Video-R1: Reinforcing Video Reasoning in MLLMs
Authors: Kaituo Feng
Kaixiong Gong
Bohao Li
Zonghao Guo
Yibing Wang
Tianshuo Peng
Junfei Wu
Xiaoying Zhang
Benyou Wang
Xiangyu Yue
Venue: NeurIPS 2025 poster ()
Keywords: Multimodal Large Language Models
Video Reasoning
Primary area: deep_learning

Abstract: Inspired by DeepSeek-R1's success in eliciting reasoning abilities through rule-based reinforcement learning (RL), we introduce Video-R1 as the first attempt to systematically explore the R1 paradigm for incentivizing video reasoning within multimodal large language models (MLLMs). However, directly applying RL training with the GRPO algorithm to video reasoning presents two primary challenges: (i) a lack of temporal modeling for video reasoning, and (ii) the scarcity of high-quality video-reasoning data. To address these issues, we first propose the T-GRPO algorithm, which encourages models to utilize temporal information in videos for reasoning. Additionally, instead of relying solely on video data, we incorporate high-quality image-reasoning data into the training process. We have constructed two datasets: Video-R1-CoT-165k for SFT cold start and Video-R1-260k for RL training, both comprising image and video data. Experimental results demonstrate that Video-R1 achieves significant improvements on video reasoning benchmarks such as VideoMMMU and VSI-Bench, as well as on general video benchmarks including MVBench and TempCompass, etc. Notably, Video-R1-7B attains a 37.1\% accuracy on video spatial reasoning benchmark VSI-bench, surpassing the commercial proprietary model GPT-4o. All code, models, and data will be released.

Video-R1: Reinforcing Video Reasoning in MLLMs
Kaituo Feng', Kaixiong Gong', Bohao Li", Zonghao Guo Yibing Wang', Tianshuo Peng', Junfei Wu*, Xiaoying Zhang", Benyou Wang", Xiangyu Yue
CUUH MMLab, PCUKK (SZ), TTiinghua University, *UCAS, SCUHK HCCL
https:///gtthbb htt.p//......ouuuueeeee/////ddd.
Abstract
Inspired by DeepSeek-Re success in eliciting reasoning abilities through rule- based reinforcement learning (RL), introduce Video-RI as the first attempt systematically explore the paradigm for incentivizing video reasoning within multimodal large language models (MLLMs). However, directly applying RL train- ing with the GRPO algorithm to video reasoning presents two primary challenges: (i) lack of temporal modeling for video reasoning, and (ii) the scarcity of high- quality video-rcasoning data. To address these issues, we first propose the T-GRPO algorithm, which encourages models to utilize temporal information in videos for reasoning. Additionally, instead relying solely on video data, we incorporate high-quality image-reasoning data into the training process. We have constructed two datasets: Video-R1-CoT-Iee for SFT cold start and Video-R1-260 for RL training, both comprising image and video data. Experimental results demonstrate that Video-R1 achieves significant improvements on video reasoning benchmarks such MMde and VSI-Bench, well on general video benchmarks including MVBench and TempCompass, etc. Notably, Video-R1-1 R1-BB attains a 37.1% accuracy on video spatial reasoning benchmark VSI-bench, surpassing the commercial proprictary model GPT-4o. All code, models, and data are released in https:///ithhu. http://ittuu.cco/ttuleeeeee www.tooleeeeeneeeeeeeeee VVie/o-ideRRR
Introduction
Recent advancements in rule-based Reinforcement Learning (RL) have significantly enhanced the reasoning capabilities of Large Language Models (LLMs) [6
particular, DeepSeek-RI [11] has demonstrated that carefully designed RL pipelines can lead to emergent and robust reasoning abilities with long
(CoT) in text-based domains. Motivated by this success, several recent efforts have explored extending RI training to Multimodal Large Language Models (MLLMs)
Notable examples include Kimi k1.5 33 and Skywork RIV V (39 which apply RL to improve reasoning over image-text pairs. However, despite these early explorations, the domain of video reasoning in MLLMs remains underexplored.
33
14 39
To bridge this gap, we present Video-R1 model, as the first attempt to systematically investigate eliciting strong video reasoning based the RI paradigm However, directly applying RL training with the Group Relative Policy Optimization (GRPO) algorithm [30] to video reasoning introduces two fundamental challenges: First. original GRPO lacks explicit reward signals for encouraging temporal reasoning in video. Without explicit temporal awareness, the model may take shortcuts for reasoning, focusing on single frame or snapshot rather than reasoning over time (see Figure for example; Video-UTR 44 also identifies a similar issue). The underexploitation for temporal cues can cause the learned reasoning strategies "shortcut" the process process-relying on superficial visual patterns, rather than engaging in deeper and temporally grounded reasoning. This could ultimately hindering generalization to more complex or diverse video reasoning tasks.
"Corresponding Authors
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Video:
Question: Are there any cyan cubes that enter the scene? A. not sure B.no C.yes
<thinkoLet me think about this carefully....The. question specifically asks about cyan cubes, see scene (shortcut)....f was already present. Hmm, this bit tricky. The question asking if ony cyan cubes enter the scene, but the cyan cube was already there..... break it down one more time to be sure: No new cyan cubes enter the scene....</hinkke Vn-onswer> B</onswer>
GRPO:
eyan
<thinko me think about this carefully....Ne Now. let's focus on the cyan cube it is not there at the beginning of the scene, It appears to moving into the scene from the bottom right corner. This indicates that the cyan cube indeed entering the that oppears in the scene, It is not present the initial state....but it is clearly visible in the final state..... This confirms that cyon enters scene.... After this thorough onalysis, I am confident that the onswer yes.....//thinle wn-onswerrC///neeeeee. /onswe>>>
T-GRPO:
Hmm, seems that the cyan cube
object
Reasoning paths of Video-R1 trained by GRPO and our proposed T-GRPO on test samples. Without explicit temporal modeling, models may learn sub-optimal video reasoning patterns by taking shortcuts, therefore failing to generalize well.
Figure 1:
The second issue lies in the scarcity of high-quality video reasoning training data, especially samples that demand strong reasoning ability or involve long reasoning path. Most existing video training datasets mainly focus on simple recognition tasks, rather than reasoning. This scarcity makes difficult to expose the model to diverse, challenging reasoning patterns during training, limiting the effectiveness of RL and hindering the emergence of robust reasoning behaviors.
To address these challenges, we propose two key solutions. First, we propose T-GRPO, an extension of the original GRPO algorithm [0] that explicitly encourages temporal reasoning. During training, the model is presented with both temporally ordered and randomly shuffled frame sequences, producing two groups of responses. A positive reward is assigned only when the proportion of correct answers from the ordered group exceeds that from the shuffled one. This strategy encourages the model to exploit temporal reasoning policy rather than relying on shortcuts derived from isolated frames.
Besides, to tackle the scarcity of high-quality video reasoning data, we strategically introduce image- based reasoning data part of training data. We construct two datasets: Video-R1-CC R1-Coo----oo for SFT cold start and Video-R1-22 1262 for RL training. The image data serves as a valuable foundation for training general reasoning skills, while the curated video samples provide the temporal complexity needed for video understanding. This hybrid training setup not only alleviates the data bottleneck but also enables the model to transfer reasoning skills learned from static images to dynamic video contexts. Combined with T-GRPO, this approach equips Video-RI with stronger, more generalizable video reasoning capabilities.
Our experiments show that Video-RI achieves consistent and significant improvements across a suite of challenging video reasoning benchmarks, including VSI-Bench (38). VideoMMMU 13, MMVU [8). MVBench (20]. TempCompass [27]. and VideoMME (.. Notably, Video-R1-7B attains 37.1 % accuracy VSI- VSI-Bench, challenging video spatial reasoning benchmark, outperforming even proprietary models like GPT-4o [15]. These results suggest that with carefully designed algorithms and data pipelines, RL can indeed unlock complex temporal reasoning capabilities in MLLMs, similar to the breakthroughs seen in the text domain. Our contributions can be summaried as follows:
We propose Video-R1, the first attempt to systematically to explore developing video reasoning MI.LMs based on the R1 paradigm. To support training, we construct two reasoning datasets: Video-R1-CoT-115L. for SFT and Video-R1-260k for RL training, incorporating both image and video reasoning samples. We hope that Video-R1 will serve foundation for future research video reasoning.
To address the lack of temporal modeling in existing RL methods, we introduce T-GRPO, a novel training algorithm that encourages the model to utilize temporal information by contrasting reasoning performance ordered and shuffled video frames.
