VidBridge-R1: Bridging QA and Captioning for RL-based Video Understanding Models with Intermediate Proxy Tasks
Authors: Xinlong Chen
Yuanxing Zhang
Yushuo Guan
Weihong Lin
Zekun Moore Wang
Bohan Zeng
Yang Shi
Sihan Yang
Qiang Liu
Pengfei Wan
Liang Wang
Venue: ICLR 2026 Poster (2026)
Keywords: video-qa
captioning
RL
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: The "Reason-Then-Respond" paradigm, enhanced by Reinforcement Learning, has shown great promise in advancing Multimodal Large Language Models. However, its application to the video domain has led to specialized models that excel at either question answering (QA) or captioning tasks, but struggle to master both. Naively combining reward signals from these tasks results in mutual performance degradation, which we attribute to a conflict between their opposing task natures. To address this challenge, we propose a novel training framework built upon two intermediate proxy tasks: DarkEventInfer, which presents videos with masked event segments, requiring models to infer the obscured content based on contextual video cues; and MixVidQA, which presents interleaved video sequences composed of two distinct clips, challenging models to isolate and reason about one while disregarding the other. These proxy tasks compel the model to simultaneously develop both holistic, divergent understanding and precise, convergent reasoning capabilities. Embodying this framework, we present VidBridge-R1, the first versatile video reasoning model that effectively bridges the paradigm conflict. Extensive experiments show that VidBridge-R1 achieves significant performance gains on both QA and captioning within one model, demonstrating the efficacy of our approach in fostering more generalizable and powerful video understanding models. All code, models, and data will be made publicly available.

Published as a conference paper at ICLR 2026
VIDBRIDGE-RI: BRIDGING QA AND CAPTIONING FOR RL-BASED VIDEO UNDERSTANDING MODELS WITH INTERMEDIATE PROXY TASKS
Xinlong Chen'223 Yuanxing Zhang', Yushuo Guan', Weihong Lin', Zekun Wang', Bohan Zeng', Yang Shi", Sihan Yang', Qiang Liu 1.2 Pengfei Wan', Liang Wang! 1.2
New Laboratory of Pattern Recognition (NLPR),
Institute of Automation, Chinese Academy of Sciences (CASIA)
School of Artificial Intelligence, University of Chinese Academy of Sciences
3 Klling Team, Kuaishou Technology *Peking University
ABSTRACT
The "Reason-Then-Respe Reeponnd paradigm, enhanced by Reinforcement Learning. has shown great promise in advancing Multimodal Large Language Models. How- ever, its application to the video domain has led specialized models that ex- cel at either question answering (QA) or captioning tasks, but struggle to mas- ter both. Naively combining reward signals from these tasks results in mutual performance degradation, which attribute to conflict between their oppos- ing task natures. To address this challenge, we propose a novel training frame- work built upon two intermediate proxy tasks: DarkEventlnfer, which presents videos with masked event segments, requiring models to infer the obscured con- tent based on contextual video cues; and MixVidQA, which presents interleaved video sequences composed of two distinct clips, challenging models to isolate and reason about one while disregarding the other. These proxy tasks pel the model to simultaneously develop both holistic, divergent understanding and precise, convergent reasoning capabilities. Embodying this framework, we present VidBridge-R1, the first versatile video reasoning model that effectively bridges the paradigm conflict. Extensive experiments show that VidBridge-R! achieves significant performance gains on both QA and captioning within one model, demonstrating the efficacy of our approach in fostering more generaliz- able and powerful video understanding models. Code is available at https: // //github. ctt.luu....oo///ii.ieeeee. wtt.aa/iriieeeee.iviiieeeee. R1/VidBridge R1.
INTRODUCTION
The release of OpenAl o1/o3 (Jaech et al., 2024) and DeepSeek-RI (Guo et al., 2025) has introduced novel Ceasson-ThenRespoore paradigm to the development of large language models (LLMs), which significantly enhances model performance through test-time scaling. Inspired by this ap- proach, growing body of research (Team et al., 2025; Chen et al., 2025a; Shen et al., 2025; Deng et al., 2025; Xia et al., 2025; Yao et al., 2025) has extended this paradigm to multimodal large language models (MLLMs). By leveraging reinforcement learning (RL), particularly the Group Relative Policy Optimization (GRPO) algorithm (Shao et al., 2024), these studies have achieved promising results in image-based reasoning tasks.
Recently, several studies (Feng et al., 2025; Zhang et al., 2025c; Chen et al., 2025g:h) have begun to explore the application of the Reason-Then- Then-Respond paradigm in the video modality. Some efforts focus on enhancing question answering (QA) capabilities in general or reasoning scenarios (Li et al., 2025c; Dang et al., 2025), while some other works concentrate solely on improving video captioning performance (Li et al., 2025b; Meng et al., 2025a). However, these approaches remain narrowly
"This work was conducted during the author's 's internship at Kling Team, Kuaishou Technology Corrrsponniig author: qiang.liue liu@nlpria..accn

---

Published as a conference paper at ICLR 2026
tailored to specific tasks and fail to achieve effective integration of both QA and captioning within a unified model framework.
A key advantage of MLLMs lies in their versatility, enabling strong performance across diverse tasks simultancously. It is therefore undesirable to enhance reasoning capabilities at the expense of generalizability by over-specializing the model in single task. To preserve generality in both QA and captioning tasks, an intuitive approach is to combine the reward signals from them during train- ing. However, as shown in Figure I, simply mixing the datasets and rewards leads to performance degradation on both tasks. We attribute this phenomenon to a confict in the learning paradigms between QA and captioning tasks when training with RL, an indirect optimization method guided by global reward signals. Specifically. QA is inherently convergent reasoning task that requires the model to locate and output a unique, low-entropy correct answer from abundant information. In contrast, captioning is a divergent generation task that demands the model to produce diverse and comprehensive high-entropy descriptions. These opposition task objectives may lead to inherent conflict during RL optimization.
To solve this problem, we resort to two proxy tasks that focuses more on comprehensive un- derstanding of the videos, rather than focusing on the downstream task form. Inspired by the midddl" (Guo et al., 2024) in code generation and "style mixing (Karras 2019)" in image generation, we design Dark- EventInfer and MixVidQA, aiming bridge the paradigm gap between QA and captioning. Specifically, in DarkEventInfer, certain event segments within the original videos are masked out with black screens, and the model is re- quired to deduce and predict the masked events based on contextual video cues. In Mix VidQA, two distinct videos are interleaved, with ques- tions targeting only one of them, and the model must identify and reason about the most rele-
LongVideoRecch
General
MVDench
VME
al.,
wi Caption
wi VideocJA
Caption VideoQA
ndd Proxy Tasks
MMVU
DREAM-IK
Reasuning QA
Captionin
IntenQQ
Figure Performance comparison on QA and captioning tasks under different training setups. Details can be found in Section
vant video content to provide accurate answers. These tasks compel the model to perform both divergent holistic understanding of the video context (as required in captioning) and convergent pinpointing of key information (as needed in QA), thereby enhancing structured video representa- tion and contextual reasoning abilities. Incorporating these proxy tasks during training, we present VidBridge-RII RI,, a versatile video reasoning model that excels at answering questions in general or reasoning scenes, as well as video captioning. As illustrated in Figure 1, VidBridge-Rt effectively alleviates the paradigm conflict between QA and captioning. leading to significant performance gains in corresponding tasks. Our contributions can be summarized as follows:
We develop VidBridge-RI, the first versatile video understanding model capable of simultane- ously handling QA and captioning tasks under the Reason-Then--Rese
paradigm.
We propose two novel intermediate proxy tasks, DarkEventlnfer and Mix VidQA, designed to bridge the paradigm gap between the divergent nature of video captioning and convergent reason- ing demands of question answering in general or reasoning scenarios.
Extensive experiments demonstrate that VidBridge-RI achieves significant performance improve- ments on various video general understanding, cognitive reasoning, and video captioning tasks.
RELATED WORK
2.1 MULTIMODAL UNDERSTANDING MODELS
Multimodal understanding models are widely recognized crucial step toward achieving artificial general intelligence (AGI) and have seen remarkable progress in recent years (Zhang et al., 2025a; 2026; Shi et al., 2025a;e; Chen et al., 2025d; 2026; Hua al., 2025). The LLaVA series (Liu al., 2023) aligns visual and language representations through fully connected layers, equipping LLMs with the ability interpret visual inputs. The Intern-VL. series (Chen et al., 2024b) achieves more
2

---

Published as a conference paper at ICLR 2026
sophisticated visual understanding by employing large-scale visual encoder to capture fine- tne-grained details. Beyond architectural innovations, various methodological approaches have been proposed to improve the model's multimodal understanding abilities. To process longer video sequences, some studies (Li et al., 2024b; Shu et al., 2024) compress visual information, while others (Wang al., 2024d; Chen et al., 2024a) extend the context window of LLMs, enabling the modeling of longer temporal sequences of video frames. Additionally, specialized models like Tarsier2 (Yuan al., 2025), Mavors (Shi et al., 2025b) and CogVLM2-Caption (Hong et al., 2024) have shown impressive results in video captioning through carefully designed training pipelines and diverse datasets. However, these models remain limited conventional video understanding paradigms. In this work, we aim to move beyond traditional frameworks and explore the performance gains achieved through the Reuson- Theoon--Theh-eeee Reensoo paradigm.
2.2 MULTIMODAL REASONING MODELS
Following the success of DeepSeek-RI (Guo et al., 2025), research on multimodal reasoning mod- els has experienced rapid advancement. In the realm of image-based reasoning, numerous stud- ies (Huang et al., 2025; Peng et al., 2025b; Tan et al., 2025; Chen et al., 2025b;c) achieve reason- ing and reflection capabilities by training models on geometry-related datasets. MM-Eureka (Meng et al., 2025b), on the other hand, compiles a wide range of problems from education curricula to sim- ulate human learning processes, thus improving the model's rros-disciplinarry reasoning abilitics. Additionally, other efforts (Liu et al., 2025c;b; Shen et al., 2025) focus on improving object detec- tion and grounding capabilities by designing In the realm of video-based reasoning. Video-R1 (Feng al., 2025) and VideoRFT (Wang al., 2025) adopt a two-stage training strategy that combines SFT with RL., enhancing the model's QA capabilities in general or reasoning scenarios after training on extensive image and video datasets. VideoChat-RI (Li et al., 2025b), in contrast, applies RL directly to develop task-specific reasoning models. Meanwhile, VideoCap-RI (Meng et al., 2025a) focuses exclusively on advancing video captioning performance. Despite these efforts, existing methods remain narrowly tailored to partic- ular tasks and lack an effective integration of QA and captioning within unified model framework. Our work aims to bridge this gap by leveraging intermediate proxy tasks.
crresectioonove--nniii
(IoU) related reward functions.
3 PROBLEM FORMULATION
The powerful versatility of MILLMs is one of the key reasons for their popularity and widespread adoption. For vidco understanding models, important application scenarios include perceptual un- derstanding of general scenes, cognitive reasoning in complex scenarios, and the generation of fine- grained captions for video content. However, recent studies adopting the Reason-Theen Thenon---------podnd paradigm have often improved model performance in specific application scenarios at the expense of the model's 's overall versatility. In this study, we aim to enhance the model's 's generalist capabilities across the aforementioned application scenarios by introducing intermediate proxy tasks to bridge the diverse capability requirements imposed by different applications. The complete implementation details, including the data curation pipeline and the reward function specifications, are presented in subsequent sections of this work.
4 DATA CURATION
In this section, we present the curation process of the training data, including the two intermediate proxy tasks designed to bridge the gap between QA capabilities in general or reasoning scenarios and video captioning tasks. By jointly training on conventional VideoQA and captioning tasks along with our proposed proxy tasks, the model not only learns the basic output format but is also encour- aged to integrate divergent holistic understanding of the video content (as required in captioning) with convergent pinpointing of key information (essential for QA). This training approach promotes structured video representation and enhances contextual reasoning abilities, thereby mitigating the divergence between QA and captioning while improving performance on both. Illustrative examples of from each task can be found in the right part of Figure or in Appendix B.3.
3

