2025), which demonstrate that few thousand high-quality, reasoning- focused samples can be sufficient to unlock powerful capabilities large models. Our methodology prioritizes rich collection of reasoning pathways over large volume of repetitive examples. (Li et al., 2024)
B.4 ANALYSIS OF CHOSEN FPS
key feature of our <vi deo_zoom tool is that the dynamically generated by the model itself, allowing it to decide not only where look but also how closely to look. To understand the model's learned behavior, we analyzed the distribution of fps values it chose across thousands of tool calls on validation set.
(fps) for "zoom-in" clip
The results in Table 10 reveal that the model does not default to the highest possible fps. Instead, its most frequent choice is a moderate fps in the (1, 2] range, which it selects in 66.2% of cases. This demonstrates that the model learns an efficient policy, requesting just enough temporal detail to solve the task without unnecessarily expending its frame budget. While high fps like 8 might seem
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excessive for full video, it is reasonable and effective choice for examining critical few-second clip, and the model learns to use it sparingly.
Table 10: Distribution of fps Values Chosen by the Model
fps Range
(0, 1]
(1, 2]
(2, 4]
(4, 8]
(8, oo)
Percentage 24.9%
66.2%
8.2%
0.6%
$0.1% %
B.5 PERFORMANCE MAXIMUM ALLOWED TooL CALLS
To understand the impact of multi-step reasoning, we evaluated how model accuracy changes with the maximum number of allowed tool calls. We varied the limit from (no tool use) to and measured performance across several benchmarks.
Table 11:
Performance by Maximum Allowed Tool Calls
Max Tool Calls
MLVU (dev)
MLVU (test)
Long Bonn
LVBench
LongVideeoResson-evval
(No Tool Use)
65.0
52.6
52.7
38.7
64.8
66.7
53.4
55.3
39.6
79.9
67.8
56.6
57.0
41.4
80.2
68.4
56.0
56.2
41.8
81.1
68.8
55.8
57.7
41.5
80.3
The results in Table 1l show a clear trend. The most significant performance jump occurs when al- lowing just one tool call, confirming the fundamental value of our agentic framework. For example, on LongVice VideoReesson--eeee eealn, accuracy leaps from 64.8% to 79.9%. Increasing the limit from one to two calls provides another consistent and significant boost, highlighting the importance multi-turn interaction for refining searches gathering more evidence. After two calls, the benefits of increas- ing the limit further show diminishing returns. While there are minor gains, the improvements are smaller and less consistent. Our final model, with limit of 4, uses this extra budget sparingly for the hard problems, but the core performance is achieved within the first two interactions.
C MORE CASES
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Q:How many green mugs were on the fridge top after first opened the fridge?A.
I0os
<think-The user is asking about the number of green mugs the fridge after the first time the fridge is opened. The relevant frames are around 342.83s. I need to see the motion of the person opening the fridge and what is inside. I will request higher frame rate clip from 342.0s to 343.0s.</thii
<video_
[342.0, 343.0), "fps": 8/c/video z00m2
<think-The zoomed clip clearly shows person opening fridge. Inside fridge, there are two green mugs visible the top shelf. This confirms the number green mugs on fridge top after the time the fridge opened 2, </think>
<answer>B, 2</answer>
Figure 8: Case Study: Single-Detail Reasoning.
Q:What does the man do before frying the food at the end of the video?
A. Adjusts the kitchen timer
Cuts some vegetables
C. Cleans the frying pan
D. Melts butter
Sprinkles some pepper on the food
Turns on range hood
5022
1002
1520k
<thinkeThe question is about the man's actions before frying the food. The relevant the video towards the end, around 1640s. need to the sequence of events leading up the frying determine what he does first. will request higher frame rate clip for the segment from 1630s 1640s. </thin moom>{('segmentt [1630.0, 1640.0), \'fpat": 1)</video zoom>
<thinkeIn the high-frame-raiee clip, the sequence events clearly. From 1630s 1634s, the is using whisk something 1638s, he picks pan the stove. This confirms that before he fries the food, he melts butter the The other options are not shown in this specific sequence. www.ee/eninnnnnseeeeeeaaaaaeee
1635s, he stops stirring and walks the stove. At stick of butter from wooden bowl. At 1840s, he seen adding the butter to
Figure 9: Case Study: Single-Detail Reasoning.