Extensive experiments multiple video benchmarks, such as VideoMMMU, VSI-Bench, MVBench, etc, demonstrate the effectiveness of our approach. Notably, Video-R1-7B achieves 37.1% % accuracy on VSI-Bench, outperforming the proprictary GPT-4o model.

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2 Related Works
2.1 Multimodal Large Language Models for Video
Video understanding is an essential capability for Multimodal Large Language Models (MLLMs), enabling them to interpret and reason over dynamic visual content [31 4 37 44
46 32 In recent years, number of MLLMs have been developed specifically to advance progress in video understand- ing tasks. For example, LLaMA-VID [2 proposes a dual-token strategy (context and content tokens) to compress video imput representations, enabling vision-language models efficiently handle long videos while retaining essential visual information. VideoLLaMA2 [4] enhances video- video-language modeling by introducing spatial-temporal convolution for better dynamic understanding and an audio branch to integrate multimodal cues for richer video comprehension. Long VA [43 extends the context window of language backbones to process significantly longer video sequences without spe- cialized video training, offering a language-centric solution to long-range temporal reasoning. VISA (37 introduces ghowledge-drrven video object segmentation task that combines world knowledge with object tracking, addressing implicit, complex video queries through a segmentation-enabbl multimodal LLM. These advancements highlight the potential of MLLMS in advancing video un- derstanding. However, most prior works have primarily focused on video perception tasks. The development of MLLMs with strong video reasoning capabilities remains largely unexplored.
2.2
Large Language Model Reasoning
The reasoning abilities of Large Language Models (LLMs) have been focal point of recent research, aiming to enhance their capacity to perform complex, multi-step problem-solving tasks 34 42
49
41 24. Unlike earlier approaches that rely on dense, step-level supervision or learned reward models to supervise reasoning paths [10,2]] DeepSeek-RI initiates a new wave of interest in sule-baed reinforcement learning, demonstrating that even coarse, outcome-only rewards can effectively elicit strong reasoning behavior [[]]. Its success shows that with a carefully designed reward structure and policy optimization strategy. models could learn to generate long CoT without requiring intermediate supervision. Following this paradigm, several recent efforts have attempted to reproduce R1's success [4
23
8
35
6 45). For example, Open Reasoner Zero [12] and Kimi k1.5 B3 explore similar rule-based RL pipelines to enhance reasoning in the text and image domains, respectively. However, despite encouraging progress, few prior work has explored how to extend this approach to the video domain. Bridging this gap remains an open challenge and a promising direction for expanding the boundaries of reasoning models.
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36
3
28
18
Methods
3.1 Dataset Construction
High-quality training data plays a crucial role in reinforcing video reasoning capabilities in MLLMs. In this section, we will introduce how curate Video-R1-260k for RL training and Video-R1-CoT. 165k for SFT cold start.
Data Collection and Curation. To overcome the scarcity of high-quality video reasoning training data, we strategically introduce image-based reasoning data as part of training data. The image-based data serves primarily to teach the model a broad range of reasoning skills, covering various difficulty levels and domains such as math, spatial logic, expert-level knowledge, etc. These samples help the model develop generalized reasoning abilitics in static contexts. In contrast, the video-based data is primarily used to train the model's 's ability to perform temporal reasoning includiin understanding event progression, capturing frame-to toree-ttoiiie to-frame dependencies, and drawing inferences based on motion and causal dynamics over time.
We collect data from a variety of public datasets and carefully sample and balance the proportion of each subset. The final composition of the Aideo-R1-260 dataset is illustrated in Figure 2 The distribution of Video-RI -1-60k dataset can be roughly categorized as follows:
General (Video, 116k): diverse set of open-domain video data, covering everyday scenarios and designed to build temporal comprchension and reasoning abilities.
3

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General (Vides) 44%
LLoVA Video 1756 (mc. 73A) (LokA-VViee--78k (free, 106)
NeXT PenceptionTest (mc, CLEVRER
115K)
8%
General (Image) 6%
A-OKVQA 44). ToonQA (mc, 28) SharesPMVV (free, 2k)
VisuTW
(fres,
14%
Chart 8%
FigurEQA (mc, DVQA//mm, PlatQ//mmm 24)
CharlQA/um, MopQA(ms TablWWWee 24). TelMWW7ouu,, 24) Char1TText(fres, 26)
ReBUT SQAAtree, 24)
Vsu/Weebsstrutttreees. 6k)
44%
Video-R1-2600
OCR 6%
TexTIQA (oon, HME3DDN((cCr CrromeWhiinglers 2k)
IAM (oor, 2k) Rendered Texteer. 4k) TexnEspstor, 2k) TextocRtorr, 2k)
14%
Math 14%
CLEVR-Math (nmm, GE0Sime, 0.8k) GeometyyKKine 2k)
GeoQAA UnGeztne, Mutineth 300K/nc/sum, 271) Super- cueee-LEE///uun...
Knowledge 14%
TQA (mc, Se) AT2D(me, 4k) ScienoeQAlme 44)
6%
PMC-VCAImc, 64) VQu-Rab(me 0.5k) SVLGAimm 4k) Aransaain,, 10k) EXAMS-V (nc. 28)
8%
6%
AI20-gp14s (fres,
Spatial Operspeaxsfieg/nn//rre. 104) Spocellao/reg//c//feee 10k)
Figure 2: The data distribution of our Video-R1-2600 dataset.
General (Image, 15k): A general-purpose image question- answering data, used provide basic visual understanding.
Chart (Image, 21k): Visual reasoning over charts, line graphs and scientific figures, focusing data interpretation and quantitative logic.
OCR (Image, 16k): Facilitate reasoning tasks that require recognizing and interpreting embedded textual content such as signs, forms, or documents.
Math (Image, 37k): Image-based math reasoning questions, including formulas, geometry diagrams, and multi-step symbolic reasoning.
Knowledge (Image, 37k): Visual commonsense and multi-discipline reasoning tasks, testing the model's ability to integrate world knowledge with visual cues.
Spatial (Image, 20k): Tasks that require understand spatial information for reasoning.
facilitate an effective SFT cold start, we leverage Owen2.5-VLL72B--IE I1 to generate CoT rationales for the samples in Video-R1-2660 The prompt template for CoT generation is provided in Appendix C.I After applying basic rule-based filtering to remove low- quality inconsistent outputs, obtain high-quality CoT dataset, Ceteo-R1-CoT-le which is used for the cold-start SFT stage.
CoT Annotation.
Data Type and Rule-based Reward Design. Since our reinforcement learning framework follows the rule-based reward paradigm of DeepSeek-RI [[], is crucial to ensure that the reward signals are both reliable and precise. To this end, the majority of our training data is designed around tasks with clearly verifiable outputs, such as multiple-choice and numerical answer formats. This allows for accurate reward computation using simple rules, ensuring stable and effective RL training.
However, increase the model's 's flexibility and its ability to generalize across diverse tasks and formats, we also incorporate smaller portion of other data types. These include free-form generation, OCR tasks, and regression problems, which are essential for adapting to real-worle applications and broader datasets.
The data types and corresponding correctness reward functions summarized as follows:
Multiple Choice:
The reward is assigned based on whether the predicted answer matches the ground-truth answer.
Numerical QA: A binary reward is given depending on whether the predicted number exactly matches the reference value.
OCR: We compute the reward using the Word Error Rate (WER), measuring the edit distance between the predicted and reference text.
Free-form QA: The reward is calculated the average of ROUGE-I, ROUGE-2, and ROUGE-L scores between the model's output and the ground-truth answer.
Regression: The closer the predicted value to the ground truth, the higher the reward, caleulated as minus their relative error.

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ordered group
Question
Ref Model
rewords
advantages
O2
R
A
Temporally ordered fromes
Policy Model
Og
Reward Function
Group Compune
A2
Aa
Randomly shuffled frames
Temporal
Reward
shuffled group
Figure 3: An illustration of our proposed T-GRPO algorithm.
3.2
Temporal Group Relative Policy Optimization (T-GRPO)
While GRPO 30 has proven effective in text-based reasoning, it lacks explicit reward signals for temporal reasoning- it insufficient for training MLLMs reason over videos. To address this, we propose Temporal Group Relative Policy Optimization (T-GRPO), which introduces a contrastive reward mechanism that explicitly encourages temporal reasoning, as illustrated in Figure 3
The core idea behind T-GRPO is to compare the model's performance on the same video question when frames are provided in different orders: (I) the temporally ordered sequence, and (2) randomly shuffled version. For each input question, we generate two groups of responses {o;
and (ōg _ using the ordered and shuffled frame inputs, respectively.
Let p and p denote the proportion of correct answers in each group. We then define a temporal reward
Tt a, p (o: 0, otherwise
(1)
where a is hyperparameter controlling the magnitude of the temporal reward. Here we set 0.3.