---

Published as a conference paper at ICLR 2026
BarkEwentnfer
Multiple Video Tasks
Policy Model
Propee
2.
oely creeet partall soneet will crean
Ui bick csn rive country
Sample responses
Poflcy apdiate
MisViaQA
F2
0g
Oc
A
Aa
Compute remaraly
Compate advaniages
puass lai puria Acany
vil) deae there bin Aesgginnne wovie
fL.
currec Inoureet
Final Reward
Format Reward
Task-specifie Reward
[u.
Vidtor&
Caption
Soanaard Barmat:
"hink imceeniu procen) Stiikk Staawerh (find aaviii Siazeaar
cornect Incernet
CC
Ressib
valid Surmat imvilii format
and
Rcppes RassoO Ruyyert
The) dore tegatics Jaiciig
Figure 2: The training framework of VidBridge-RI. By incorporating intermediate proxy tasks, VidBridge-RI effectively bridges the gap between QA capabilities general or reasoning scenarios and video captioning tasks.
4.1 CURATION OF DARKEVENTINFER
Inspired by the "fill-in-the "ill-n-thhemmidle"" (Guo et al., 2024) approach code generation, we design the first proxy task, DankEventinfer, encourage holistic video context understanding. Specifically, in this task, the model is presented with a video containing black-screen segments and is required to infer the events that occur during the masked periods based contextual video cues.
Using event captions and their corresponding timestamp annotations from COIN (Tang et al., 2019) 9),, randomly select one event per video and replace it with black screen. The model is then asked to reason about and predict the masked event based on the contextual information provided by the surrounding visible segments.
To ensure the quality and learnability of the dataset, we conduct human evaluation on the masked videos. Instances where human annotators are unable infer the masked event are removed, and any inaccurate or ambiguous captions are revised. These steps ensure the final dataset offers meaningful contextual signals, enabling models effectively learn how reason about the "dark events".
4.2 CURATION MIXVIDQA
Drawing inspiration from the "style mixing" (Karras et al., 2019) technique in image generation, we design the second proxy task, Mix VidQA, to enhance the model's ability to attend to and extract key information from videos. In this task, the model is presented with interleaved video sequences from two distinct video clips, and required to answer questions based on most relevant video while ignoring the other.
We source video clips from Kinetics (Kay et al., 2017), each lasting around seconds. To construct the mixed video sequences, interval ranging from 1.5 to 2 seconds. For each mixed video, we generate a set QA pairs using VL-72B (Wang et al., 2024b), explicitly referring one of the original clips. The model to be trained is expected to identify the relevant video segment to provide the correct answer based on the mixed video sequences.
randomly select two video clips and interleave them with a random
Qwen2-VL-72B
All generated QA pairs undergo manual review to ensure quality. Any pairs with ambiguous refer- ences unclear ground truths are removed, guaranteeing the reliability of the dataset.
4

---

Published as a conference paper at ICLR 2026
4.3 CURATION OF VIDEOQA AND CAPTIONING DATA
In addition the two proxy tasks described above, we incorporate additional data for conventional VideoQA and video captioning to explicitly familiarize the model with the target output formats of these downstream tasks. For the conventional VideoQA task, sample instances from the training set of NEXT-QA (Xiao et al., 2021), ensuring broad coverage of question types. For the captioning task, which demands richer linguistic generation, we curate high-quality videos from Koala-36M (Wang et al., 2024c) and KVQ (Lu et al., 2024), prioritizing video diversity in aspect ratios, motion intensity, visual scenes, and thematic content. This enables the model gain a deeper understanding of dynamic video content. For Koala-36M, we adopt the original captions. For KVQ that lacks video captions, we employ Gemini-2.5-PP (Gemini Team, 2025) to generate high-quality textual descriptions.
4.4 DATA FILTERING FOR GRPO TRAINING
Considering that the GRPO algorithm may become ineffective when all candidate answers receive identical rewards, resulting in zero advantage functions, we adopt pre-filtering strategy. Specif- ically, we utilize Qwen2.5-VI. VL--7 (Bai et al., 2025), forcing it to engage reasoning by specific prompt and sampling five responses with temperature of 1.0. For the captioning task, we com- pute the Fl score of each response using AutoDQ (Wang et al., 2024a), and discard samples where the variance of the Fl scores across the five responses is less than 0.2. For other tasks, we filter out questions for which all generated answers are uniformly correct. The above methods ensure effective policy updates during GRPO training. The final training set comprises 1,841 samples from DarkEventInfer, 2,332 from MixVidQU 2,003 from VideoQA, and 4,624 from the Captioning task, totaling 10,800 high-quality training instances.
TRAINING STRATEGY
Many existing works (Feng al., 2025; Zhang et al., 2025b) adopt two-stage training approach, where the model is first trained via SFT and then refined by RL. However, we find that when using high-quality reasoning data with carefully designed intermediate proxy tasks, the SFT stage is not only unnecessary but can also impair the model's 's inherent reasoning potential by forcing it to learn specific reasoning pattern (as illustrated in Appendix E). In contrast, applying RL directly the model can effectively stimulate its reasoning abilities. For the RL algorithm, we employ GRPO without KL divergence regularization (i.e., setting À 0 in Equation 2, analysis can be found in Appendix C.3), and design diverse reward functions for different tasks, as shown in Figure 2 and detailed in the following.
5.1 GROUP RELATIVE POLICY OPTIMIZATION
Group Relative Policy Optimization (GRPO) significantly reduces both training time and GPU mem- ory usage by eliminating the need for a separate critic model in Proximal Policy Optimization (PPO). Specifically, GRPO works by sampling group of G responses {or, 02, .... oG} for each question q from the old policy model Ta. then computing their corresponding rewards {r1, TG) to derive the ad advantage function A, for response o;:
A Ti meann(ri, F2, .2............. rah)
(1)
sid((ri, (2,,,,,,.... rG))
The current policy model To is then optimized using the following objective function:
I G
To(o, (q) Toms (os1g) Ass
T (0) = E to. 1 Nome 19) (q)
min
G
1=1
To(o: (4)
clip
- 111,-,1 (
x DeL. (Tel (TellTTee)
Th (o:1g)
5

---

Published as a conference paper at ICLR 2026
5.2 REWARD FUNCTION DESIGN
5.2.1 DARKEVENTINTER
For the DarkEventnffer task, we use Qwen2.5-72B (Yang et al., 2024) as the judge to assess the qual- ity of model responses. Given the inherent difficulty in accurately describing black-screed screen events, the judge model is prompted to make three-tier evaluations: fully correct, partially correct but error-free (i.e, incomplete but without introducing incorrect claims), or containing any errors, with rewards of 1, and 0, respectively, formalized in Equation 3.
2, if the answer is fully correct
Durktiventttfer
1, if partially correct and error-free
(3)
0, if the answer contains any errors
5.2.2 MIXVIDQA
For the Mix VixVIdQA task, also employ Qwen2.5-72B the judge. Empirical observations veal that QA tasks are generally less challenging than accurately describing black-screen events. Therefore, two-tier evaluation scheme implemented for Mix VidQA: correct answer receives reward of 1, while an incorrect one receives O.
5.2.3 VIDEOQA
For the conventional VideoQA tasks, particularly multiple-choice questions, selected options are extracted from the model's output using regular expressions and compared with the ground truth. A reward is assigned for correct answers, and for incorrect ones.
1, if the answer is correct
RMixVidQA RvidsoQA
(4)
0, if the answer incorrect
5.2.4 CAPTIONING
In the captioning task, GPT-3.5-Turbo' is utilized as the judge model (analysis can found in Appendix C.I) and the AutoDQ (Wang et al., 2024a) methodology is employed to calculate the event-level recall and precision of mener-gene captions relative to ground-trus -ruth captions. The weighted sum of the two metrics constitutes the AutoDQ reward RAuaaDo:
RAuiaD Recall + Precision
(5)
Here, is weighting factor set to 0.5, which balances the contributions of recall and precision. Given that precision can be enhanced by generating more concise captions, while recall necessitates more comprchensive descriptions of video content, this setting helps equalize their improvement difficulty and mitigates the risk of reward hacking.
Moreover, design keywords reward Reyyward, based rally relevant set and speculation- appculaation-rllated set S, which aims to promote the inclusion of temporal keywords in the generated captions C while discouraging speculative irrelevant content. Exam- ples of these keywords are provided in Appendix B.4.
predefined keyword sets: tempo-
Σ e( if CAWES
WEC
Recywords
Σ I I( ((wET)),)). WEC
(6)
min
otherwise
Here, 7 serves as the upper bound for temporal keyword rewards, empirically set 2, to prevent excessive meaningless temporal keywords. The final caption reward Resption combines both compo- nents through weighted summation:
Reaption RAuiiDo + B -
The weighting coefficient set to 0.2 here, reflecting the secondary role of the keywords reward compared to event recall and precision. Further discussion of hyperparameter choices is provided in Appendix C.2.
openai wtt.//cc//oomeeeeee.. htt.://oor...ppaaaccccccc//ooaaa

---

Published as a conference paper at ICLR 2026
5.2.5 FORMAT REWARD
Additionally, we introduce format reward encourage structured outputs that follow the Reason- Then-Respond paradigm. The reward is formally defined as:
1
valid format
Rormat
(8)
0, invalid format
Unlike existing approaches that often treat format compliance as a separate reward component, we implement it implicitly. Specifically, only responses that conform to the required format are eligible to receive any of the task-specific rewards, as formalized in Equation 9. This design serves crit- ical mechanism to prevent two common forms of reward hacking observed in prior work: (a) correct formats but incorrect answers; and (b) correct answers but incorrect formats. By enforcing format compliance as a prerequisite of task- task-specific rewards, our training framework could accelerate the convergence toward outputs that are both structurally correct and semantically accurate.
Riotll Riormat C DDurkEienllneer + RMixVilQA RvideoQA RCaptionn)
(9)
6 EXPERIMENTS
6.1 EXPERIMENTAL SETTINGS
6.1.1 BENCHMARKS
For QA tasks in general video understanding, we conduct experiments on Video-MME (Fu et al., 2024), Long VideoBench (Wu al., 2024) and MVBench (Li al., 2024a). For video reasoning tasks, we extend experiments on MMVU (Zhao et al., 2025), NExT-QA (Xiao et al., 2021), Inten- IQA (Li al., 2023), Causal-VidQA (Zang al., 2023), Video-Holmes (Cheng et al., 2025), well as our held-out test sets: DarkEventUnfer-Test and Mix BixVidQA-Tess Each of the latter two contains 100 test samples. For video captioning tasks, we evaluate models on DREAM-IK (Wang et al., 2024a) and VidCapBench (Chen et al., 2025f).
6.1.2 BASELINES
First, we evaluate both the direct outputs and the reasoning-elicited responses of Qwen2.5- VL.- 7B-Instruct (Bai et al., 2025) fundamental baselines. For contemporaneous works, conduct comparative analyses with three prominent models: Video-RI (Feng et al., 2025), VideoChat-RI I (Li al., 2025b), and VideoRFT (Wang et al., 2025). Additionally, we perform SFT on Qwen2.5-VL- 7B using our constructed 10k training samples for epochs and include it in the comparison.
6.3.3 IMPLEMENTATION DETAILS
We employ Owwn2.55VL-7BB VL-7B-Instruce as the backbone model for training. To ensure training effi- ciency and stability, uniformly sample 16 frames from each video at the maximum resolution of
For GRPO training, we sample 8 responses per question with a temperature of 1.0 ensure diversity. The learning rate set 1c-6, and the batch size 32. During inference, for QA tasks, video frames are sampled at fps with a maximum of 128 frames per video, while main- taining the same resolution as training. For captioning tasks requiring more details, we uniformly sample 16 frames from each video, which in most cases corresponds to sampling rate exceeding 2 fps. Greedy decoding employed during inference ensure deterministic outputs, and the output length is limited to 2,048. All experiments are conducted on 8 NVIDIA A800 GPUs.
6.2 EXPERIMENTAL RESULTS
We present the performance comparison of VidBridge-RI with baseline models on general video understanding and captioning tasks in Table 1, and on video reasoning tasks in Table 2.
In QA tasks for general video understanding, we observe that explicitly forcing the model to rea- son before answering leads to degradation in performance. This likely attributable to the