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Q: In accordance with the video footage, who protects the mermaid?
1000s
1602s
30254
<think-The user is asking about who protects the mermaid. The initial frames show the mermaid and shark together, but need to see the sequence of events to understand the interaction and who is protecting her. I'll request a clip from around 30 seconds to see what happens next.</think>> <video zoom> zoom=""segmen""; (30.0, 32.0), 4)</videe zoom>
cvideo_zoom>[""
<thinke-The zoomed clip confirms that shark is holding drawing the mermaid and dog, which suggests that shark is planning something. However, clip does not show the immediate interaction the protection the mermaid. need to request clip from around seconds see happens </think> <video_ poooooooom>>/// ('segment": [32.0, 34.0], "fps": </vidoo zoom>
<think<Thhe zoomed clip shows that the shark now standing next the mermaid, and the dog in background. confirms
actively protecting the mermaid, dog mermaid clear,
immediate vicinity. interaction between shark clearty the providing s/ttcctton../think- <answer>\bowed(A. The shark.)s/answer>
Figure 10: Case Study: Self-Correctio and Refined Reasoning.
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Q: Which of the following options correctll matches the sequence of actions as they actually appear in the video?
A, making jewelry cleaning toilet paragliding carving pumpkin
B. paragliding ->> making jewelry --> cleaning toilet carving pumpkin
C. carving pumpkin -> cleaning toilet makingjeweiy ->> paragliding
D. paragliding ~> cleaning toilet -> pumpkin- ->>
<think The user wants to match sequence events presented different clips provided frames. accurately match the sequence, need clarify order of specific moments relevant timestamps necessary
mentioned question. higher get precise details. start segment where paraglider </think> <video zoemee"semment" [50.0, 52.0). "fpa": 8)</video_ zoom>
inspection
<think-TThe clpp confirmed that the sequence inclucies paragtiding. Next, I need to confirm the order of the other specified Pantiessuu214mmakinn jewelry, cleaning toilet, and carving pumpkins. I'll zoom into another section where jewelry and cleaning the toilet are visible for better details and context. I'll focus on the relevant timestamps for these activities. Ins/tinnkes Ludeo
evideo zoom> (V" seggment'': (90.0, 92.0), V'fpal": 4) </video zoom>
<think>TThe second clip confirms that the activity of making jewelry occurs before cleaning the toilet. Now, will inspect the segment where cleaning a toilet visible to better understand the sequence these events. The cleaning clips usually appear towards will request clip from around 180.0s 182.0s see transition from the previous scene next. wtthi/tt-i-iviioooooooo ("sogment": (180.0, 182.0], "fps": 8) </vdoo_ zoom>>
later part of the sequence
<thinke-The third clip confirms that the cleaning of the toilet follows the lu201cmaking jewelrylu201d activity. Finally, need to confirm the last action, which is carving a pumpkin. The pumpkin carving is at the initial stages in the video sequence. will now request sequence around the moment this specific activity is visible in greater detail. stitk-i-thinkk--vvddo.. zoom> ("segment": [279.0, 281.0), "fps": 8) </vidoo_zooms
<think>The zoomed clip confirms carving pumpkin shown between 279.0s 281.0s. This confirms carving pumpkin final in the sequence. Combining the observations, sequence of events is paragliding, making jewelry, cleaning toilet, carving pumpkin. </think> <answer>B, paragliding making jowelry cleaning toilet --2 >umpkinc/answer>>>>>>>>>>>
Figure 1l: Case Study: Multi-Detail Reasoning for Complex Queries.
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Based on the video, which event are the attendees most likely participating in, and what commenorative item is associated with the manager 2014 achievement?