This contrastive design encourages the model to perform better when the video is presented in correct temporal order than when is shuffled. The model is only granted this positive reward if its current reasoning strategy for a given question demonstrates a reliance on temporal information. For tasks with continuous rewards (e.g., free-form answers), threshold (e.g., 0.5) can be used to determine whether a response is considered correct.
Importantly, F; is only applied to correct responses to ensure meaningful positive advantages. Apply- ing it to all responses would dilute the reward signal and hinder effective learning. In other words, when the model's 'ss reasoning policy successfully relies temporal patterns, correct responses reinforced with a higher reward, while incorrect ones remain unaffected.
Formally, the temporal-augmentel reward is defined as:
P'ss + Tty if og is correct
R4
(2)
ri
otherwise
where r; is the reward for response i, containing both the correctness reward and the format reward, following [11]. R, is the final reward used for calculating advantages. This reward shaping ensures that when the model answers correctly under a temporal setting but fails to outperform the shuf- fled baseline, it receives additional reward- reward-pushing the optimization toward adopting a more temporally aware reasoning policy. The temporal reward ri could also added to the advantages directly.
Then, the advantage A, computed over the rewards within each group:
R² mean ((R,))
A
(3)
std({R)
5

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Following DeepSeek RI [T]. the final policy update is as follows:
min TT(0:19) As. Te.u (os q)
clip To(O:19)
FFCRPO(() [o.)
mend Temn( (1,1g) -,- 1,1, -c
(4)
TKL (Tol(
By explicitly comparing the model's performance under ordered and shuffled inputs, T-GRPO introduces a contrastive training signal that drives the model to prefer reasoning strategies that leverage temporal patterns. It is worth noting that T-GRPO is only employed for video-based inputs in the training process of Video-R1.
3.3 Training Strategies
We adopt Qwen2.5- 1--------BB----s Instruct [1] as the base MLLMs for training. Similar to DeepSeek-RI R1 []. the training process is conducted in two stages: SFT cold start followed by RL training. For these two stages, both adopt image-video mixed training strategy.
In the first stage, we perform SFT on the Video-R 1-w------TTT--551 dataset. This step serves as a cold-start initialization, equipping the model with basic reasoning capabilities across variety of modalities. The resulting model is denoted as Qwen2.5-V1-77-SFT..
In the second stage, we further train the Qwen2.5-VL-7B: dataset using our proposed T-GRPO algorithm. This reinforcement learning phase is designed to guide the model beyond the rigid, pattern-matching matching behavior induced by SFT, encouraging it to freely explore better reasoning strategies. The resulting model is denoted as Video-RI-7B.
model on the broader Video-R1- 1-2200k
To further enhance the quality of reasoning, we introduce a length- reward to regulate the length of the model's output. Specifically, this mechanism aims to strike balance between encouraging deeper reasoning and preventing overthinking. For each reasoning path o,, if the predicted answer is correct and the response length falls within predefined interval [/min Amax], the model receives an additional reward 77 w. Formally:
R, + w, if is correct and 4nin len(o.) S bmax
R;
(5)
R, otherwise
This reward encourages the model to think deeply without overthinking. In this paper, we set = 0.2, Imin 320 and l Imar 512. The analysis of this reward's effect is provided in Appendix A.2
3.4 Aha Moment in Video Reasoning
We find that Video-RI is capable of performing complex reasoning with self-reflective behaviors, often referred to as "aha moments" [11]. Specifically, the model occasionally revisits its interpretation of the video or reflects on previous outputs, particularly when confronted with ambiguous temporal cues multi-step inference tasks. These behaviors indicate that the model is not merely executing memorized patterns but is actively engaging in internal feedback loops by re-examining evidence. An
example is demonstrated in Figure 4] More examples can be found in Appendix B
4 Experiments
4.1 Setup
Benchmarks. We evaluate our model on six video benchmarks: VSI-Bench (38 VideoMMMU
[3, MMVU 48). MVBench (20). TempCompass (27), and VideoMME 9. Among them, the first three are video reasoning benchmarks. which focus primarily on assessing the model's 's reasoning capabilities in video understanding. The latter three are general-purpose video understanding bench- marks, which include a mixture of perception and reasoning tasks. For MMVU, we evaluate on its multiple-choice question set for stability and consistency. For evaluations, we follow the decoding configuration used in the official Qwen2.5-VE 5L demo, with top_p _ 0.001 and temperature 0.01.

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Video:
You are a nobot beginning at the blue built in bookshelf facing the blue built-in bookshelf. You want to navigate to the bathroom sink. You will perform the following actions (Note: for each [please fill in], choose either 'turn back," "turn left,' or 'turn right. '): [please fill in] Go forward until the door of the buthroom. 3. [please Fill in] 4. Go forward until the sink, You have reached the final destination.
Question:
A. Turn Back, Turn Right B. Turn Left. Turn Left C. Turn Right, Turn Left
D. Turn Back, Turn Left
buift-in bookshelf. To get to the bethroom sink, need to move in specific direction. The first action is to go forward, which meons I need to move away from the bookshelf. Since I'm facing bookshelf, I should turn left door. should turn left to go into bathroom and then turn left agait to reach
Qwen2.5- 1----------I--eree
Let me think through this step by step. stort
forward. After that, need ge forward until I
neach bathroom door, Once I'm the sink. This sequence of actions oligns with option B. crowerrB/onsswer
Video Mileo-R1-0 R1-7B: ethinloLet think about this carefully. The goal navigate from the blue built-in bookshelf
bathroom sink. The first step is to determine the direction. from the bookshelf to the bathroom, The blue built-n bookshelf is on the left side of the room. The bathroom is on the right side of the room. So, I need to turn right from the bookshet kahkh to head towards the bathroom.
2, After turning right, need to go forward until I reach the door of the bathroom. This meons need to continue moving forword in the direction I just turned.
Once neach bathroom door, need to turn enter the bathroom. This because sink typically located on the left side of the bathroom.
So, the sequence of actions should be:1. right from bookshelf. 2, forword until the bathroom door. 3, Turn left to enter bothroom, 4, forword until the sink (Perception and Reasoning)
Hmm, this Option A: Back, Turn Right This would mean going back bookshelf
align with the sequence actions
Let's break down time to verify.
turning right, which doesn't
sense for nasching the bathroom..
Option B: Tum Left, Turn Left - This would mean turning left from the bookshelf and than turning left again, which also doesn't make sense.
Option b: Turn Back, Turn This would mean going back bookshelf and then turning left, which is not the cornect path. (Reflection)
Oh, The correct sequence indeed in option C, Let's confirm last time. The cornect sequence forword to the bothroom door, turn left, and go forword to the sink, This matches option C, Final onswer: Let's 's's verify one last fime. Yes, C 8 the correct onswers/ thinko conswerCC/onsvee>
right, go
Figure 4: An example of Video-R1-7B" reasoning output on VSI-Bench.
Training Details. We train our model using up to 8 NVIDIA A100 (80GB) GPUs. For efficiency considerations, we limit the maximum number of video frames 16 during training. Each frame is processed at a max resolution of 128 x 28 x 28 pixels. During inference, we increase the frame resolution to 256 x 28 x 28 pixels and frames to 16~-64 to enhance performance. The ordered group size G is set to 8 and the shuffled group size G is set to half of that for efficiency. More details are provided in Appendix D
We first perform SFT on Video-R1-CoT- 165k for one epoch to obtain the Owen2.5-VLL77B-ST model. This is followed by RL. training on Video-R1-260k to produce the final Video-R1 I model. Due to current computational resource limitations, we train the model for only 1k RL steps. Surprisingly, even within this limited training budget, the model exhibits significant improvements in video reasoning performance, indicating the strong effectiveness of both our data design and algorithm. Further experiments exploring the impact of scaling up RL. training are presented in Appendix A.
4.2 Main Results
As shown in Table 1 Our experimental results across six benchmarks validate the effectiveness of Video-R1 in video reasoning and general video understanding tasks. The key findings are as follows.
Superior Performance of Video-R1. Video-RI significantly outperforms previous models across most benchmarks, with particularly strong gains on video reasoning tasks such as VSI-Bench, VideoMMMU, and MMVU, This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the effectiveness of reinforcement learning for video tasks.
RL Works Better Than SFT. We observe that the SFT model Qwen2.5- VL------V-BB--FT does not consistently improve performance. In some cases (e.g.. VideoMME), performance even slightly drops after SFT, likely due to overfitting or limited generalization in unseen scenarios [ In contrast, after only 1K steps of reinforcement learning, Video-R1 achieves significant performance boosts. This clearly demonstrates the importance of RL in unlocking generalizable video reasoning capability.