---

Published as a conference paper at ICLR 2026
General U'dderstanding Tisks
Captioning Tasks
Model
Reaso- ning
Video-MME
Long Video Bench
MV-
DREAM-IK
VidCapBench Acc/ Pre Cov / Con
Overall
S/M/1.
Bench
FI / Rec Pre
Qwen2.5 VL.
X
59.4
69.0/ 58.8/51D
50.6
58.7
30.9/ 28.3/
12.1/48.5 4.1748.5//B1 5/888/ 4,5
Owen2.5-V1-277
53.4
65.4/5177
38.8
56.3
34.4/ 3
10.6/46.2 /3.88 15.2
Qwen2.5-VL-7B-SFT
x
54.3
64.8 /47.7
43.9
55.6
29.3/ 29.0/
11.9/4822 81.2/ 6.1
Video-RI
58.4
69.0/
148.4 1488
53.3
58.3
31.6/29.4/34
11.7/48.3 /
VideoChat-RI
61.1
71.07 62.4 50.2
52.6
58.3
32.2/ 28.2 / 37.9
10.8 (18//43774.6
74,6/ 17.2
VidcoRFT
62.2
71.8
62.2 52.1
57.4
60.3
31.57 30.4 32.8
12.1 12.1/47/4/8.5
VidBridge-RI (Ours)
64.3
73.0
64.4 55.8
89.3
61.9
35.2/ 32.8 37.9
12.5 49.8 80.6
Table 1: Performance comparison video general understanding and captioning tasks. S, M, denote short, medium, and long, respectively. Rec, Pre, Acc, Cov, and Con abbreviate recall, precision, accuracy, coverage, and conciseness.
Reasoning Tasks
Reaso- ning
Model
Causal- VidQA
Video- Holmes
DarkEvent- Infer-Test
MixVidQA-
MMVU
NEXT-QA
IntentQA
Test
Qwen2.5-VV -
41.9
91.3
63.7
38.0
29.0
Qwen2.5-VL-7D
49.0
77.3
89.9
68.3
35.3
70.0
20.0
Twen2.5-VL-7B-S1E
X
52.3
71.4
84.2
67.1
36.6
80.0
33.0
Video-RI
50.9
79.8
90.9
60.9
36.5
61.0
27.0
VideoChat-R1
51.1
79.6
93.6
68.8
33.0
70.0
15.0
VideoRIF
52.4
94.9
23.0
VidBridge-Rt R1 (Ours)
54.7
97.1
70.7
40.0
117.0
49.0
Table 2: Performance comparison on video reasoning tasks.
relative simplicity of these tasks, where imposing deliberate reasoning on vanilla model may distort its initial correct understanding. However, after training with meticulously curated tasks, VidBridge-RI demonstrates remarkable capabilities, achieving an overall score of 64.3 on Video- MME, with optimal performance across different video lengths. Furthermore, VidBridge-R out- performs the strongest baselines on both Long VideoBench and MVBench by 2.1 % and 1.6%, re- spectively, demonstrating its strong general video understanding capabilities.
Regarding video captioning tasks, find that forcing the model to reason before responding im- proves performance on DREAM-IK but degrades it on VidCapBench. This discrepancy arises be- cause when compelled to reason, the model prioritizes output accuracy, leading to shorter captions that align more closely with the ground-truth captions DREAM-IK and achieving a very high precision score. In contrast, VidCapBench evaluates caption quality by using judge model to answer questions based on model-generated textual captions, where longer captions tend to yield better results. Nevertheless, VidBridge-RI strikes favorable balance between the two benchmarks, ensuring both accuracy and comprehensiveness in its generated video captions.
In video reasoning tasks, VidBridge-RR also demonstrates outstanding performance, surpassing the strongest baseline by an average of 1.9% across five conventional QA tasks. Furthermore, on our held-out testset, which follows the same task format as our designed proxy tasks but employs entirely distinct and unseen data, with evaluation metrics consistent with Equations and 4, VidBridge-Ri achieves significant improvements. This indicates that the model has already developed a dual capa- bility for both the divergent holistic comprehension of video content and the convergent pinpointing of key information, thereby supporting effective performance in both captioning and QA tasks within general and reasoning scenarios.
6.3 ABLATION STUDY ON THE TRAINING TASKS
In Table 3, we perform in-depth analysis of how different training tasks influence model per- formance. When trained exclusively on the captioning task, the model achieves substantial gains on the DREAM-IK benchmark, but performs poorly other tasks. Conversely, training solely on the VideoQA task leads to improvements on conventional QA benchmarks, yet the model still underperforms the captioning task.
8

---

Published as a conference paper at ICLR 2026
Compesition
General Understanding
Reasoning
Caption
Video -QA
DurEEvaa--
MidVid-
Caption
Video- MME
LongVideo- Bench
MV- Bench
MMVU
Intent-
DarkEvent- Infer- Test
MixVid- QA-Test
DREAM-IK
Infer
Pl Rec Pre
58.0
41,9
53.5
50.6
92.5
64.0
16.0
34.8 /3.6// 40.4 / 34,3
63.2
56.4
58.7
53.8
96.4
60.0
24.0
54.8
54.7
52.5
13.0
50.6/
33.7
61.5
56.4
53.2
96.5
46.0
16.0
33.0/28.9/ 38.5
63.4
57.4
59.6
53.3
97.0
113.0
41.0
/4.7/3111 29.3
<<<9
63.5
51.0
28.27 37.9
63.8
58.6
60.1
51.1
97.2
121.0
54.0
32.2 28.3 37.4
60.7
51.4
56.1
51.7
81.6
117.0
$2.0
34,9
/ /2.4/37.8
64.3
59.3
54.7
49.0
35.2 /3.8/ 37.9
Table 3: Ablation study on the training task composition. indicates that we expanded the volume of VideoQA and Caption task in their original proportions match the total data volume used the final training setup with four tasks, thereby ablating the effect of data volume.
However, naively mixing these two tasks, while seemingly reasonable, results in performance degra- dation across both tasks, even with increased data volume (line 4). We attribute this phenomenon conffict in the learning paradigms between QA and captioning under RL, an indirect optimiza- tion method guided by global reward signals. Specifically, QA is a convergent reasoning task that requires the model output unique, low-entropy correct answer from abundant contextual infor- mation. In contrast, captioning is divergent generation task that demands the production of diverse and comprehensive high-entropy descriptions. The opposing task objectives may lead inherent conflicts during RI optimization.
By progressively incorporating our proposed intermediate proxy tasks, DarkEventinfer and Mix VidQA, which are designed to mitigate these issues, the model exhibits substantial performance improvements across all evaluated tasks. Upon further incorporating the captioning task, although reasoning tasks, remarkable improvement
slight performance degradation is observed achieved in captioning. Collectively, these findings demonstrate that FidBridge-RI. through the strategic integration of intermediate proxy tasks, effectively bridges various video understanding tasks, demonstrating exceptional comprehensive capabilities and broad adaptability.
6.4 TRAINING DYNAMICS
As illustrated in Figure 3, VidBridge- R1 exhibits distinct training dynam- ies across different task categories, reflecting the varying cognitive de- mands of each task. For QA tasks in general video understanding (Video- MME, Long VideoBench), the model demonstrates rapid performance im- provement during the initial train- ing phase, which can be attributed to the relatively simpler nature of these tasks, facilitating easier learning. For video reasoning tasks (NExT- QA, MMVU, Causal-VidQA, Dark- EventInfer, Mix VidQA), the model shows steady and progressive performance enhancement, validating that our training framework can consistently activate the model's reasoning capabilities. In the video captioning task (DREAM-IK), the model's performance initially declines before exhibiting oscillatory improvement, suggesting that the integration of reasoning capabilities into caption generation is more intricate compared to QA tasks. Despite these varied training trajectories across tasks, VidBridge-RR ultimately achieves sustained and robust performance gains across all tasks, underscoring the effectiveness of our metic- ulously constructed intermediate proxy tasks in facilitating comprehensive video understanding.
Performincg
Video-MMME
Long VidenHeench
NEXT=QA
MMVU
54
59
2.4k 2.2
2.4 M 2.
2ALLS 7399
Cansal
Fven
MixVidDA
DREAM-IK
114
2. 2c5..k
2c
2 7.3.
Training samples
Figure 3: The training dynamics of VidBridge-RI video general understanding, reasoning, and captioning tasks.

---

Published as a conference paper at ICLR 2026
133
Ocaomod
(a) Qwen2.5-VL and SubOpt
(b) Qwen2.5-VL VL and VidBridge-Re
Figure 4: Distribution of output token entropy across different models on captioning and QA tasks. SubOpt denotes the suboptimal model trained exclusively on conventional QA and captioning tasks. For finer detail, please zoom in.
6.5 ANALYSIS OF THE OUTPUT ENTROPY
To more intuitively illustrate the inherent conflict between the Caption and QA tasks during opti- mization, we present the distribution of output token entropy for both tasks across different models in Figure To mitigate the confounding effects of varying video inputs, all experiments are con- ducted on the Video-MME benchmark. For the QA task, we use the original QA pairs provided in the benchmark; for the captioning task, we prompt each model to generate descriptive captions for the same set of videos.
The results reveal that, for the original Qwen2.5-VV substantial gap exists between the output entropies on the Caption and QA tasks, making simultaneous optimization of these two tasks chal- lenging. As shown Figure 4a, when trained solely with conventional VideoQA and Caption tasks, the resulting suboptimal model still exhibits a significant entropy disparity between the two tasks during the critical generation phase (tokens #60 to #120). Moreover, during the initial generation stage, the entropy of the QA task is adversely influenced by the Caption task, rising to abnormal level comparable to that of the base model on the Caption task. In contrast, when our proposed proxy tasks introduced, depicted in Figure 4b, the entropy gap between the Caption and QA tasks markedly narrowed, thereby enabling more effective joint optimization without compromising task-specific performance.
CONCLUSION
In this work, we identify the challenge of the paradigm conflict between the convergent QA tasks and the divergent captioning tasks during RL for video understanding models. We show that naively combining their reward signals leads to performance degradation in both tasks. To mitigate this is- sue, we introduce two novel intermediate proxy tasks, DarkEventInfer and Mix VidQA, designed to bridge the gap by encouraging the model to simultaneously develop holistic contextual understand- ing and precise information localization capabilities. Building upon this approach, we introduce VidBridge-B R1, the first versatile video reasoning model that reconciles the divergence between QA and captioning tasks during RL training. Extensive experiments demonstrate that VidBridge-RI effectively alleviates the task conflict and achieves significant performance improvements across di- verse general video understanding, reasoning, and captioning benchmarks, highlighting its strong multi-task and generalization ability.
ACKNOWLEDGEMENTS
This work is supported by the National Natural Science Foundation of China (92570204, 62576339, 62236010).
REPRODUCIBILITY STATEMENT
The majority of training and inference details are described in Section 6.1.3. The construction process of the training data is presented in Section 4 and Appendix B.3. In addition, the prompts used for generating training data and for LLM-based evaluation are provided in Appendix B.2.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, al. 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 rl-like reasoning large vision-language models. arXiv preprint arXiv:2504.1 11468, 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: httpps:///gttht wtt.cccm//eepeee Agent/ R1-V, 2025b. Accessed: 2025-02-02.
Mingrui Chen, Haogeng Liu, Hao Liang, Huaibo Huang, Wentao Zhang, and Ran He. Unlocking the potential of difficulty prior in rl-based multimodal reasoning. arXiv preprint arXiv:2505.1322 2025c.
Xinlong Chen, Yue Ding, Weihong Lin, Jingyun Hua, Linli Yao, Yang Shi, Bozhou Li, Yuanx- ing Zhang, Qiang Liu, Pengfei Wan, et al. Avocado: An audiovisual video captioner driven by 2025d.
temporal orchestration. arXiv preprint arXiv:2510.1033
Xinlong Chen, Yuanxing Zhang, Qiang Liu, Junfei Wu, Fuzheng Zhang, and Tieniu Tan. Mixture of decoding: An attention-inspired adaptive decoding strategy to mitigate hallucinations in large vision-language models. arXiv preprint arXiv:2505. 70661, 2025e.
Xinlong Chen, Yuanxing Zhang, Chongling Rao, Yushuo Guan, Jiaheng Liu, Fuzheng Zhang, Chen- gru Song, Qiang Liu, Di Zhang, and Tieniu Tan. Vidcaphench: A comprehensive benchmark of video captioning for controllable text-to-video generation. arXiv preprint 2XXv:25022112822 2025f.
Xinlong Chen, Weihong Lin, Jingyun Hua, Linli Yao, Yue Ding, Bozhou Li, Bohan Zeng, Yang Shi, Qiang Liu, Yuamxing Zhang, et al. Diadem: Advancing dialogue descriptions in audiovisual video captioning for multimodal large language models. arXiv preprint arXiv:2601.19267. 2026.
Yi Chen, Yuying Ge, Rui Wang, Yixiao Ge, Junhao Cheng, Ying Shan, and Xihui Liu. Grpo- care: Consistency-awart reinforcement learning for multimodal reasoning. arXiv preprint arXiv:2506. 2025g.
Yukang Chen, Fuzhao Xue, Dacheng Li, Qinghao Hu, Ligeng Zhu, Xiuyu Li, Yunhao Fang, Haotian Tang, Shang Yang, Zhijian Liu, et al. Longvila: Scaling long-context visual language models for long videos. arXiv preprint arXiv:2408. 200224008......... 2024a.
Yukang Chen, Wei Huang, Baifeng Shi, Qinghao Hu, Hanrong Ye, Ligeng Zhu, Zhijian Liu, Pavlo Molchanov, Jan Kautz, Xiaojuan Qi, et al. Scaling to long videos. arXiv preprint arXiv:2507.07966, 2025h.
Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. Proceedings of the IEEE/CVE conference on computer 2024b.
vision and pattern recognition, pp.
Junhao Cheng, Yuying Ge, Teng Wang, Yixiao Ge, Jing Liao, and Ying Shan. Video-holmes: Can milm think like holmes for complex video reasoning? arXiv preprint arXiv:2505.21374, 2025.
Jisheng Dang, Jingze Wu, Teng Wang, Xuanhui Lin, Nannan Zhu, Hongbo Chen, Wei-Shi Zheng, Meng Wang, and Tat-Seng Chua. Reinforcing video reasoning with focused thinking. arXiv preprint Stt::2505.24715 2025.
Yihe Deng, Hritik Bansal, Fan Yin, Nanyun Peng, Wei Wang, and Kai- Wei Chang. Openvlthinker: An early exploration to complex vision- lasion-language reasoning via iterative selfoiimprveeent.. arXiv preprint ar arXiv:2503. 17352, 2025.
11