A. A Baltimore Orioles home game at Camden Yards, featuring 2014 AL Manager of the Year garden gnome giveaway
B. A Baltimore Ravens playoff game, distributing Super Bowl commemorative footballs
C. A Baltimore Orioles fan convention, offering signed baseballs by 2014 MVP players
D. A charity event at local park, giving away team jerseys from the 2014 season \nkn"
beseball stadium (l"Camden Yords'" visible). primarily wearing "'riolles" shirts Several frames show people handing out presenting commeroorative boxes. Breckgrund, we see Y'Buck Showalterl" on products. Frame 11 contirms \"Buck Showalter Garden Gnome 2014 AL Manager of the Year. 1" The event is cleerly associsted
<hhnnk-The video shows crowd
distribution specificaally 2014 schievement, Ined a better look the fromes where the man being interviewed. Beeuu6169uu24b at timestamp 12s seems most relevant. will request higher frame rate clip from there the specific moment when the product presented the reason aiveaway mentoned.s/thin soggettsssseetttttt [12.0, 14.0), Vipett: @jx/vicee_zooom zoom>
(he event game day
managee's
<thinleInThe previous tool call incorrect because it focused interview to enswer question. question requires ldfentfylnng both the event the speaific commemorstive generally shows an Onioes event, the previous call from 12s-14s only shows fan talking and provides no details about the item Itself. The crucial information presented the item's box.InNow will zoom in to inspect the 'text on the commerorative boxt between 21.0s and 23. 23.0s with higher temporal. resolution to clearly read the fetails..ns//inne <video zoom> ('segmentt": [21.0, 23.0), Vipstt: <Niddo__oomm zoom
that did not contain the necessary informatio
video
given away. Frame:
frame
clearty
<thinkolnnin the corrected high-frame-rste dij, the careers zooms on the boves being given away. The text the box now perfectl clear, Frame 4 21.52a and subsequent frames show the Beltimore Orioles and the text BBBUK SHOWALTER GARDEN sigh confirms the location is Oriole Park at Camden Yards, This confirms all parts of option A: the team (Orioles), the location (Camden the achievement (2014 AL Manager
MANAGER THE YEARY. crowd wearing Orioles gear, Yards), the item (garden www.eeeeniibooooeAAjeereee
GNOMEY" and
STREETY (frame 4]
beari.nn</thinc>
Figure 12: Demostration of cold start data.
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Based the video's content, which activity shown scenes **not** one the restrictions Saudi Arabian women face according to the 1110 Things that Saudi Arabian Women Still Cannot Dol"?
Driving a car independently
B. Voting in elections
C. Competing freely in sports
D. Marrying without male guardian consent
idantify actidity among the given list that is not one of the restrictions 1n3. ried confirm what the video explicitly states as restriction.in/ieee' examine the provided frames driving, which market, with text ("MAKE-UP(" on the left Frame at 194 seconds: Shows womentuBcss0u.5511 foooiinoogaaa at an abays, with text 1*8. WOMEN' LOYLIM" Other frames like
<think-in1. video
Saudi Arabian Women Still Cannot Int". The
which of these activities appearin in the video restriction:n- Frame at seconds: Shows woman 7V Frame
seconds: Shows I'VOTING""'n
seconds: Shows
introduce other restrictions, However, no explicit mention of the spacific activity of protected Wwiite men so they can righffully protect Ininlhh restriction about men having to present for cartain of women's actions be protected explicitty mantions men. It's clear from the provided frames that voting appears explieitly as restriction (V"VOTIGG"* 115s). cirectly answers question as Voting elections explicitly mentioned as restriction, and other options are mentlaned previously in the video. can see the relevant point about vating and contirm it by requesting highartrame rote clip inpnnnnneee's inspect segment around seconds where I'VOTING!' tirsst presented. request for more detailed frames hore is appropriate. tnchtiikk> cvideo poommn""""mentt" (101.0,, 103.0), V'fpst:: 7/:/video zoom>
and
<thinkoAnThe previous tool call was incorrect because focused on a completely irrelevant time segment. The call zoomed ntt 101.0s to 103.0s, which shows woman getting her makeup done. This segment has na connection to of the activities listed in question (criving, voting, sports, marriagel. The enalysis was therefore answer the question correctly, must first undorstand that the video lists all four optionsiu20144 14A, C, and Diu20 14a restrictions. The question asks which
premise
misalighed tool InlnTo
"not" restriction, implying the video itself provides visual evidence contradict points. Ininn "*DrivingAA"" is listed as restriction, but the video shows a woman driving, smiling, and making a peace sign (Frame 9, 33.39e An VVVeting [9)* is listed as restriction (Frame 53, 314.48s) "Comppting sports [C]" is usted as restriction, but the 237
of
consent (D)** is and pearictiion(Frmmm (Freme 46, 272.94e) Anknooth driving and sports show visual contradictions, However, the scene of the women driving direct celebratory of footage that strongly suggests this Vrestriction"" being overcome no longer in place. will zoom in this specific moment to confirm the celebratory nature of the action, which would explain why it's the correct answer. answer. The previous analysis completely missed knwnNoo zoom inspect the motion of "the woman driving car' between 52. 54 cuideo zoom? (1 ""segmant)" [52.0, 54.0), \"fpal:: 8) chvide zoom?
shows female athletes as a restriction
Olympics
"Marrying
higher temporal resolution.