7

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Table 1: Performance of different models on benchmarks.
Video Reasoning Benchmark
Video General Benchmark
Models
Frames
VSI-Bench VideoMMMU MMVU (mc)
MVBeach TempCompass
VideoMME (wo sub)
GPT-4o
15
34.0
61.2
75.4
71.9
LLaMA-VID
41.9
45.6
VideoLLLaM22
44.8
54.6
47.9
LongVA-77
29.2
23.9
56.9
52.6
VII.A-1.5-8B
25
28.9
20.8
58.8
VILA-1.5-40B
[6]
31.2
34,0
60.1
Video-UTR-7B
[0
58.8
59.7
52.6
LaaVA-OmeVision--
32.4
56.7
Kangaroo-8B
26
61.1
62.5
56.0
Qwen2.5-VL-7B (Col)
16
27.7
47.8
59.2
57.4
72.2
53.1
Qwen2.5-VL-7B-SFP
16
31.8
47.4
61.3
59.4
69.2
52.8
Qwen2.5-VL-77 (CoT)
32
30.1
48.1
60.0
59.0
72.6
56.6
Qwwe225-VI--BBSFT
33.3
63.5
60.5
69.9
55.4
Qwen2.5-VI-7B (CoT)
64
31.4
50.4
60.0
59.2
72.9
59.6
Qwen2.5-VL-7B-SFT
64
34.8
49.4
61.6
60.6
70.0
58.8
Video-R1-7B
16
34.6
49.8
64.2
62.7
72.6
57.4
Video-R1-7B
32
35.8
52.3
63.8
63.9
73.2
59.3
Video-RI-7B
37.1
52.4
63.8
64.8
61.4
Table 2: Ablation Study.
Video Reasoning Benchmark
Video General Benchmark
Models
Frames
VSI-Bench VideoMMMU MMVU (mc)
MVBench TempCompass VideoMME (wo sub)
Vidco-R1-7B-ww-immac
16
32.3
45.8
60.6
60.9
69.8
53.8
Vidbo-Rl-7B-wo-tcmporr
32.7
48.3
62.1
61.1
71.3
54.5
Video-R1-7B-22
16
31.8
49.5
63.8
60.4
70.9
53.8
Video-RI-7B
34.6
49.8
62.7
72.6
$7.4
More Frames Lead to Better Reasoning. When increasing the input frame number, we observe performance improvements almost all benchmarks. This indicates that richer context and temporal information contribute positively to the reasoning performance. Therefore, developing models capable of understanding and reasoning over longer video inputs is promising direction for future research.
4.3
Ablation Study
In this section, we conduct an ablation study by designing three variants our model: Video-RI- 7B-wo-image, which removes all image-based data during training and relies solely on video data; Mido--Rl-7B-wo-temponn
which replaces our proposed T-GRPO algorithm with the original GRPO method; Video-RI which skips the SFT cold start and directly conducts RL training.
As shown in Table both ablated models perform worse than the full Video-RI-7B across all benchmarks. In particular, removing image data leads to noticeable drop in performance on both video reasoning and general benchmarks, indicating that image- based samples play a crucial role in bootstrapping general reasoning ability. Similarly, without temporal -wwrre training via T-GRPO, the model struggles fully leverage temporal cues, resulting in weaker performance on benchmarks. Additionally, skipping the SFT cold start leads to worse performance, underscoring the value of our Video-RI Video-R1-CoTT-633 -Co--16kkk dataset and the importance of initializing with SFT before RI. training. These ablations validate the effectiveness of our proposed methods.

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0.75
0.75
450
Accuracy Reward
0.70
Temperal Reward
0.70
Response Length
400
0.65
0.65
330
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250
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Steps
Steps
(a) Accuracy reward
(b) Temporal reward (scaled)
(c) Response length
Figure 5: RL training curves.
4.4 Training Curves
Figure fillustrates the RL training dynamics of Video-R1. As shown in Figure 5a), the accuracy reward exhibits generally upward trend, indicating that the model continuously improves its ability to produce correct answers under reinforcement learning. In Figure [)g the temporal reward Fr (scaled 0~1 for visibility) also demonstrates a steady increase. This suggests that the model is progressively adopting more temporally grounded reasoning strategies with T-GRPO, Interestingly, the response length in Figure drops initially during RL training, then rises and stabilizes. We guess that this may reflect learning transition: the model first discards its sub-optimal SFT reasoning style, and eventually settles on new reasoning policy.
4.5 Effect of Temporal Reward Analysis
Video-R1-wo-temporr
60.2%
To further assess the impact of T-GRRO, measure the percentage of responses that incorporate temporal rea- soning for questions requiring it. Specifically, Qwen2.5-VL-72D to identify all temporally grounded questions across six benchmarks, and then Qwen2.5- VL-72B to evaluate whether the model responses demon- strate temporal reasoning. The evaluation prompt can be found in Appendix C.2) As shown in Figure 6 Video-RI R1 trained by T-GRPO incorporates temporal reasoming 75.0% of responses, compared 60.2% for Video-RI- Mideo-RI-wo-tepppe -o-tteommporal, which lacks tempo- ral modeling. This clear gap demonstrates the effectiveness of T-GRPO in encouraging the model to leverage temporal information during reasoning.
Vid Vo-eo-11
75.0%
Percentage
Figure 6: Percentage of temporal reason- ing responses.
Average Performance
We also conduct a sensitivity analysis on the magnitude of the temporal reward, controlled by the hyperparameter
a, as shown in Figure 7 The average performance is cal- culated the mean performance across six benchmarks.
40
We observe slight drop in performance at a 0.1 and 0.3 yield similar and
30
0.4, while a 0.2 and
favorable results. This indicates that the model is relatively insensitive to the choice of within reasonable range.
0.1
0.2
0.3
0.4
Figure Sensitivity analysis.
Conclusions
In this work, we present Video-R1, R1, as an attempt to investigate the RI paradigm to enhances video reasoning in multimodal large language models. Motivated by DeepSeek-Re we extend rule-based reinforcement learning to video by introducing T-GRPO, temporal- aware extension of GRPO that explicitly encourages temporal reasoning (iteo-R1-CoT-T for SFT cold start and Video-R Tideo-R1-2660 1-260k for RL. Experimental results across six benchmarks validate the effectiveness of our approach. We hope this work provides foundation for further research in video reasoning with MLLMs.
video. To support training, we curate two datasets,

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Acknowledgment
This work is partially supported by the National Natural Science Foundation of China (Grant No. 62306261), and The Shun Hing Institute of Advanced Engineering (SHIAE) Grant (No. 8115074). This study was supported in part by the Centre for Perceptual and Interactive Intelligence, a CUHK- led InnoCentre under the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Govemment. This work also partially supported by Hong Kong RGC Strategic Topics Grant STG1/E-443/24N, and CUHK-CUHK(SZ)GGDSS)) Joint Collahoration Fund YSP26-4760949.
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A Additional Experiments
A.1 Scaling Up RL Training
In previous experiments, our Video-RI is trained with 1k RL steps, similar to prior works that apply the RI paradigm other domains 7 29 To further explore the impact of scaling up reinforcement learning, we extend the training to 10k RL steps, As shown in Table 3 the performance of Vidco-RI generally improves on various benchmarks with more RL training steps. These results demonstrate the effectiveness of large-scale RL training and suggest that additional training can further enhance the model's reasoning capabilities.
Table Results on more RI training steps.
Video Reasoning Benchmark
Video General Benchmark
Models
Frames
VSI-Bench VidcoMMMU MMVU (mc)
MVBench TempCompass VideoMME (wo sub)
Video-R1-7B steps)
16
34.6
49.8
64.2
62.7
72.6
57.4
Vidco-RI R1-BB (Ik steps)
32
35.8
52.3
63.8
63.9
73.2
59.3
Video-R1-7B (Ik steps)
64
37.1
52.4
63.8
64.8
73.2
61.4
Video-RI-7B (10k steps)
16
34.5
50.7
64.2
63.7
73.7
57.2
Video-RI R1-eo-R1-BB (10k steps)
32
35.7
52.7
66.2
64.7
74.2
59.3
Video-RI-72 (10k steps)
64
37.8
51.4
65.0
65.5
74.2
61.8
A.2 Effect of Length Reward Analysis
To evaluate the impact of the proposed length reward, we conduct an ablation study by comparing Video-RI with a variant trained without the length reward, denoted as Video-RI-wo-len. shown in Figure (()), removing the length reward leads to clear decline in response length during RL training, whereas Video-R1 maintains steadily increasing length that eventually stabilizes a higher level. Finally, Video-RI Rl-wo-len achieves lower average performance across benchmarks compared to Video-RI, as shown Figure S a). We guess that this is because the model fails to preserve a deep reasoning style without the length reward during training, instead favoring shorter and potentially less informative responses. This likely results in weaker generalization during evaluation. These findings highlight the importance of encouraging the model to maintain moderate level of reasoning effort during training.