---

Published as a conference paper at ICLR 2026
Kaituo Feng, Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang. Tianshuo Peng, Benyou Wang, and Xiangyu Yue. Video-rl: Reinforcing video reasoning in mllms. arXiv preprint arXiv:2503.21776. 2025.
Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evalua- tion benchmark of multi-modal Ilms in video analysis. arXiv preprint arXiv:2405.21075 2024.
Google Gemini Team, Gemini 2.5:
Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities, 2025. URL abs 3bbs/2007.05
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong Wentao Zhang, Guanting Chen, Xiao Bi, Yu Wu, YK Li, et al. Deepseek-coders When the large language model meets programming- the rise of code intelligence. arXiv preprint arXiv:2401.4411 2024.
Daya Guo, Dejian Yang. Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-rl: Incentivizing reasoning capability in Ilms via reinforcement learning. arXiv preprini arXiv:2501.129%1 2025.
Wenyi Hong, Weihan Wang, Ming Ding, Wenmeng Yu, Qingsong Lv, Yan Wang, Yean Cheng, Shiyu Huang, Junhui Ji, Zhao Xue, et al. Cogvlm2: Visual language models for image and video understanding. arXiv preprint urXiv:2408. 16500, 2024.
Daili Hua, Xizhi Wang, Bohan Zeng, Xinyi Huang, Hao Liang, Junbo Niu, Xinlong Chen, Quanqing Xu, and Wentao Zhang. Vabench: A comprehensive benchmark for audio-video generation. arXiv preprint arXiv:2512.09299, 2025.
Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Zhe Xu, Yao Hu, and Shaohui Lin. Vision-rl: Incentivizing reasoning capability in multimodal large language models. arXiv preprint arXhv:2503.06749) 2025.
Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. Openai ol system card. arXiv preprint arXiv:2412. 2024.
Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings the IEEE/CVF conference computer vision and pattern recognition, pp. 4401-4410, 2019.
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijaya- narasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1005.06950, 2017.
Bozhou Li, Xinda Xue, Sihan Yang. Yang Shi, Xinlong Chen, Yushuo Guan, Yuanxing Zhang, and Wentao Zhang. The unseen bias: How norm discrepancy in pre-norm milms leads to visual information loss. arXiv preprint wwxiv:25/2.08377. 2025a.
Jiapeng Li. Ping Wei, Wenjuan Han, and Lifeng Fan. Intentqa: Context-aware video intent reason- ing. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 11963- 11974, 2023.
Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mybench: A comprehensive multi-moda video understanding benchmark. In Proceedings of the IEEF/VVF Conference on Computer Vision and Pattern Recognition, pp- 22195-22206, 2024a.
Xinhao Li. Ziang Yan, Desen Meng, Lu Dong, Xiangyu Zeng. Yinan He, Yali Wang. Yu Qiao, Yi Wang, and Limin Wang. Videochat-rl: Enhancing spatio-temporal perception via reinforce- ment fine-tuning. arXiv preprint arXiv:2504. 06958, 2025b.
Yanwei Li, Chengyao Wang, and Jiyy Jia. Llama-vid: An image is worth 2 tokens in large language models. In European Conference on Computer Vision, pp. 323-340. Springer, 2024b.
12

---

Published as a conference paper at ICLR 2026
Yunxin Li, Xinyu Chen, Zitao Li, Zhenyu Liu, Longyue Wang, Wenhan Luo, Baotian Hu, and Min Zhang. Veripo: Cultivating long reasoning video-llms via verifier- guddie iterative policy optimization. arXiv preprint arXiv:2505.19000 2025c.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36:34892-349113 2023.
Qiang Liu, Xinlong Chen, Yue Ding, Bowen Song, Weiqiang Wang, Shu Wu, and Liang Wang- inrention-guider self-reflection for zero-shot hallucination detection in large language models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 21016-21032, 2025a.
Yuqi Liu, Bohao Peng, Zhisheng Zhong, Zihao Yue, Fanbin Lu, Bei Yu, and Jiaya Jia. Seg- zero: Reasoning-chain guided segmentation via cognitive reinforcement. arXiv preprint arXiv:2503.06520, 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 WxXiv:2503.0/785 2025c.
Yiting Lu, Xin Li, Yajing Pei, Kun Yuan, Qizhi Xie. Yunpeng Qu Ming Sun, Chao Zhou, and
Zhibo Chen. Kvq: Kwai video quality assessment for short-form videos. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 25963-25973 2024.
Desen Meng, Rui Huang, Zhilin Dai, Xinhao Li, Yifan Xu, Jun Zhang, Zhenpeng Huang, Meng Zhang, Lingshu Zhang, Yi Liu, et al. Vidcocap-rl: Enhancing milms for video captioning via structured thinking. arXiv preprint arXiv:2506.00 0/755, 2025a.
Fanqing Meng, Lingxiao Du, Zongkai Liu, Zhixiang Zhou, Quanfeng Lu, Daocheng Fu, Botian Shi, Wenhai Wang, Junjun Hc, Kaipeng Zhang, et al. Mm-eureka: Exploring visual aha moment with rule-based large-scale reinforcement learning. CoRR, 2025b.
Yi Peng. Xiaokun Wang, Yichen Wei, Jiangbo Pei, Weijie Qiu, Ai Jian, Yunzhuo Hao, Jiachun Pan, Tianyidan Xie, Li Ge, et al. Skywork rlv: Pioneering multimodal reasoning with chain-of- thought. arXiv preprint ar XrXvv22044.05599 2025a.
Yingzhe Peng, Gongrui Zhang, Miaosen Zhang, Zhiyuan You, Jie Liu, Qipeng Zhu, Kai Yang, Xingzhong Xu, Xin Geng, and Xu Yang. Lmm-rl: Empowering 3b Imms with strong reasoning abilities through two-stage rule-based rl. arXiv preprint 2r03h:2503.07535 2025b.
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 arXiv:2402.03330 2024.
Haozhan Shen, Peng Liu, Jingcheng Li, Chunxin Fang. Yibo Ma, Jiajia Liao, Qiaoli Shen, Zilun Zhang, Kangjia Zhao, Qianqian Zhang, et al. Vlm-rl: A stable and generalizable rl-style large vision-language model. arXiv preprint arXiv:2504.0700
2025.
Yang Shi, Yuhao Dong, Yue Ding, Yuran Wang, Xuanyu Zhu, Sheng Zhou, Wenting Liu, Haochen Tian, Rundong Wang, Huanqian Wang, al. Realunify: Do unified models truly benefit from unification? comprehensive benchmark. arXiv preprint arXiv:2509. 24897, 2025a.
Yang Shi, Jiaheng Liu, Yushuo Guan, Zhenhua Wu, Yuanxing Zhang, Zihao Wang, Weihong Lin, Jingyun Hua, Zekun Wang. Xinlong Chen, et al. Mavors: Multi-granulaan video representation for multimodal large language model. arXiv preprint arXiv:2504. 10068, 2025b.
Yang Shi, Huanqian Wang, Wulin Xie, Huanyao Zhang, Lijie Zhao Yi-Fan Zhang, Xinfeng Li, Chaoyou Fu, Zhuoer Wen. Wenting Liu, et al. Mme-videoocr: Evaluating ber-based capabilities of multimodal Ilms in video scenarios. arXiv preprint arXiv:2505.2 2133, 2025c.
Yan Shu, Zheng Liu, Peitian Zhang, Minghao Qin, Junjie Zhou, Zhengyang Liang, Tiejun Huang, and Bo Zhao. Video-xl: Extra-long vision language model for hour-scale video understanding. arXiv preprint 2409 10440994440 /4485, 2024.
13