"ethink=Anin the corrected high an in a headscart and sunglasses is sitting in the driver's seat of car. She turns towards the camera, smiles contidently, and raises
high-trom----ttee fromo-rate clip, can now clearty see sequence events
52. 54.
presents ecene, slong with others of women driving, immediataly atter listing Driiing a Carl" as restriction number 7. This visual evidence of woman driving heaplly and triumphently directly contredicts the video's claim that this something they Vitill cannot While other activities like Vssmpeeing sports)" are elso shown, the footage the woman driving with peace sign is more direct and personal refutation of the restriction presented in the video. The other listed restrictions do not have such clear, contradictary visuel representaiion.tnstiin Sanseer>bpoeed/A/llnnneess
\"peacel" \"victory\" sign. The action deliberate celebratory.
Figure 13: Example of cold start data.
25


Reviews:
[{"review_id": "GvAcUCPoZJ", "reviewer": "Reviewer_21rm", "summary": "This paper investigates the video LLM with self-bootstrapped clip selection capability by SFT and RL to reduce the frame sampled and inference cost. This paper presented VideoZoomer, a agentic framework integrating <video_zoom> tool call and <think> thinking abilities, to select video clips for its processing. The proposed VideoZoomer method can reach outperforming accuracies compared to previous methods while using less input frames. The author also performed detailed ablation studies on cold-start, reflection, RL, and score design.", "questions": "See weaknesses.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1) This paper is one of the first cohort to explore the frame selection method by agentic RL in video LLM domain, delivering substantial novelty. Also, the agentic RL framework is not simply adopted from the image/language domain to the video domain. Instead, it incorporates tool calling and methods designed specifically for videos (e.g., temporal zoom-in, on-policy reflection).\n\n2) The resulted agentic model by GRPO training, outperforms the baseline model by a large margin, especially on the long video understanding task. The author also detail key components in proposed RL framework, showing that the cold-start and reflection finetuning is essential for good RL models in reasoning video frame selection model, which is of great valuable insights.", "weaknesses": "Overall this paper is of great technical value and soundness, but there are some minor concerns listed below:\n\n1) There are several minor typos across texts, including line 151: \"stragety\", line 277: \"fotmat\" and more.  The author should perform grammar and word check throughout the paper;\n\n2) Are other capabilities of video LLMs (Qwen 2.5-VL) well maintained? Like short video captioning?\n\n3) Qwen 2.5-VL is known to lack of native <think></think> reasoning capabilities. The authors performed off-policy warm-start and on-policy reflection SFT to enable the reasoning capability but is it robust when the input data is out of distribution of SFT training data, like very simple CLEVRER data? Just curious and I want to hear the authors' insights into it.", "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": "SPWLv32WSv", "reviewer": "Reviewer_JYRh", "summary": "The paper introduces VideoZoomer, an agentic model enabling MLLMs to dynamically adjust their visual focus during reasoning. Beginning with a low-frame-rate overview, the model uses a temporal zoom tool to capture high-frame-rate clips at key moments, progressively gathering fine-grained evidence. The training process involves supervised fine-tuning on curated datasets, followed by optimization with DAPO. Experimental results demonstrate that the 7B model exhibits diverse and sophisticated reasoning capabilities, achieving strong performance in long video understanding tasks.", "questions": "- Will further scaling up the training dataset continue to improve the model’s performance?  \n\n- The paper mentions starting with 64 frames as input, gradually increasing by 16 frames each time. However, in Figure 1 (the right part), the number of input frames for VideoZoomer seems inconsistent with this description.  \n\n- Will the code and the training dataset used in the paper be open-sourced?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "- The paper contributes a training dataset comprising 11,000 trajectories, which is used to enhance the tool-calling capabilities of models.  \n\n- The case visualizations presented in the paper are good", "weaknesses": "- The technical contributions of the paper are limited, as its main novelty lies in providing a curated training dataset to enhance the tool-calling capabilities of models.\n\n- The method adds a bonus to tool-call rewards when the final answer is correct, which increases the unnecessary frequency of tool use. For example, the model may continue calling tools unnecessarily, retrieving irrelevant clips even after it already has the correct answer, ultimately inflating the reward.