450
Video-R1 R1-wo-en
Response Length
400
Average Performance
350
40
300
250
30
200
200
400
600
800
1000
20
Video-R1
Video-R1-wo-len
Steps
(a) Performance comparison
(b) Length eurve comparison
Figure 8: Effect length reward.
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B
Reasoning Examples
Video:
Question:
Which move motion video system energy?
<thinkolet me think about this for moment. The question is asking about which move the video loses kinetic energy. To snswer this, I need to corefully analyze the video and identify the different actions and their cutcommss.
Video-R1-78:
First, lef's break down the video content. The video shows a series of actions involving pool table, a bowling alley, and crosh. In the pool toble scene, ployer takes shot, and the balls oround, the bowling dilyy collide.
ball rolls down the
lone hits
the
New, let's
enengy
scene-
In pool scene, the player takes a shot,
bells move. kinetic energy is transferred
cue stick
bolls, the bolls continus move, This does not seem to loss kinetic enensy.
2. In the bowling alley scene, the ball rolls down the lone and hits the pirs. The ball loses some kinetic energy upon hitting the pins, but still has some energy left as it rolls away.
3. In the car crash scane, the two cars collide. The kinctic energy is transfarred between the cors, and some energy lost as heat and deformation tion of the cars, This sigrificant loss of kinetic energy. Hmm, seems that the crash di dissipated in Rensoning)
the most kinetic is lost. The collide, and the heat and deformation, which significant less companed the other (Percaption
the
sse, car crash scene the where the most kinetic energy lest, The other scenes involve transfers energy. but the car crash involves significant loss.
Let's is verify this. The car crash scanne indeed the one where the most kinetic energy is lost, os the cors collidi and the energy is dissipated in dramatic way. (Reflectioo)
the move that loses the most kinetic
the video the thhnno
conswer- The crash
loses the system energy s/answer>
Figure 9: An example of Video- R1-7''s reasoning output on MMVU Benchmark.
Video:
Question: Why did leave before responding Rochael? A. He really had to pee. B. He forget he left his stove on at home.
C. He left his dog tied outside.
Because he updet his feelings for her not neciprocated. E. He being called someone outside.
<thinloLet me think about this for moment. The question asking why Ross left before responding Rachoel, options previded give us a range possible ressons, and we need consider each carefully.
Video-RL R1-78:
First, let's break down the context. Ross and Rachael are likely in romantic relationship. and Ross has been feeling he left before responding to her.
something for Rachael. The question
Option really not significont enough recson to leave before responding.
pee." seems unlikely becouse not common reason for leaving conversation abruptly.
Option B: "He forgot he left his stove on home." This is olso unlikely. While it's possible he could have forgotten, It's
typical for leaving conversation.
Option left his dog tied up outside." This possible, but it's not common neason for loaving conversation, IV's mone of a practical issue nother than a significant emotional reason.
Option D: 'Becouse he upset his feelings for hen not neciprocated. This soems plausible. If Ross upset that before responding to
feelings were not reciproccted, might
avoid further emotional pain.
Option "He was being called by somcone outside." This is possible, but it's not common reason for leaving a conversation. It's more of a practical issue nother than significant emational recson.
Hrmm, after considering all the options, Option seems likely couse Ross to leave before responding to Rachoel, (Perception and Reasoning)
plousible, significant emotional reason that would
Oh. see. The most logical reason for Ross to leave before responding to Rachael is that he was upset his feelings for her were reciprocoted. Let's verify this one lest time. Yes, fits the context and the emotional stote of Ross, (Reflection) </thinlo <onswer>bx/nne Dansseers
Figure 10: An example of Video-R1-7B's reasoning output on MVBench.
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C Prompt Template
C.1 Prompt Template for Training and Inference
Figure illustrates the prompt template for training and inference of all models. We also use this prompt for the COT annotation.
Prompt Template for Training and Inference
(Question)
Please think about this question as if you were human pondering deeply, Engage in an internal dialogue using expressions such as 'let me think', 'wait', 'Hmm'. oh, see'. 'let's 'et's break it down', etc, or other natural language thought expressions. It's encouraged to include self- reflection or verification in the reasoning process. Provide your detailed reasoning between the <thinlo and </thinko tags, and then give your final answer between the <answer> and </answer> tags.
TemplatT)
"multiple choice": Please provide only the single option letter (e.g. A, C, D, etc.) within the konswer c/answer> tags.
"numerical": Please provide the numerical value (e.g.. 42 or 3.14) within the sanswer </answer> togs. from the image/video clearly and provide your
"OCR": Please transcribe
within <answer>
</answer> tags.
"free-form": Please provide your answer within canswers </onswer> tags.
"regression": Please provide the numerical volue (e.g., 42 or 3.14) within the <enswer> </onswer> tags.
Figure 11: Prompt template for training and inference.
C.2 Prompt Template for Temporal Reasoning Evaluation
Figure iillustrates the prompt template for temporal reasoning evaluation.
Prompt Template for Temporal Reasoning Evaluation
(Question)
You are given question about video and response to that question. Your task assess whether the response engages with any aspect of the video's temporal structure that is, any elements related to time, sequence, progression. This may include references to actions occurring in particular order, events unfolding over time, descriptions of what happens before after, mentions duration, speed, direction, any general sense of temporal flow conveyed the response. Consider broad range interpretations, including responses that reflect understanding of how events evolve or follow another. Provide your reasoning between the <think and </thinko tags, and then write either 'yes' 'no' between the <answer> and </answer> tags.
Figure 12: Prompt template for temporal reasoning evaluation.
D Additional Implementation Details
We use the Adam optimizer with learning rate of 1e-6 to train our model. The SFT stage takes approximately 40 hours per epoch, while RL stage takes around 15 hours for steps. The hyperparameter 3 in the KI. divergence term of the GRPO algorithm is set to 0.04. To ensure training stability, we apply a weight decay rate of 0.01 and clip the maximum gradient norm 5. The maximum response length is set to 768 tokens.
23

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E Limitations and Future Works
We envision this work a foundation for advancing research video reasoning with MLLMs. Below, we outline its limitations and potential avenues for future work:
Increasing Frames Number. Currently, our model is trained with 16 video frames, which may limit its ability to handle long-range temporal dependencies. In future work, we can develop more efficient training and inference strategies that allow scaling to longer videos, enabling more comprehensive temporal reasoning.
Better Temporal Modeling Method. Although T-GRPO introduces effective temporal- aware reasoning, it brings additional computational overhead due to contrastive evaluation and reward calculation. This could be mitigated through inference acceleration framework such as vL..M or by exploring more efficient mechanisms for temporal modeling.
Adaptive Response Length Control. Our current length control mechanism applics fixed reward within predefined range, regardless of the complexity of cach sample. Future work could explore dynamic length control strategies, where the model adaptively determines the appropriate response length based on the difficulty type of the question.
Refined Image-to- reasoning data in a straightforward manner by mixing into the training set. Future research could design more principled approaches for leveraging image data to more effectively transfer reasoning ability from images to vidcos.
Knowledge Transfer. At present, we incorporate image- image-hased
Generalist Video Reward Modeling. Currently, we use rule-based reward functions tailored to different tasks. A promising future direction is to develop generalist video reward model, which is capable of providing consistent and scalable reward signals across various video reasoning tasks. Such a model could reduce reliance on handcrafted rules and enable broader applicability of reinforcement learning in video understanding.