---

Published as a conference paper at ICLR 2026
Huajie Tan, Yuheng Ji, Xiaoshuai Hao, Minglan Lin, Pengwei Wang. Zhongyuan Wang, and Shanghang Zhang. Reason-rft: Reinforcement fine-tuning for visual reasoning. arXiv preprint arXiv:2503.20752. 2025.
Yansong Tang, Dajun Ding. Yongming Rao, Yu Zheng, Danyang Zhang. Lili Zhao, Jiwen Lu, and Jie Zhou. Coin: large-scale dataset for comprchensive instructional video analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1207-1216, 2019.
Kimi Team, Angang Du, Bohong Yin, Bowci Xing, Bowen Qu, Bowen Wang, Cheng Chen, Chenlin Zhang, Chenzhuang Du, Chu Wei, et al. Kimi-vl technical report. arXiv preprint arXiv:2504.07491, 2025.
Jiawei Wang, Liping Yuan, Yuchen Zhang, and Haomiao Sun. Tarsier: Recipes for training and evaluating large video description models. arXiv preprint arXiv:2407.00634 2024a.
Peng Wang. Shuai Bai, Sinan Tan, Shijie Wang. Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision- vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409. 121911 2024b.
Qi Wang, Yanrui Yu, Ye Yuan, Rui Mao, and Tianfei Zhou. Videorft: Incentivizing video reasoning capability in milms via reinforced fine-tuning, arXiv preprint arXiv:2505. 12434, 2025.
Qiuheng Wang, Yukai Shi, Jiarong Ou, Rui Chen, Ke Lin, Jiahao Wang. Boyuan Jiang, Haotian Yang. Mingwu Zheng, Xin Tao, et al. Koala-36m: A large-scale video dataset improving con- sistency between fine-grained conditions and video content. arXiv preprint 10X1v:2410.08260, 2024c.
Xidong Wang, Dingjie Song, Shunian Chen, Chen Zhang, and Benyou Wang. Longllava: Scal- ing multi-modal 1lms to 1000 images efficiently via hybrid architecture. arXiv preprint arXiv:2409.02888 2024d. 2024d.
Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li. Longvideobench: A benchmark for long-contex interleaved video-language understanding. Advances in Neural Information Processing Systems, 37:28828-28881 2024.
Jiaer Xia, Yuhang Zang, Peng Gao, Yixuan Li, and Kaiyang Zhou. Visionary-rl: Mitigating short- cuts in visual reasoning with reinforcement learning, arXiv preprint arXiv:2505.14677 2025.
Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question- answering to explaining temporal actions. In Proceedings of the IEEE/CVF conference com- puter vision and pattern recognition, pp. 9777-9786, 2021.
An Yang, Baosong Yang. Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2. technical report. arXiv e-prints, pp- arXiv-2412, 2024.
Yi Yang, Xiaoxuan He, Hongkun Pan, Xiyan Jiang, Yan Deng, Xingtao Yang, Haoyu Lu, Dacheng Yin, Fengyun Rao, Minfeng Zhu, et R1-onevision: Advancing generalized multimodal rea- soning through cooss formalization. arXiv preprint arXiv:2503. (00.:2553..66.... 2025.
Huanjin Yao, Qixiang Yin, Jingyi Zhang, Min Yang, Yibo Wang, Wenhao Wu, Fei Su, Li Shen, Minghui Qiu, Dacheng Tao, et al. R1-sharevl: Incentivizing reasoning capability of multimodal large language models via share-grpo. arXiv preprint arXiv:2505.16673 2025.
Liping Yuan, Jiawei Wang. Haomiao Sun, Yuchen Zhang, and Yuan Lin. Tarsier2: Advancing large vision-language models from detailed video description to comprehensive video understanding. arXiv preprint arXiv:2501.07880 2025.
Chuanqi Zang. Hanqing Wang, Mingtao Pei, and Wei Liang. Discovering the real association: Multimodal causal reasoning in video question answering. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 19027-19036. 2023.
14

---

Published as a conference paper at ICLR 2026
Jiajun Zhang, Jianke Zhang, Zeyu Cui, Jiaxi Yang, Lei Zhang, Binyuan Hui, Qiang Liu, Zilei Wang, Liang Wang, and Junyang Lin. Plotcraft: Pushing the limits of Ilms for complex and interactive data visualization. arXiv preprint arXiv:2511.00010. 2025a.
Jiajun Zhang, Zeyu Cui, Jiaxi Yang, Lei Zhang, Yuheng Jing, Zeyao Ma, Tianyi Bai, Zilei Wang, Qiang Liu, Liang Wang, et al. From completion to editing: Unlocking context-aware code infilling via search-and-ree and-replace instruction tuning. arXiv preprint arXiv:2601. 13384, 2026.
Jingyi Zhang, Jiaxing Huang, Huanjin Yao. Shunyu Liu, Xikun Zhang, Shijian Lu, and Dacheng Tao. R1-vI: Learning to reason with multimodal large language models via step-wise group relative policy optimization. arXiv preprint arXiv:2503. 12937, 2025b.
Xingjian Zhang, Siwei Wen, Wenjun Wu, and Lei Huang. Tinyllaa-viddeorl: Towards smaller Imms for video reasoning. arXiv preprint arXiv:2504.09641, 2025c.
Yilun Zhao, Lujing Xie, Haowei Zhang, Guo Gan, Yitao Long, Zhiyuan Hu, Tongyan Hu, Weiyuan Chen, Chuhan Li, Junyang Song, et al. Mmvu: Mcasuring expert-level multi-discipline video understanding. arXiv preprint arXiv:2501.12380 2025.
15

---

Published as a conference paper at ICLR 2026
A THE USE OF LLMS
Throughout the coding and debugging stages, we leveraged LLMs for technical guidance. Follow- ing the collaborative drafting of the manuscript, we again engaged LLMs to polish and refine its language and overall expression.
B ADDITIONAL EXPERIMENTAL DETAILS
B.1 DETAILS BENCHMARKS
To comprehensively evaluate the model performance, we selected representative benchmarks to as- sess the model's capabilities in general video understanding, video reasoning, and video captioning, respectively.
For general video understanding, we conduct experiments on Video-MME (Fu et al., 2024), LongVideoBench (Wu al., 2024) and MVBench (Li et al., 2024a).
Video-MME is comprehensive benchmark for evaluating MLLMs across diverse video types and temporal lengths. It features 900 manually annotated videos spanning 254 hours and 2,700 QA pairs, offering a rigorous test of models' general understanding ability. We evaluate Video- MME without subtitles in our experiments.
LongVideoBench is designed to evaluate the long-form multimodal perception and relation capa- bility of MLLMs. It includes 3,763 web-collected vidcos spanning various lengths and themes and 6,678 human-annotated multiple-choice questions, distributed across 17 ine-grained categories, which assess different aspects of video-language understanding.
Bench is designed evaluate the temporal understanding capabilities of MLLMS through 20 challenging video tasks that go beyond static image reasoning. By systematically transforming static tasks into dynamic ones, covers a wide range of temporal skills and ensures fair evaluation using ground-tree truth annotations converted into multiple-choice questions.
For video reasoning tasks, we conduct experiments on MMVU (Zhao et al., 2025), NExT-QA (Xiao al., 2021), IntentQA (Li et al., 2023), Causal-VidQA (Zang et al., 2023), Video-Holmes (Cheng et al., 2025), and our held-out test set DarkEventlnfer-Tee and Mix MixVidQA-Test.
MMVU is designed to evaluate the expert-level video reasoning ability of MLLMs. It contains 3,000 expert-annotated questions over 1,529 videos, which span 27 subjects from four disci- plines: Science, Healthcare, Humanities & Social Sciences, and Engineering.
NEXT-QA tests the model's reasoning ability over causal, temporal, and descriptive question types. In our experiments, we used the validation split containing 4,996 video-question pairs with five answer options.
IntentQA contains 4,303 videos and 16k multiple-choice QA pairs focused on reasoning about people's 's intent in the video. We perform zero-shot evaluation on the test set containing 2k questions.
Causal-VidQA requires the model to answer questions including scene description, evidence rea- soning, and commonsense reasoning. Moreover, for the commonsense reasoning questions, the model is required to not only provide right answer but also offer proper reason justifying why that answer is true. We present the average results across all categorics.
Video-Holmes evaluates complex reasoning capabilities through 1,837 QA pairs requiring strong reasoning skills, with a focus on suspenseful short films.
test sets randomly sampled from constructed video reasoning dataset, each containing 100 samples. They are designed to evaluate the model's capability of performing contextual reasoning across video sequences and distinguishing between different video sources when answering questions, respectively.
DarkEventInfer-Tees and MixiiAA-Tees
For video captioning tasks, we conduct experiments on DREAM-IK (Wang et al., 2024a) and Vid- CapBench (Chen al., 2025f).
16

---

Published as a conference paper at ICLR 2026
Prompts for eliciting model reasoning
(original prompts} + "Output the thinking process in <think> </think> and final an- swer in <answer> </ </answer> tags, l.e., <think> reasoning process here </think> <answer> answer here </answer>."
Figure 5: Prompts for eliciting model reasoning
Prompts to generate QA pairs in MixVidQA
Please create ten question-answer (QA) pairs that require reasoning across multiple segments of the video content. Avoid using terms like "frames" in your QA generation.
Guidelines:
1. **Provide clear and specific references in each QA pair,** ** In your generated questions, AVOID ambiguous pronouns like "the video", "the background", "the scene", "the person", "the environment", "the man". "the woman", ctc. Each QA pair should stand independently with explicit references.
The questions must integrate information from multiple segments the video.
3. Ensure the questions involve complex reasoning. such as deducing outcomes based on the
events or details presented specific segments.
Avoid creating questions that can be answered without watching the video.
5. Ensure each question has a clear, unambiguous, and definitive answer. Do not generate
questions with uncertain or speculative answers.
6. Exclude subjective questions or those involving keywords such as emotional, spiritual, contribution, importance, or implication.
7. Do not generate true/false or yes/no questions. Format the QA pairs in the following JSON structure: [("question": "xxx", "answer":
"xxx")....
Figure Prompts to generate QA pairs in Mix VidQA.
DREAM-IK is a challenging benchmark for detailed video description, featuring 1,000 clips from diverse sources such as films, stock footage, and short-form videos. Each video is paired with fine-grained human-annotated descriptions, and evaluated using AutoDQ, metric better suited for assessing rich, multi-event narratives than traditional captioning scores.
VidCapBench comprises 643 videos designed to evaluate video captions from the perspective of their utility for text-to-video generation. Unlike DREAM-IK, which provides ground truth captions. VidCapBench assesses caption quality by inputting both the captions and carefully de- signed evaluation questions into a judge model. The quality of the captions then determined by the accuracy of the judge model's responses to these questions.
B.2 DETAILS PROMPTS
B.2.1 PROMPTS TO ELICIT REASONING
In Figure 5, present the prompts that used elicit the model reasoning before response.
B.2.2 PROMPTS TO GENERATE QA PAIRS IN MIXVIDQA
In Figure 6, present the prompts that used to generate the initial QA pairs for Mix ViXVAA
B.2.3 PROMPTS TO EVALUATE DARKEVENTINFER
In Figure 7, present the prompts that used evaluate model response in Evennfftt
17

---

Published as a conference paper at ICLR 2026
Prompts to evaluate model response in DarkEventlnfer
You are an evaluator tasked with determining if a given response matches the Ground Truth (GT) provided. Your job is to compare the response and GT carefully and return value based on their consistency.
Instructions:
1. Read the Response and GT carefully: Ensure you understand both the response and the
GT completely.
2. Evaluate the Consistency:
*Score 2: If the response semantically covers the GT entirely, even if the response is longer.
*Score If the response partially covers the GT, but does not fully encompass it. *Score O: If the response is entirely different and irrelevant from the GT or the response is
None.
Response:
(model response)
GT: {ground )ruth)
Your judgment:
Figure 7: Prompts to evaluate model response DarkEventlnfer.
Prompts evaluate model response in VidOA
Given question along with its ground truth and generated answer, please judge whether the generated answer is True or False. If the ground truth or the generated answer is ambiguous, consider it False
Question: {question}
Groung truth: (ground truth) Generated answer: (model response)
Your judgment:
Figure 8: Prompts to evaluate model response in Mix
B.2.4 PROMPTS TO EVALUATE MIXVIDQA
In Figure 8, we present the prompts evaluate model response in ViXVAA
B.3 DETAILS OF THE DATA CURATION
This subsection delineates the construction pipelines for DarkEventInfer and MixVidQA, illus- trated in Figure 9.
DarkEventlnfer presents the model with video containing black- segments and requires to infer the events that occur during the masked periods based on contextual cues. Based on the event captions and their corresponding timestamp annotations from COIN (Tang et al., 2019), we randomly select
event per video (c.g., Event 4 in Figure 9) and add black-screen mask to it. The model is then asked to reason about and predict the masked event based on the contextual information from the unmasked segments.
The Mix VidQA task is designed to present the model with interleaved video clips from two distinct sources and require it to answer questions based on the most relevant video source while ignoring the other. Specifically, we source videos from Kinetics (Kay et al., 2017), each lasting around 10 seconds, which rich in actions. To construct the mixed sequences, two videos randomly
18