\n\n- While the paper mentions using 11,000 trajectories for the cold-start phase of training, it does not specify the data used during the RL phase, leaving gaps in transparency and reproducibility.", "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": "ixwZMlmaOG", "reviewer": "Reviewer_mPz9", "summary": "VideoZoomer is an agentic long-video QA framework that starts with a coarse “glance” and then issues targeted zoom calls over selected time spans to gather fine-grained evidence before answering. It’s trained in two stages—cold-start supervised trajectories (including reflection to correct failures) followed by reinforcement learning with rewards that encourage accurate, well-formatted, and purposeful tool use. Experiments across diverse long-video understanding and reasoning benchmarks show consistently better accuracy–efficiency trade-offs than single-pass baselines, and the approach remains compatible with stronger initial frame selectors.", "questions": "1. How does accuracy change with 0/1/2/3/4 zoom calls? Is there a point of diminishing returns?\n\n2. When the model zooms the wrong time span, does it recover in later rounds, or does it lock in and fail?\n\n3. How often does the model answer correctly without making any zoom calls, and what types of questions and accuracy are those?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper introduces a clear agentic framework that couples coarse “glance” perception with targeted temporal zooming, yielding a principled separation between broad coverage and fine-grained evidence acquisition.\n\n2. This paper evaluates across diverse long-video understanding and reasoning benchmarks, with the largest gains on tasks that require precise temporal detail, supporting the method’s intended use case.", "weaknesses": "1. The zoom tool is basically one-dimensional: A lot of long-video questions hinge on tiny textual clues (scoreboards, signs, on-screen text), and just “adding frames” won’t reliably capture those.\n\n2. The cold-start data comes from external frontier models (e.g., GPT-style teachers). That brings possible style bias\n\n3. Multi-round zooming can be expensive in practice.", "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": "HG1PTQcgw6", "reviewer": "Reviewer_xgg2", "summary": "This paper proposes an agentic video processing framework: the model is first provided with several frames at a low frame rate and then it can use the temporal zoom tool to obtain high-frame-rate clips at autonomously chosen moments. The framework is worked in a multi-turn manner. The training undergoes a two-phase recipe: a cold-start supervised fine-tuning phase and a reinforcement learning phase. And the authors construct a cold start dataset with 11k samples and diverse reasoning patterns. The resulting model VideoZoomer achieves a remarkable performance improvement over the baseline Qwen2.5-VL 7B.", "questions": "Please see the weaknesses above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper constructs a cold start dataset with diverse reasoning patterns, especially the reflection data. Construction reflection data from self-generated failure CoTs is interesting and reasonable, which could be a better way to collect high-quality CoT data.\n2. The model is evaluated on a broad range of benchmarks, including long video understanding benchmarks and long video reasoning benchmarks. The robust improvements across multiple benchmarks validate the effectiveness of the proposed methods.\n3. The paper also demonstrates that VideoZoomer can further equip a frame selector for frame initialization to improve the performance, showing that the method is orthogonal to other dynamic frame sampling methods.", "weaknesses": "1. This work uses LongVideoReason as the training dataset and constructs a cold start training dataset. And the reflection data is constructed from incorrect rollouts. The rollouts are multi-step CoTs, and the errors may occur at any step, CoTs for zoom-in or CoTs for answers.  However, it seems that the LongVideoReason dataset does not have gt timespan annotations. How to determine whether the CoTs for zoom-in are correct or not?\n2. The cold start dataset is relatively small.\n3. The ablation study on reflection cold start data is not convincing, as more sft data is used.\n4. The performance of Qwen2.5-VL 7B baseline on long video understanding benchmarks is relatively low with 128 frames. Can you provide more inference details?\n5. The framework is a multi-step reasoning process. A common failure case is that the reasoning can not stop (i.e. the model zooms in on the video endlessly or the model does not provide a valid answer within the budget). How to handle this failure case during training and inference?\n6. Compared with other dynamic frame sampling works, this work introduces another sampling variable, 'fps'. And in the provided cases, Figures 4 and 8, fps=8 is extremely high compared to other sampling strategies. Can authors show how the predicted fps affects the performance?\n7. The 'fotmat' in Equation (1) is a typo.", "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)