24


Reviews:
[{"review_id": "IeYNuRqZyG", "reviewer": "Reviewer_Exe4", "summary": "This paper introduces Video-R1, a framework that systematically explores the R1 paradigm to enhance video reasoning in large multimodal models. The authors identify two main challenges when applying the GRPO algorithm to video reasoning: (1) insufficient reward for temporal reasoning, which can lead to shortcut solutions, and (2) a lack of high-quality video reasoning data. To address these, they propose the T-GRPO algorithm, which incentivizes the use of temporal information by rewarding the model only when it performs better on temporally ordered frames compared to shuffled ones. Additionally, they construct two new datasets: Video-R1-CoT-165k for SFT and Video-R1-260k for RL. Experimental results show that Video-R1 significantly improves performance on both video reasoning and general video benchmarks.", "questions": "Please refer to weakness part.", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\nVideo-R1 is a representative early exploration of video reasoning with large multimodal models (open-source), distinguished by its strong performance. In addition to the benchmark results reported in the paper, Video-R1 also achieves top performance on recent released benchmark [a]. The proposed T-GRPO algorithm is both simple and effective, encouraging the model to reason more deeply about temporal relationships in video data.\n\n[a] Video-Holmes: Can MLLM Think like Holmes for Complex Video Reasoning? arXiv: 2505.21374.\n\n### Weaknesses\n\n- 1. It seems that the paper does not report the original Qwen2.5-VL-Instruct performance. Please include this baseline using the same inference parameters as your model. Qwen2.5-VL-7B-Instruct should also be competitive. This comparison is essential for readers to clearly understand the benefits of your approach.\n\n- 2. Given access to 8x80G GPUs, why not train on longer sequences? With Qwen-style smart resize, the model should support at least 24k sequence length (approximately 240 frames) during training. To convincingly demonstrate improvements in temporal reasoning, please train and evaluate the model on more frames and provide comparative results.\n\n- 3. Concerns with T-GRPO Algorithm Setting:\n    - a. Since training is limited to 16 frames, longer videos (e.g., >2 minutes. BTW, please specify the video durations in Video-R1-CoT-165k and Video-R1-260k) will lose significant temporal correlation. This may make the distinction between temporally ordered and randomly shuffled frames ambiguous, potentially weakening the proposed temporal reward.\n    - b. The rationale for why temporally ordered frames should yield higher rewards is unclear. If the model can reason correctly from randomly shuffled frames, does this not indicate even stronger temporal reasoning, as it can \"recover\" the original order?\n    - c. While most video LLMs use causal visual token order, some employ full-attention encoders, making it difficult to guarantee that token order reflects true temporal order. Your approach is based on Qwen2.5VL uses a single-frame encoder, ensuring visual order matches temporal causality. Please clarify that in your methods section.", "was_revised": true, "final_justification": "I am satisfied with the authors’ response, which addresses my main concerns. Video-R1 represents an early exploration of video RLVR, with contributions in performance, data, and methodology. Accordingly, I have increased my rating.", "rebuttal": "> Q1: It seems that the paper does not report the original Qwen2.5-VL-Instruct performance. Please include this baseline using the same inference parameters as your model. Qwen2.5-VL-7B-Instruct should also be competitive. This comparison is essential for readers to clearly understand the benefits of your approach.\n\nA1: Thanks for your advice. We include this baseline for comparison as follows: \n\n|                        | Frames | VSI-Bench | VideoMMMU | MMVU(mc) | MVBench | TempCompass | VideoMME (wo sub) |\n| ---------------------- | ------ | --------- | --------- | -------- | ------- | ----------- | ----------------- |\n| Qwen2.5-VL-7B-Instruct | 16     | 30.2      | 47.2      | 61.9     | 60.2    | 71.6        | 55.8              |\n| Video-R1               | 16     | 34.6      | 49.8      | 64.2     | 62.7    | 72.6        | 57.4              |\n\nFrom the above table, we can find that our Video-R1 still outperforms Qwen2.5-VL-Instruct, illustrating the effectiveness of our method. We will add this baseline in the next version.\n\n\n\n> Q2：Given access to 8x80G GPUs, why not train on longer sequences? With Qwen-style smart resize, the model should support at least 24k sequence length (approximately 240 frames) during training. To convincingly demonstrate improvements in temporal reasoning, please train and evaluate the model on more frames and provide comparative results.\n\nA2：Thanks for your comments. Unlike standard SFT, training models with GRPO demands significantly more computational resources due to several factors: 1. Multiple sampling per question (usually more than 8) 2. Loading a reference model $\\pi_{ref}$ with the same size as the reasoning model for calculating the KL divergence 3. Long COT output (700~1000 tokens for some questions) . As a result, we encounter **out-of-memory (OOM) issues when training with 64 or more frames** on 8×80GB GPUs. \n\nTo further demonstrate our method’s effectiveness with longer sequences, we have trained the model on 48 frames and evaluated it on 64 and 128 frames. The results are as follows:\n\n|          | Train Frames | Evaluation Frames | VSI-Bench | VideoMMMU | MMVU(mc) | MVBench | TempCompass | VideoMME (wo sub) |\n| -------- | ------------ | ----------------- | --------- | --------- | -------- | ------- | ----------- | ----------------- |\n| Video-R1 | 16           | 64                | 37.1      | 52.4      | 63.8     | 64.8    | 73.2        | 61.4              |\n| Video-R1 | 48           | 64                | 37.6      | 52.8      | 65.0     | 65.3    | 74.0        | 62.9              |\n| Video-R1 | 48           | 128               | 38.3      | 52.8      | 66.2     | 65.7    | 74.2        | 63.8              |\n\nWe can find that training and evaluating on more frames generally yields better performance. We will explore experiments on more frames in the future.\n\n\n\n> Q3：Since training is limited to 16 frames, longer videos (e.g., >2 minutes. BTW, please specify the video durations in Video-R1-CoT-165k and Video-R1-260k) will lose significant temporal correlation. This may make the distinction between temporally ordered and randomly shuffled frames ambiguous, potentially weakening the proposed temporal reward.\n\nA3: Thanks for your comments. We provide the duration statistics of our datasets in the following table:\n\n|                   | Average duration | Percentage of Videos < 2 min |\n| ----------------- | ---------------- | ---------------------------- |\n| Video-R1-CoT-165k | 38.78 s          | 94.0%                        |\n| Video-R1-260k     | 36.87 s          | 94.9%                        |\n\nAs shown, the average video length in both datasets is approximately 40 seconds, and over 94% of the videos are shorter than 2 minutes. This suggests that sampling 16 frames still allows the model to capture a reasonable portion of the temporal structure in most cases. We agree that incorporating more frames for  continuous temporal modeling could further improve performance for long video, and we consider this a promising direction for future work.\n\n\n\n> Q4： The rationale for why temporally ordered frames should yield higher rewards is unclear. If the model can reason correctly from randomly shuffled frames, does this not indicate even stronger temporal reasoning, as it can \"recover\" the original order?\n\nA4：Thanks for your comments. In practice, the model usually tends to take shortcuts for reasoning rather than trying to employ/recover the temporal order. Specifically, it often relies on spurious cues, such as a single frame or snapshot, to guess the answer, while neglecting the overall temporal information. We observe that the model can sometimes arrive at the correct answer through such incorrect reasoning paths. Under GRPO, these paths are rewarded equally, which can lead to suboptimal policies and poor generalization. \n\nTherefore, we explicitly design the reward signal to favor trajectories derived from temporally ordered inputs, encouraging the model to learn policies that capture temporal structures rather than rely on spurious cues. One contemporary work [1] in July 2025 also adopts a similar approach in image reasoning domain, which contrasts the response probabilities between normal image input and corrupted image input.\n\n\n\n[1] Perception-Aware Policy Optimization for Multimodal Reasoning\n\n\n\n> Q5： While most video LLMs use causal visual token order, some employ full-attention encoders, making it difficult to guarantee that token order reflects true temporal order. Your approach is based on Qwen2.5VL uses a single-frame encoder, ensuring visual order matches temporal causality. Please clarify that in your methods section.\n\nA5: Thanks for your advice. We will clarify this in the paper."}, {"review_id": "AWm4qeRkNT", "reviewer": "Reviewer_d9TR", "summary": "This paper proposes a variant of GRPO for video understanding, enhancing temporal reasoning. The authors claim that existing multi-modal large language models do not fully utilize the temporal information. To address this, the authors introduced Video-R1-260K and Video-R1-CoT-165K curated datasets. Following DeepSeek-R1, the authors used rule-based rewards depending on data types. As presented in Figure 3, the variant of GRPO, T-GRPO, utilized randomly shuffled videos and their outputs. Based on the relation between responses from original and shuffled videos, rewards are differently augmented. Also, if the length of the response is reasonable, a higher reward is given. This simple variant provides considerable performance gain.", "questions": "1. Are the responses to original and shuffled videos paired? \n2. How did you curate newly introduced datasets?