---

Published as a conference paper at ICLR 2026
DarkEfentnfer
Original video
Prompt:
screen segment imeenddd to cover up specific event Infor the event that poobably heing hidden by the black
Event
Event
Event
Event4
Event 5
Masked video
Add mask
Ground truth:
The black screen was likely obseuring the prscess of seasoning the fish with crucial
proparing cooking
MixVidQA
Prompe:
You will receive video with alternating segments sources, is well question tidd to one of these sources. Anewer the que- siion lased snlely the relevant video
soparate
Clip
clips Eg
Question: What boy's focus dur- ing the vileo??
Geound truth:
bay appears ging and stacking cups.
Clip A-1
Clip B-1
Clip A-2
Clip D-2
Clip A-3
Clip D-3
Figure 9: Details the data curation of DarkEventlnfer and MixVidQA.
selected and divided into clips with durations ranging from 1.5 to seconds, which are then inter- leaved to form a new composite video. For each mixed video, we generate a set QA pairs using Qwen2-VL-72B (Wang al., 2024b), where each question explicitly refers videos. The model is expected to identify the relevant video clips to provide the correct answer based on the mixed video sequences.
of the original
For the above data, we have conducted manual verification of all annotations. During this process, we corrected inaccuracies ambiguities in the automatically generated annotations and removed instances that were excessively challenging or ambiguous even for human annotators. This ensures that the final datasets maintain high quality and are suitable for effective model training.
B.4 DETAILS OF THE KEYWORDS REWARD
In Equation define the keyword reward by introducing the time-series-related keyword set T and the speculation-related keyword set S, which guide the model to generate captions that are both temporally coherent and free from subjective speculation. In Figure 10, illustrate examples of the keywords in these two sets specifically.
Examples of the keywords set in keywords reward
cemporally-relevant set T:
("start with", "then", "next", "after", "begin with", "followed by", "following", "subse- quently", "initially", "first", "second", "finall"", "lastly",
Speculation-related set
("possibly". "likely", "appears to", "seems to", "might", "may", "potentially", "probably", "implying". "perhaps", "presumably"..
Figure 10: Illustrative examples of the keyword sets utilized in the keywords reward.
19

---

Published as a conference paper at ICLR 2026
C ADDITIONAL EXPERIMENTAL ANALYSIS
C.I ABLATION ON THE JUDGE MODEL FOR CAPTIONING TASK
In the main text, we employ GPT-3.5-TTubb as the judge model for the captioning task. To evaluate the impact of different judge models on the training process, we present the exper- imental results obtained when using GPT-4.12 as the judge model in Table 4.
DREAM-1K F1 Roe Pree
VidCapBench
Judgee Model
Acc/Pre Cov
Tirbo GPT-4.1
34.8 30.6 / 40.4
13.0/47.8 80.8 13.6 13.3 / 49.5 79.2/ 8.7
34.9/
Table 4: Ablation on the judge model for the cap- tioning task.
The experimental results indicate that the per- formance of using GPT-3.5-Turbe and GPT-4.1 as judge models is quite close. This suggests that the performance improvement achieved by our method primarily stems from the effectiveness of our training strategy and the high-quality training data. As long the judge model possesses sufficient capability for evaluation, the experimental results are not critically sensitive to the specific choice of the judge model.
We opt to use OpenAI's API service for judgement in the captioning task, rather than deploying local Qwen model done in other tasks, because the AutoDQ (Wang et al., 2024a) evaluation method involves frequent calls to the judge model. If implemented locally, this would significantly reduce training efficiency and lead to an unacceptable increase in training time. In comparison, utilizing OpenAl's ss API greatly enhances training efficiency. Therefore, considering both computa- tional efficiency and economic cost, we ultimately select GPT-3.5- Turbo as the judge model for the captioning task.
C.2 ANALYSIS OF THE CAPTION REWARD
In the design of the caption reward, several crucial strategies have been implemented to avert undesirable be-
Coefficient and (3 the Caption Reward
DREAM-IK FI Rec / Pre
VidCapBench Pre Cov / Con
haviors and enhance the overall qual-
1.0, 8 (reward hacking)
37.0/ 29.9 48.6
10.3/44./76.2 12. 12.2/47.1 80.4 12.2
ity of generated captions. To pre- vent the model from hacking the pre- cision reward by generating overly
0.5, 0 (wll weyds))))
32.9/30.6 30.6 35.6
0.5.3 = 0.2 (Ours)
/ 47.8/ 80.8 13.6
brief captions, we set the coefficient of the precision term in Equation 5
Table 5: Analysis on the caption reward.
0.5. Additionally, promote temporally coherent caption generation and suppress speculative or irrelevant content unrelated to the video, we incorporate the keywords reward as defined in Equa- tion 6. comprehensively validate the effectiveness of our reward design, we conduct ablation studies by training models solely on the captioning data, with their respective performances summa- rized in Table 5.
When is set to 1, equal to the weight of the recall term, the model achieves notable increase in precision by producing extremely concise captions. However, this improvement is largely superfi- cial, as it comes at the cost of reduced descriptive richness and contextual coverage. Moreover, the model performs significantly worse on the Qa-bdsed captioning benchmark VidCapBench, indicat- ing degradation in overall caption quality. Therefore, is essential to reduce the weight of the precision reward to avoid reward hacking.
In the absence of the keywords reward, the model's performance deteriorates compared to when the reward is included. This indicates that the keywords reward plays a pivotal role in guiding the model generate temporally structured and semantically grounded captions, thereby substantially improving performance on the video captioning task.
hhttps://openal wttpe/ennni...ciccccoeeeeee coom/indx//ppt--.
20

---

Published as a conference paper at ICLR 2026
Coeflicient y of KL Divergence
General Understanding Tisks
Reasoning Tasks
Caption Tasks
Long Vide-- Bench
DarkEvent- Infer-Tese
MixVid- QA-Test
DREAM-IK Fl Rec/ Pre
Video-MME
MVBench
NEXT-QA
IntentQA
64.3
117.0
36.2/328/373
0.05
63.5,a8)
56 2,.2,
_7.9.4.) 65.0
81.3,a3: 80.8
9..0ca. 95.7(a14
91,0,as)
39.0
34.9/30. / 40.5
0.10
62.6(-17)
54.1
85.0,az)
24.0
34.7 /0.6/ 40.1
Table 6: Ablation study on the coefficient of KL divergence.
General Understanding Tasks
Reasoning Tasks
Caption Tasks
Format Reward
LongViden- Bench
DarkEvent- Infer-Test
MixVid- QA-Test
DREAM-IK
Video-MME
MVBench
NEXT-QA
IntentQA
/ Pre
None Explicit
64.1
57.8
58.2
81.2
96.2
110.0
47.0
33.1/31.335.0
63.5
58.2
59.4
80.3
96.7
112.0
49.0
33.8/31.2 36.8
Implicit
64.3
59.3
61.9
81.6
97.1
117.0
49.0
35.2/ 32.8 37.9
Table 7: Ablation study on the format reward.
C.3 ABLATION STUDY ON THE KL DIVERGENCE
In the objective function of the GRPO algorithm (Equation 2), the KL divergence constraint term introduced to limit the deviation between the current policy model and the previous policy model, therehy ensuring the stability of the training process. However, during our training, we observed that discarding the KL divergence constraint would be preferable option when aiming to more rapidly stimulate the model's reasoning capabilities. Table presents our ablation experiments on the coefficient of the KL divergence constraint, demonstrating that stronger constraints lead to slower model convergence, ultimately impairing final performance. Consequently, when using fixed number of training samples, omitting the KL. divergene constraint better facilitates the model's reasoning abilities and yields optimal performance.
C.4 ABLATION STUDY ON THE FORMAT REWARD
During training, we adopt an implicit format reward, as defined in Equation 9. This mechanism ensures that the models are eligible to receive any of the task-specific rewards only when its output adheres to the required format. To evaluate the effectiveness of this approach, we compare two alternatives in Table 7: one without format reward (denoted as "None"), and another with explicit format reward, where the format reward is incorporated as separate term into the overall reward function alongside the task-specific reward.
Experimental results indicate that explicitly incorporating the format reward leads to slight per- formance improvement compared to the setting without format reward in most scenarios. However, both approaches are outperformed by the implicit format reward mechanism. The underlying reason that the implicit format reward guides the model to gradually learn the correct output format while generating accurate answers, thereby preventing the model from converging to suboptimal solutions that either satisfy only the format constraints or focus solely on answer correctness, promoting more balanced and effective training.
C.5 ANALYSIS OF VIDBRIDGE-RI RI'S REASONING PATTERNS
Figure 11 presents illustrative examples of VidBridge-R1 's reasoning patterns across both VideoQA and captioning tasks. In the VideoQA task, VidBridge-Ro R1 effectively integrates the analysis of the video content with logical inference throughout its reasoning process, ultimately arriving at the correct answer. In the captioning task, VidBridge-RI initially provides fragmented observations of the characters and events depicted in the video during the reasoning process. These partial insights then synthesized into comprehensive and coherent video caption in the final answer. It is noteworthy that these reasoning patterns emerge naturally from the model itself, rather than being forcibly imitated through SFT, further demonstrating the effectiveness of our training framework.
21

---

Published as a conference paper at ICLR 2026
VideoQA
Captioning
Prompt:
the video electtical energy the capacitor is conversed into light
energy
Prompt: Prov vle detailed yct concise doscription of the video.
energe of the bulb.
VidlBridg-RR:: <think> The video begine with a wide shee of nighttime trfan scenc, festuring unimated characters
B. The electrical energy stored the battery converted into electrical enurgy in the caparitor and light energy and heat loss the baib, v/ enengy elcerical enery of battery.
ground croswall,, The background inchides buildings sany The chamesers are mitially motionless chameter statss to move, siting up and looking sround. Another veticlo, green SUV, then enters the friem from the left side, driving over the clamatoon The scese miintiins a corsistent nighttime sctting with the same background clements throuhhut <think-
illuminsned
capacitor is
into
D. The clettiistl energy stered in the heatety is converte into
<anwwer> The video
with wide nighttime whan showing animuted charaeters croswall,,
clectrical energ and hest loss the capacitor.
character sitting and looking around, followed by entry of green foo lell, driving over the two churactess, all cointaiiing the same background illumanated buildings and a stury sky. slusswer
ground
The electiial
converted
electrical enery
buttery.
VidBridge-Re R1: <hhnk- The video shows a circuit with batter, a capacitor, and light bulb Jottllyy the battery provides electiccal energy to charge the cspacitor Onee clureed the capacitor stones this electricd mergy. When the woitc elosed, the espacior dischangs thruug the light bulh, convening it stoed clamnial enenyy into light enery and came hest energy dug to resistance the ciscuit This process a typical demonstration energy canvensio electical circui <thiink energy stored
Sanewar-B.
clectical energy the and enagg
Figure 1I: Illustrative examples showcasing the reasoning patterns of VidBridge-RI. In VideoQA tasks, the keywords indicating video content descriptions appear in italics, while reasoning steps are underlined. In captioning tasks, corresponding elements in the reasoning process and the final answer are marked with the same color.
D FUTURE WORK
Hallucination remains a critical challenge that substantially undermines the credibility of the model (Liu et al., 2025a; Chen et al., 2025e; et al., 2025a). Future work will explore effective strategies for detecting and mitigating hallucinations in both the chain-of-thouuht reasoning process and the final outputs generated by VidBridge-R1, with the goal of further improving the model's reliability and trustworthiness.
E ATTEMPTS OF SFT COLD-START BEFORE RL
Some recent studies (Feng et al., 2025; Zhang et al., 2025b; Peng et al., 2025a; Yang et al., 2025) suggest that employing SFT as a cold-start strategy to teach the model reasoning pattern before RL, can effectively improve its reasoning capabilities. In this section, we aim to evaluate the effective- ness of this approach within our proxy task framework by constructing reasoning chains for the tasks presented in the main text and conducting experiments in which the model is first initialized through SFT, followed by RL-based training.
E. CURATION OF THE REASONING CHAINS
To avoid data overlap between the SFT and RL phases, we utilize the data filtered out in the main text due to high similarity among model responses for the SFT phase, while maintaining consistency with the data used in the RL phase as per the main text. In the following, we will detail the construction methods of the reasoning chains for SFT across all tasks, with the workflow illustrated in Figure 12.
E.1.1 CONSTRUCTION OF REASONING CHAINS FOR QA TASKS
For the QA tasks, including DarkFventlnfer, MixVidQA, and conventional VideoQA, we employ the following methodology construct the reasoning chains used in the SFT phase.
First, we feed each video along with its corresponding query and ground truth into the Qwen2-VL- 72B. The model is instructed to generate video caption tailored to the query and ground truth, accompanied by a reasoning process and a final conclusion. The generated captions, reasoning steps, and conclusions are then fed into Qwen2-72B, which filters out samples where the reasoning or conclusion is logically inconsistent with the caption, ensuring that only those captions containing sufficient and relevant information to support the reasoning and conclusion retained.
22

---

Published as a conference paper at ICLR 2026
Caption:
Thinking process:
Lo vilo shore koob aal slecpy per asand white soce
bay
Olay las iec The qustie a aikin any ond kaone the Ex alees demertzade onken
rifer dosrpiin resticee idetifiation
with yothre visble on ar At poit, under stoqy
Question:
QwQ
rightt li ou tigs fave orlinas, So optiss sas "ydles big lags dwy'' cat" wan adoas bauass or ase connee deporiccs weneb deficin
Hin deo end lower the the chorp an deensiicaas?
eubessie raile thak mas bekesbon Dr rugle not Deresiiond sonols naght aud cakeaine So the carrec strver i.A.
thore
poreetios
soo
S Qwen2.VL
ydleo hagesher
Instruction:
C waunhabatt
B bccese diceein pranding
Owen2
goond Your credt toaks captis fully deceher vicask carter cosation
E. Rod call.
Revised thinking precess:
deep thik snceeris. Slaeai Naower yelon up lags shop our "lawwar
Ground Truth: A.ycloo
The
Gemini
Caption with reflection:
Dkg, ki's teai don bn oppnn precitee procso setlone by seecace Copfice Sentence I: The rean wajee lene réce ilaetiie
skke proeecarly
peruo riking kene srow fice Ciption Senence
backyoead chrtcien and the cericcea of other diore Retketas uracuall backgroad toall, not huasang coneates Haneva chem
Lnstruction:
araiin cesinre with edaar changa the pedlix
Dosk ostaed captin loz
indiskal scnisces Depsi coch
kieri besepeees dourleng "eoremen of fakee moes toso locaoual poncatr hai guondd Exouptos the prisary actie Ou fan Iuscarate Comettin: The bacigread armutaoo apartienn
wheder
alge wits th
Original eaption:
vidoo conset and onainn uoe-dee
The mane
rakes The beonr.
(ill provisslly ponzmod cpttins.
@wen2.5
aqaiatin antie taclddag i bilees. They care segatirr joxping cur cheucles (bouda and enveriee of other rider.
Revised caption with reflection:
teckgread sepake
-Bidi Lat's pracre optice lap to viy Opocio Sex eace The be beckgoond mosiss conisior, wils miloc desge in poolion rain sifject niter bockgoond
revemeee ofher
Obissc foad. riderappooo afterh rider
Pipeline for constructing reasoning chains. The upper part illustrates the process for the QA task, and the lower part shows the corresponding procedure for the caption task. For clarity, the data filtering step for non-compliant entries is omitted.
Figure 12:
Based on the filtered data, prompt QwQ' to generate more natural, detailed, and structurally clear reasoning processes using the captions and queries as input. Finally, the query, ground truth, and the reasoning chains produced by QwQ jointly fed into Qwen2-72B, which further refines any erroneous reasoning steps that are not corrected in subsequent reasoning steps, while preserving the original structure much possible. During this stage, any occurrences of the phrase such as "video caption" are replaced with the term "video" to better support SFT for multimodal large language models.
To ensure data quality, we manually review 20% of the curated samples, confirming both logical consistency and linguistic accuracy to meet the training requirements.
1.1.2 CONSTRUCTION OF REASONING CHAINS FOR THE CAPTIONING TASK
For captioning tasks, the pipeline used to construct reasoming chains used in QA tasks is no longer applicable. Instead, leverage the strong capabilities of 2em to construct high-quality reasoning chains.
Specifically, input both the video and its original caption into Gemini, which is prompted to decompose the caption into individual sentences and generate them sequentially. After each gener- ated sentence, Gemini is instructed to reflect on whether the current output conflicts with the video content or with previously generated sentences. If conflict is detected, the model is required to immediately correct the inconsistency, disregarding the original caption, and continuing to generate new captions along with corresponding reflections. If no conflict is identified, the model should proceed to the next sentence from the original caption and perform another reflection step.
To further enhance the linguistic flu- ency of the reasoning process, we feed the generated caption and reflec- tion sequences into Qwen2.5-72B. This model is instructed to preserve the logical structure of the original reasoning while rephrasing and pol- ishing the language, thereby aligning the overall process more closely with the natural cognitive mechanism of "generating while reflecting" that characterizes human caption creation.
Phase
DarkEventlafer
MixVidQA
VideoQA
Captioning
Total
SFT RL
1,576
1,203
4,639 2,003
10.891
18,309 10,800
1,841
2,332
4,624
Table 8: Data distribution in our training set.
htttps://qwennn 1o/bl ol/////////// og/ quq- 32b 3---332bbbrree preview
23

---

Published as a conference paper at ICLR 2026
Understunding
Reasoning
Caption
Model
Reaso- ning
Video-MME
LongVideo- Bench
MVBench
NEXT-QA
IntentQA
DarkEvent- Infer-Tes
QN-Test
DREAM-IK
/e/ Pre
Dwen2-5-VL-7B
X
59.4
50.6
58.7
72.0
91.3
73.0
29.0
30.928.3772
S-en225-VL-Ans-SPP
54.3
43,9
55.6
71.4
89.9
70.0
20,0
2933 29.0 29.6
D.en2.5.VL-CCT1555
55.2
50.6
28.2
86.2
276/27991
88.1
28./29.5/27.
VidBerdge-R1 (Ouns
59.3
97.1
35.2/328/372
Table 9: Performance comparison of different training methods. "Ans-SFI" refers to SFT using direct answers without reasoning chains. "CoT-SFT" denotes SFT with reasoning chains. "CoT- SFT-RL" stands for RL based on the CoT-SFT model.
As in the QA tasks, we manually review 20% of the generated samples to ensure both logical con- sistency and linguistic accuracy. confirming that the data meet the quality standards required for training. The final numbers of training samples used in the SFT and RL phases summarized in Table 8.
E.2 EXPERIMENTAL RESULTS
In Table 9, we present the experimental results related the approach of SFT-then-RL. Specifi- cally, we compare VidBridge-RI with three additional settings: (1) SFT using only direct answers without reasoning chains D0wen2.5-VL-Ans-SFTH
(2) SFT with the inclusion of reasoning pro- cesses (Qwem2.5-VL-CoT--FF) and (3) further applying RL after SFT with reasoning processes (0wan2.5wVL-CoI--FFTR.)
The results demonstrate that, across general video understanding, video reasoning, and captioning tasks, directly applying RL significantly outperforms the approach involving an initial phase of SFT. This performance gap be attributed to the fact that, during the cold-start phase, the model is com- pelled to learn complex reasoning patterns that exceed its current cognitive capacity, consequently exerting negative impact the subsequent RL phase. Moreover, the performance improvement of RL on the base model is diminished in the scenario of CoT-SFT, further supporting our hypothe- sis. These findings collectively suggest that compelling the model imitate sophisticated reasoning processes beyond its current capabilities may ultimately degrade its final performance.
E.3 QUALITATIVE RESULTS
Figure 13 presents qualitative comparison between two training paradigms: direct RL (VidBridge- R1) and SFT cold-start followed by RL Sowee225-VI--oo--SF--
While the model initialized with SFT demonstrates reasoning patterns that appear more aligned with human cognitive processes, its outputs often contain significant hallucinations. In contrast, the model trained directly with RL tends to produce more concise and accurate reasoning content.
Specifically, in the upper case, Dwwe2.55-VL-CoT-SEE segment lasts for an hour, despite the entire video being less than three minutes. Furthermore, misrepresents the temporal sequence of events. In comparison, VidBridge-Rt R1 accurately identifies the location of the black screen event and provides a correct inference. In the lower case, Qwen2.5- VL-COT-SFFTRL hallucinates the presence of various additional ingredients besides the chicken, while VidBridge-RI correctly recognizes that the pot contains only chicken and water.
erroneously claims that the black screen
These observations suggest that compelling the model imitate complex reasoning processes be- yond its current capability may lead to hallucinatory inferences inconsistent with the video content, thereby ultimately undermining the model's final performance.
F ADDITIONAL CASE STUDIES
As illustrated in Figures 14, 15, and 16, we present qualitative comparisons between VidBridge-RI and the baseline models (Video-R1, VideoChat-RR and VideoRFT) across a range of tasks, includ- ing general video understanding, cognitive reasoning, and descriptive captioning. The results indi- cate that all baseline models exhibit varying degrees of hallucination during their lengthy reasoning chains, while VidBridge-RI consistently delivers accurate and reliable performance.
24

---

Published as a conference paper at ICLR 2026
Cases on Reasoning Tasks
You will provided with video featuring a black screen segment that deliberately conceals particular event. You should analyze the video and deduce the event most likely hidden by the black screen. Ground Truth: The event hidden by the black sereen segment is most likely the mixing and chemical reaction between the liquid the bottles and materials on the ground.
Prompt:
<think> Okay, let's tackle this problem step by step... The key points from the video A man is kneeling pan and spreads it around. Then he adds liquid from a bottle, mixes them, the mixture starts reacting, 4. After mixing, there's a black screen for one hour. 5. When the screen comes back, the reaction has stopped, and the powder is gone. So the black screen is during the one-hour period after the reaction starts... So the concealed event
-ive2.5-VLL-CoT-STT
white powder bag labeled "Solutions." " 2. He pours the powder into
ongoing reaction that uses up powder, resulting its complete disappearance. </think> <answer> The event hidden by the black screen segment is likely the continuation the chemical reaction between the powder and liquid, which leuds to the complete consumption of the powder. <'answer>
VidBridge-RI: <think>
The black screen segment in the video likely conceals particular event series of actions that occur between visible frames. This could include application solution, the reaction setting of materials, and the cleaning and preparation steps that the person is performing. The black sereen serves to hide the details of this specifie process, creating pause in the visual narrative to build anticipation segment likely hides specific
for the next visible action result. </think> <answer> The black or scries of actions that occur between the visible frames, such as the mixing, application, reaction, and cleaning processes involving the substances and tools laid out on the floor. </answer>
Prompt: You are given video clip made up of alternating segments from two different sources, along with question related
the question using only the information from the relevant video source. Question: What is the primary ingredient used alongside the meat during the cooking process? Ground Truth: Water is the main ingredient used alongside the meat for boiling.
them. Your should
<think> Okay, let's tackle this question. The user is asking about the primary ingredient used alongside the meat during the cooking process... need identify the cooking frames... Looking at the cooking frames, mentions that the main ingredient chicken, along with various ingredients. So, the primary ingredient used alongside the chicken during the cooking process is another unspecified food item that complements it in the dish. Therefore, the answer to the question is that another unspecified food item is used alongside the chicken while being cooked. </think> <answer> Another unspecified food item used alongside the chicken while being cooked. </answer>
Swen2.5-VI-CoT--FFE
VidBridge-R1: <think>
The primary ingredient used alongside the meat during the cooking process is water. </think> <answer-Water is the primary ingredient used alongside the meat. </answer>
Figure 13: Qualitative comparison of direct RL (VidBridge-RI) with SFT cold-start then RL (Qwen2.5-VL-Coo--S5
Notably, as shown in Figure 15, although Video-RI generates a correct reasoning chain, it ultimately produces inconsistent and incorrect conclusion. This discrepancy suggests potential limitations the model's ability to maintain coherence over long reasoning sequences. In contrast, VidBridge- R1 achieves superior accuracy by adopting more concise reasoning chains derived from itself, rather than relying on forced imitation through SFT, which better aligns with its inherent capabilities, leading to more coherent and dependable outputs.
25