\n3. How sensitive is the proposed method to the additional reward values for temporal reasoning, and length in (2) and (5)?\n4. Regarding temporally augmented reward defined in (2) why r_i  is given when the response is not correct?\n5. Is the proposed method applicable to other temporal data? applications?", "limitations": "Yes.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n1. **Simplicity.** The proposed method is a simple modification of the GRPO using data augmentation and modified rule-based reward functions with additional rewards when the rate of correct responses of the original data is higher than shuffled videos. Also, the responses have reasonably lengths, the authors give a higher reward. This is extremely simple and applicable to various criteria. \n2. **Motivation.** After the advance of DeepSeekMath trained with GRPO, numerous papers have studied variants of GRPO. In MLLMs, or LLM-based agents, post-training became important to achieve competitive performance in downstream applications. Especially, the \n\n**Weaknesses**\n\n1. **Limited technical contribution.** The simplicity of solutions is desirable but no theoretical analysis or derivation is provided. Also, the relation between the proposed method and other alternatives are not discussed. GRPO is relatively new but many variants of GRPO have been studied. Without comparing the proposed method with existing GRPO variants and discussing the difference, it is difficult to the novelty of this work.\n2. **Hyperparameters.** The manually designed reward functions and additional rewards entail hyperparameters. Presumably, this work is sensitive to those hyperparameters. No in-depth analysis is provided and large dependency on hyperparameters raises questions on generalizability of this work to a new data or model.\n3. **Curated data.** This work requires another non-trivial manual effort to construct the dataset. The authors provided Video-R1-260K and Video-R1-CoT-165K datasets. These datasets are curated. To this extent, this work improves the performance with more annotations. As Table 1 shows mixed results. Qwen2.5-V(CoT) is better than SFT in two Video General Benchmarks: TempoCompass and  VideoMME (wo sub). However for other benchmarks, it is opposite.", "was_revised": true, "final_justification": "My all major questions are answered and concerns are addressed by rebuttal except for the limited contribution. I read other reviewers comments and I believe that this work is timely developed to improve the generalization power on temporal data. This work will provide useful insight to improve alignment methods with various data augmentation.", "rebuttal": "> Q1：**Limited technical contribution.** The simplicity of solutions is desirable but no theoretical analysis or derivation is provided. Also, the relation between the proposed method and other alternatives are not discussed. GRPO is relatively new but many variants of GRPO have been studied. Without comparing the proposed method with existing GRPO variants and discussing the difference, it is difficult to the novelty of this work.\n\nA1：Thanks for your comments. Several contemporary works have also explored variants of GRPO, either in general domains [1,2] or in specific domains such as agents [3]. For example, DAPO [1] introduces a clip-higher strategy and a dynamic sampling policy to improve LLM reasoning performance. GiGPO [3] proposes a two-level structure (episode-level and step-level) for estimating relative advantages to facilitate more effective RL training for agents.\n\nDifferent from them, we are **the first to explore a tailored GRPO algorithm for video reasoning**, in order to encourage deep temporal reasoning instead of relying on superficial visual patterns. Our ablation studies and temporal reward analysis experiments confirm the effectiveness of the proposed method. We leave more in-depth theoretical analysis for future work. \n\nTo further validate the effectiveness of our method, we compare our T-GRPO with DAPO. The results are as follows:\n\n|                     | Frames | VSI-Bench | VideoMMMU | MMVU(mc) | MVBench | TempCompass | VideoMME (wo sub) |\n| ------------------- | ------ | --------- | --------- | -------- | ------- | ----------- | ----------------- |\n| Video-R1 (DAPO [1]) | 16     | 31.7      | 49.0      | 62.9     | 61.6    | 69.1        | 56.3              |\n| Video-R1 (T-GRPO)   | 16     | 34.6      | 49.8      | 64.2     | 62.7    | 72.6        | 57.4              |\n\nFrom the above table, it can be observed that the model trained by our proposed T-GRPO obtains better performance than DAPO. This further confirms the effectiveness of our method. We will add this discussion in the next version.\n\n[1] DAPO: An Open-Source LLM Reinforcement Learning System at Scale\n\n[2] Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning\n\n[3] Group-in-Group Policy Optimization for LLM Agent Training\n\n \n\n> Q2：**Hyperparameters.** The manually designed reward functions and additional rewards entail hyperparameters. Presumably, this work is sensitive to those hyperparameters. No in-depth analysis is provided and large dependency on hyperparameters raises questions on generalizability of this work to a new data or model. \n>\n> How sensitive is the proposed method to the additional reward values for temporal reasoning, and length in (2) and (5)?\n\nA2: Thanks for your comments. While large-scale sensitivity analysis can be computationally intensive for MLLMs, **we have already included an ablation study on the temporal reward in Eq. (2), as shown in Section 4.5 of our submitted manuscript.** The relevant information is as follows:\n\n| Temporal Reward                     | $\\alpha=0.1$ | $\\alpha=0.2$ | $\\alpha=0.3$ | $\\alpha=0.4$ |\n| ----------------------------------- | ------------ | ------------ | ------------ | ------------ |\n| Average Performance on 6 benchmarks | 55.4         | 56.8         | 56.9         | 55.1         |\n\nWe find that performance slightly declines when $\\alpha=0.1$ or $\\alpha=0.4$, whereas $\\alpha=0.2$ and $\\alpha=0.3$ result in consistently strong performance. This suggests that the model is robust to the choice of $\\alpha$ within a moderate range. \n\nBesides, we also add a sensitivity analysis of the length in Eq.(5). The results are as follows.\n\n| Min Length                          | $l_{min}=256$ | $l_{min}=320$ | $l_{min}=384$ |\n| ----------------------------------- | ------------- | ------------- | ------------- |\n| Average Performance on 6 benchmarks | 56.7          | 56.9          | 56.4          |\n| **Max Length**                      | $l_{max}=448$ | $l_{max}=512$ | $l_{max}=576$ |\n| Average Performance on 6 benchmarks | 56.5          | 56.9          | 56.8          |\n\nFrom the above table, we can see that our method is also insensitive to the choice of $l_{min}$ and $l_{max}$. However, completely removing the length reward leads to performance degradation, as discussed in Appendix A.2 of our manuscript.\n\n\n\n> Q3：**Curated data.** This work requires another non-trivial manual effort to construct the dataset. The authors provided Video-R1-260K and Video-R1-CoT-165K datasets. These datasets are curated. To this extent, this work improves the performance with more annotations. As Table 1 shows mixed results. Qwen2.5-V(CoT) is better than SFT in two Video General Benchmarks: TempoCompass and VideoMME (wo sub). However for other benchmarks, it is opposite.\n\nA3：Thanks for your comments. Interestingly, we observe that Qwen2.5-VL-7B-SFT performs worse than Qwen2.5-VL-7B (CoT) in some cases. This highlights the limitations of SFT, which tends to rely on memorization and may generalize poorly to unseen test data [1]. In contrast, after RL training with our GRPO-based method, the model achieves consistent improvements across all benchmarks, including both reasoning and general understanding tasks. This aligns with the conclusion from prior work [1]: *“SFT memorizes, RL generalizes.”*\n\nHowever, SFT still plays a critical role as a cold-start phase. Our ablation study in Table 2 of the manuscript shows that removing this phase (i.e., in Video-R1-zero, which skips SFT and directly applies RL) leads to inferior performance compared to the full SFT+RL pipeline. This highlights the value of our curated Video-R1-CoT-165k dataset. We hope our datasets can serve as a useful resource for future research in the community.\n\n[1]  SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training\n\n\n\n> Q4： Are the responses to original and shuffled videos paired?\n\nA4: Thanks for your comments. They are not paired. We respectively sample responses from the original and shuffled videos. If the proportion of correct answers in the original group is higher than that in the shuffled group, a positive temporal reward is assigned.\n\n\n\n> Q5：How did you curate newly introduced datasets?\n\nA5: Thanks for your comments. For dataset construction, we incorporated both image-based and video-based reasoning data. The image-based data was collected from a range of public datasets and selected to cover diverse domains (e.g., mathematics, spatial reasoning, and expert-level knowledge) and varying difficulty levels. Its purpose is to help the model acquire general reasoning skills in static contexts. For video-based data, our focus is on enabling temporal reasoning abilities, such as understanding event progression, motion dynamics, and frame-to-frame dependencies. We collect data from a variety of public datasets and carefully sample and balance the proportion of each subset. All samples in Video-R1-260k are standardized to the same format for consistency.\n\nTo facilitate an effective SFT cold start, we leveraged Qwen2.5-VL-72B-Instruct to generate Chain-of-Thought (CoT) rationales for the Video-R1-260k samples using a structured prompt (see Appendix C.1). After applying rule-based filtering to eliminate low-quality or inconsistent generations, we obtained a high-quality subset—Video-R1-CoT-165k—which we used during the initial supervised training stage.