---

Published as a conference paper at ICLR 2026
Cases on Video Understanding Tasks
Sy 2oyo
10
daratds
Prompt: What do the scenes shot Indus River and Yangze River have in common?
They all shot at dusk. /
B. The land is covered with snow in both scene.
C. There are bridges on both scene.
They both contain island in the middle of the lake.
Qwen2.5-VL. (reasoning): <think> The Indus River scene shows bridge, and the Yangtze River seene also shows a bridge. No other common features are shared between the two scenes. </think> <answer> C. There are bridges both scene. </answer>
<think> Let's break down the options... Option A: "They are all shot dusk." " This option suggests both scenes were captured during same time of day. However, there is no explicit mention or visual evidence in the video description that indicates the time of day for either scene. Therefore, this option seems unlikely... Option C: "There are bridges on both scene." " This option suggests that both scenes feature bridges. The video description clearly mentions bridge over the Indus River and a bridge over Yangze River. Both descriptions explicitly state the presence of a bridge, so this option appears to be correct... After evaluating all the options, the most accurate description of the scenes for the Indus River and Yangze River is that there are bridges both scenes. Final answer: C tthnnk> <answer> C </answer>
Video-RI:
VideoChattRII: <think> Let's analyze each option for the scenes shown: 1. Option A: They are all shot at dusk. The Indus River scene appears to be during daylight with clear skies. The Yangtze River scene also appears he during daylight with clear skies. Neither scene shows signs of dusk... 3. Option C: There bridges on both scenes. The Indus River scene has a bridge visible across the river. The Yangize River scene also has a bridge visible across the river.. Based on this analysis, the only commonality between the two scenes is that they both contain bridges. </think> <answrr> C. There are bridges on both scene. </answer>
The video begins with the Syr Darya River, followed by the Indus River, which is shown as a wide river with city in background... until reaching the Yangtze River, which is described as flowing into the East China Sea. For the Indus River, the video shows a wide river with a city in the background, indicating bustling urban the background, also suggesting a significant urban presence. Now, let's evaluate the given options: Option A: They are all shot at dusk. The video does not specify the time of day for either the Indus or Yangtze River scenes, this option cannot be confirmed... Option C: There bridges both scenes. The Indus River scene includes a bridge over the river, while Yangtze River scene also features a bridge. This option correct... Based on the visual evidence, the common element between Indus and Yangtze River scenes is the presence of bridges. </think> <answer> C </answer>
VideoRFT: <think>
For the Yangtze River, the video depicts wide river with city
VidBridge-RI <think> The scenes shot the Indus River and Yangtze River are both shown in the video, and upon analysis, both scenes are captured during dusk, as indicated by the lighting and sky conditions each respective frame, </think> <answer> A. They are all shot at dusk, </answer>
Figure 14: Qualitative comparison of VidBridge-RI with baseline models on QA tasks in general video understanding.
26

---

Published as a conference paper at ICLR 2026
Cases on Reasoning Tasks
Prompt: Which of the following interventions would be best suited to prevent future events such as those shown in the video from occurring in the future?
A. Installing horizontal drainage systems to reduce groundwater pressure within the slope.
B. Regrading the slope to gentler angle by removing material from the upper sections.
C. Securing the cliff face with rock bolts and covering it with wire mesh to stabilize loose rocks.
Planting deep-rooted vegetation on the cliff face reinforce slope with root systems.
E. Diverting the road away from the cliff by constructing a tunnel through the mountain.
Qwen2.5-VL (reasoning): <think> The video depicts landslide a similar type slope failure. To prevent future occurrences, the most effective intervention should address the root cause of the instability. Option A: Installing horizontal drainage systems can help manage groundwater pressure, which is often contributing factor slope failures... Option C: Securing the cliff face with rock bolts and wire mesh provide immediate stability but may not address the underlying issues causing the instability... </think> <answer>> A.
Installing horizontal drainage systems to reduce groundwater pressure within the slope. </answer>
Video-RI: <think> Let's break down the options one by one to determine which intervention would be most effective for preventing future landslides similar events... Considering the effectiveness and practiculity, the best intervention would be secure the cliff face with roek bolts and covering it with wire mesh. This provides immediate and effective stabilization. Final answer: C </think> <answer> A. Installing horizontal drainage systems to reduce groundwater pressure within the slope. </answer>
VideoChat-RI: <think> The video shows landslide or a similar type slope failure. To prevent future occurrences, the intervention should address the root cause of the instability, which could be related to water pressure, slope angle, loose rocks, or soil composition. Option addresses groundwater pressure, which be significant factor slope stability... Option C focuses on securing the slope with physical reinforcement, which is effective but may not be the most cost-effective solution... Given the immediate need to address the issue without major structural changes, installing horizontal drainage systems seems like the most direct and effective way to manage groundwater pressure and prevent future landslides. </think> <answer> </answer>
VideoRFT: <think ... Option A involves installing horizontal drainage systems reduce groundwater pressure. This could help stabilize the slope by managing water flow, which crucial preventing landslides caused by saturated soil. However, the video does not show any signs of water erosion, so this option might not be directly applicable... Option C proposes securing the cliff face with rock bolts and wire mesh. This method can stabilize loose rocks and prevent them from falling, underlying issues causing the slope instability... Given the immediate need to prevent further landslides, Option A (horizontal drainage) seems the most direct approach manage groundwater pressure and stabilize the slope. </think> <answer> </answer>
might not address the
VidBridge-Ris <think<The video depicts landslide similar slope failure event, which likely caused by factors such as unstable slope angles, loose rock, or lack of root reinforcement. Among the given options, the most comprehensive und effective intervention to prevent future similar events would be one that addresses <answer~C. Securing the cliff face with
multiple potential causes and provides long-term stability. rock bolts and covering it with wire mesh to stabilize loose rocks </answer>
Figure 15: Qualitative comparison of VidBridge-RI with baseline models on QA tasks in video reasoning.
27

---

Published as a conference paper at ICLR 2026
Cases on Captioning Tasks
Prompt: Provide detailed yet concise description of the video.
Qwen2.5-VI. (reasoning): <think> The video depicts scene night with starry sky. Two characters, one with blue hair and another with pink hair... The overall atmosphere seems calm but slightly mysterious due to the characters' positions and the setting. </think> sanswer> The video shows two animated characters in nighttime urban setting, with character sitting up while the other lies down. A green passes by, partially covering the view of the characters. The scene has a calm yet mysterious ambiance. <answer>
Video-R1: <think>> The video depicts an animated scene set at night... </think> <answer>> The video begins with two animated characters lying the ground in a nighttime urban setting. One character has blue hair and the other has pink hair... As the scene develops, the character with blue hair starts to move, sitting up and then standing. The character with pink hair remains lying down. The character with blue hair then walks away. leaving the character with pink hair still lying on the ground. A green car drives into the frume from the left side, partially obscuring the view of the characters. The video concludes with the character with pink hair still lying on the ground, while the green car continues to drive away. <answer>
MideoChat-Rie RI: <think> The vidco depicts two animated characters lying on the ground... </think> <answer> The video shows animated characters lying on the ground in urban setting night. They appear to be in distress or unconscious, as they are sprawled out on the pavement. A green car drives past them, indicating that they might have been hit by the vehicle. The background features typical cityscape with buildings and a clear sky filled with stars, suggesting it's late at night. The overall mood of the video conveys sense of urgency and concern for the characters' well-being. </answer>
<think> To describe video, break it down step-by-step... </think> <answer> The video shows two animated characters lying on a quiet, starlit urban street at night. One character has blue hair and wears light-colored outfit, while the other has pink hair and white and yellow outfit. Initially, they are motionless, resting near streetlight with illuminated buildings in the background. the blue-haired charucter begins to sit up and look around, followed by the pink-haired character who also sits A green vehicle with red roof then enters the scene from behind, moving toward the characters. Throughout, the characters interact, gesturing and facing each other, while the serene, sturry night and urban backdrop remain constant. </answer>
VideoRFT:
video progresses,
VidBridge-R1: <think> The video begins with a wide shot of a nighttime urban scene, featuring two animated characters lying the ground in crosswalk. The background includes buildings with illuminated windows a starry sky. characters are initially motionless, then one character starts move, sitting up and looking around. Another vehicle, a green SUV, then enters the frame from the left side, driving over characters who remain the ground. The scene maintains consistent nighttime setting with the same background elements throughout. </think> <answer> The video starts with wide nighttime urban scene showing two animated characters lying on ground a crosswalk, then transitions to one character sitting up and looking around, followed by the entry of'a a green SUV from the left, driving over the two characters, all while maintaining the same background of illuminated buildings and starry sky. </answer>
Figure 16: Qualitative comparison of VidBridge- R1 with baseline models on video captioning tasks.
28


Reviews:
[{"review_id": "bwZ0ywpg20", "reviewer": "Reviewer_NB7J", "summary": "This paper addresses the conflict between video QA and video captioning under reinforcement learning (RL)–based training within the \"Reason-Then-Respond\" paradigm. The authors argue that QA (convergent reasoning) and captioning (divergent generation) inherently pull model optimization in opposite directions, causing performance degradation when trained jointly. To mitigate this conflict, the paper introduces two proxy tasks: 1) DarkEventInfer (predict masked video segments based on context) and 2) MixVidQA (answer questions about one of two interleaved video clips). Integrating these proxy tasks, the authors propose VidBridge-R1, a video understanding model trained via GRPO RL without an SFT stage. Experiments across general video QA, video reasoning, and captioning benchmarks demonstrate improved overall performance compared with several existing RL-based video MLLMs.", "questions": "Please refer to the weakness section for my questions.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Overall, the paper clearly identifies a meaningful optimization conflict between QA and captioning under RL training and motivates why naive multi-task RL leads to mutual degradation.\n2. The two proxy tasks (DarkEventInfer and MixVidQA) are intuitively aligned with promoting both holistic contextual reasoning and selective information grounding, and their construction process is described with adequate clarity and filtering steps.\n3. The experimental evaluation is comprehensive, covering general video QA, reasoning QA, captioning benchmarks, and held-out test sets for the proxy tasks.", "weaknesses": "1. While the proxy tasks appear to be effective, it is still not fully demonstrated \"why\" these particular tasks are optimal among possible bridging tasks. A brief analysis or comparison with alternative proxy formulations (e.g., temporal reordering tasks, masked key-frame inference) would provide more insights to the readers.\n2. The experiments are mainly conducted on Qwen2.5-VL-7B-Instruct. It is encouraged to discuss how generalizable the method is to stronger or smaller video-language backbones, e.g., 2B or 32B levels.\n3. The code of this project is not uploaded for review. And the paper does not contain the Reproducibility Statement, yielding strong concerns in the reproducibility issue and the actual contribution to the community. I believe that it would be hard to reproduce the results given the limited implementation details in the paper.", "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": "NsUTH4384l", "reviewer": "Reviewer_rDVT", "summary": "The paper focuses on post-training a video LLM for both QA and captioning, arguing naively combining the reward signals for both tasks leads to suboptimal performance. Two proxy tasks are presented:\nDarkEventInfer, which presents videos with masked event segments, forcing the model to use context to infer the missing content. \nMixVidQA, which interleaves sequences from two different videos and challenges the model to reason about one while ignoring the other. \nThe resulting VidBridge-R1 model is trained with these proxy tasks alongside traditional QA and captioning tasks. Experiments show that VidBridge-R1 achieves sota performance across a various QA and captioning benchmarks. The paper also includes an ablation study and analysis of the training dynamics.", "questions": "- How much compute do the new tasks add compared to the standard ones?\n- About the training dynamics study: the initial dip in captioning performance on Dream is striking. How sensitive is the final model performance to the sampling ratio between the four tasks during training? \n- Is there any other evidence than the downstream performance for the conflict between the QA and captioning task?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- Well motivated problem statement and intuitive proposals\n- Sota performance on both captioning and QA tasks", "weaknesses": "- The ablation in Table 3 eludes important rows showing the benefit of the proposed tasks together with the caption task, as well as the row with VidMixQA and not DarkEventInfer\n- Each task being based on different data makes it difficult to disentangle the benefits of the task vs the data", "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": "yMsXtKjWXV", "reviewer": "Reviewer_mgtE", "summary": "This paper proposes two kinds of data augmentation methods for video reasoning tasks, namely DarkEventInfer and MixVidQA, enabling the model to predict the answer under the augmented video. Experiments show the effectiveness of the proposed method.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The idea sounds reasonable. Effective data augmentation methods are always needed.\n2. The writing is clear.\n3. The presented results are promising.", "weaknesses": "1. As an data augmentation method, whether the method is scalable is questionable.\n2. Lack of detailed analysis, why the reasoning models show poor performance  on video general understanding and captioning tasks, but the model trained with the augmented reasoning data bring clear improvement. Is this a specific kind of overfitting or data leakage?", "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)