\n\nWe believe our carefully curated datasets, combining broad reasoning coverage and temporal reasoning supervision, provides a strong foundation for training multimodal LLMs on video reasoning tasks.\n\n\n\n> Q6：Regarding temporally augmented reward defined in (2) why r_i is given when the response is not correct?\n\nA6：Thanks for your comments. We follow the original GRPO reward design in Deepseek-R1, where $r_i$ includes both correctness reward and format reward. If the answer is incorrect but the format is valid (or vice versa), the reward $r_i$ is computed as 0 + 1 = 1. If both the answer and format are incorrect, the reward $r_i$ is 0 + 0 = 0. If both are correct, the reward $r_i$ is 1 + 1 = 2. We will clarify this in the final version.\n\n\n\n> Q7：Is the proposed method applicable to other temporal data? applications?\n\nA7: Thanks for your comments. In principle, our proposed method can be applied to other temporal tasks. We leave broader applications for future exploration."}, {"review_id": "9kUYU85Sr7", "reviewer": "Reviewer_oq4J", "summary": "In this work, the authors propose a Video-R1 paradigm for reinforcing video reasoning with MLLMs, inspired by the learning style of DeepSeek-R1. First, they build two reasoning datasets, i.e., Video-R1-CoT-165k and Video-R1-260k, respectively for SFT and RL training. Second, they introduce a T-GRPO learning method that leverages temporal modeling in RL to enhance video reasoning.", "questions": "Please see weakness section.", "limitations": "Please see weakness section.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "*Strengths\n\n1 The main contribution of this work is an early attempt on video reasoning. Different from the previous works, this paper systematically explores the R1 learning paradigm on video MLLMs,  based on SFT (with Video-R1-CoT-165k) and RL training (with Video-R1-260k). It would benefit video understanding community from basic perception towards deep thinking on complex video content.\n\n2 The claims of this work are basically supported by their experiments on six video benchmarks: VSI-Bench, VideoMMMU, MMVU, MVBench, TempCompass, and VideoMME. Especially on video reasoning benchmarks like VSI-Bench, VideoMMMU and MMVU, the proposed Video-R1 achieves a preferable performance than recent open-source models and baselines, showing its effectiveness.\n\n3 The structure of this paper is basically well organized. \n\n\n\n*Weaknesses\n\n1 The temporal reward is mainly based on frame order. The order-relevant supervision has been widely investigated in the video undestanding community such as [1-3].\n\n2 The evalution is mainly based on video question answering. There are more temporal-relavent tasks such as temporal grounding and object tracking, which should be further investigated like VideoChat-R1 [4].\n\n3 Small Concern: What is Qwen2.5-VL-7B (CoT)? Moreover, Qwen2.5-VL-7B (CoT) is generally worse than Qwen2.5-VL-7B-SFT on video reasoning benchmarks, while it is often better than Qwen2.5-VL-7B-SFT  on video general benchmarks such as TempCompass and VideoMME. Why this happens?\n\n*Reference\n\n[1] Shuffle and Learn: Unsupervised Learning using Temporal Order Verification, ECCV2016\n\n[2] Unsupervised Representation Learning by Sorting Sequences, ICCV2017\n\n[3] Self-Supervised Video Representation Learning With Odd-One-Out Networks, CVPR2017\n\n[4] VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning, arxiv 2025", "was_revised": true, "final_justification": "Thanks for the rebuttal. The authors have addressed most of my main concerns. I keep my original rating.", "rebuttal": "> Q1: The temporal reward is mainly based on frame order. The order-relevant supervision has been widely investigated in the video undestanding community such as [1-3].\n\nA1:  Thank you for pointing this out. Prior works have indeed explored temporal order as a form of self-supervised learning signal in video understanding tasks [1–3], such as predicting or verifying frame order. In contrast, our work investigates **how to leverage temporal order as a reward signal within a GRPO-based RL framework to improve video reasoning in MLLMs**. We will cite these related works and include a discussion of them in the final version.\n\n[1] Shuffle and Learn: Unsupervised Learning using Temporal Order Verification, ECCV2016\n\n[2] Unsupervised Representation Learning by Sorting Sequences, ICCV2017\n\n[3] Self-Supervised Video Representation Learning With Odd-One-Out Networks, CVPR2017\n\n> Q2：The evalution is mainly based on video question answering. There are more temporal-relavent tasks such as temporal grounding and object tracking, which should be further investigated like VideoChat-R1 \n\nA2：Thanks for your comments. As the first work to explore R1-style reinforcement learning for video reasoning in MLLMs, we primarily validate our method on video question answering, similar to prior work such as Vision-R1 [1]. In principle, our framework can also be extended to other temporally grounded tasks such as temporal grounding or object tracking. Due to the limited rebuttal time, we will investigate these tasks in the future to build a more general video reasoning model.\n\n[1] Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models\n\n\n\n> Q3：Small Concern: What is Qwen2.5-VL-7B (CoT)? Moreover, Qwen2.5-VL-7B (CoT) is generally worse than Qwen2.5-VL-7B-SFT on video reasoning benchmarks, while it is often better than Qwen2.5-VL-7B-SFT on video general benchmarks such as TempCompass and VideoMME. Why this happens?\n\nA3: Thanks for your comments. Qwen2.5-VL-7B (CoT) refers to the Qwen2.5-VL-7B-Instruct model directly prompted to generate COT reasoning responses without any additional training. In contrast, Qwen2.5-VL-7B-SFT is the same model fine-tuned on our curated Video-R1-CoT-165k dataset using SFT.\n\nInterestingly, we observe that Qwen2.5-VL-7B-SFT performs worse than Qwen2.5-VL-7B (CoT) on some general video benchmarks such as TempCompass and VideoMME. This highlights the limitations of SFT, which tends to rely on memorization and may generalize poorly to unseen test data [1]. In contrast, after RL training with our GRPO-based method, the model achieves consistent improvements across all benchmarks, including both reasoning and general understanding tasks. This aligns with the conclusion from prior work [1]: *“SFT memorizes, RL generalizes.”*\n\nHowever, SFT still plays a critical role as a cold-start phase. Our ablation study in Table 2 of the manuscript shows that removing this phase (i.e., in Video-R1-zero, which skips SFT and directly applies RL) leads to inferior performance compared to the full SFT+RL pipeline.\n\n[1]  SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training"}, {"review_id": "SINmjr6PyV", "reviewer": "Reviewer_jHCR", "summary": "The paper introduces an R1-style framework for video reasoning task.\nThe main contributions, as mentioned in the paper, are as follows:\n1. Introduce T-GRPO, an adaptation of GRPO for temporal-based video reasoning.\n2. Video-R1, a Qwen-based model trained using T-GRPO for video reasoning.\n3.  2 datasets for cold starting and RL training on video reasoning tasks.\n\nThe paper provides detailed information about the T-GRPO algorithm. It uses the GRPO algorithm allong with an additional reward based on reasoning using the right order of frames. This forces the model to use temporal information during reasoning allowing for accurate outputs.\n\nThe contribution of the datasets allows for exapnding this methodology with outher foundation models as the backbone as well as addding to the pool of present data for future works. \n\nThe authors of the work undertake extensive experimentation on multiple video reasoning datasets.\nThe work shows a significant performance increase on various video reasoning datasets using their methodology, showing the viability of their method with a strong ablation study measuring the upliftment due to each part of their framework.", "questions": "The same as described in the weaknesses.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 2, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1.  The paper introduces a new framework and potential direction for video reasoning.\n2. The paper shows significant performance in various datasets.\n3. The work presents extensive experimentation and ablation study along with a detailed appendix.\n\nWeakness:\n1. Some variables in the equations are not clearly described or assume prior knowledge, making it difficult to follow. For example,\n    a. In Equation 2, it is unclear whether $R_i$ is calculated as the reward for each output or the overall set of outputs. \\\n    b. In Equation 3, $\\pi_{ref}$ is not defined when describing the GRPO algorithm.\n    \nWhile the definition of variables in other places can be inferred from context, it leaves room for ambiguity, leading to difficulty in reproducing the results.", "was_revised": true, "final_justification": "The authors have addressed my concerns and believe that the work shines light on a promising direction to push the field of video reasoning.", "rebuttal": "> Q1：Some variables in the equations are not clearly described or assume prior knowledge, making it difficult to follow. For example, a. In Equation 2, it is unclear whether  is calculated as the reward for each output or the overall set of outputs. b. In Equation 3,  $pi_{ref}$ is not defined when describing the GRPO algorithm. \n>\n> While the definition of variables in other places can be inferred from context, it leaves room for ambiguity, leading to difficulty in reproducing the results.\n\nA1: Sorry for confusing you. Regarding Equation (2), the reward $r_i$ is computed for each output $o_i$, not for the question $q_i$ as incorrectly stated in the current version. We will correct this typo and revise the explanation for clarity. For Equation (3), following DeepSeek-R1, $\\pi_{ref}$ refers to the policy of reference model used to compute the KL divergence. This term helps stabilize RL training by preventing the learned policy from drifting too far from the initial model. We will carefully revise the manuscript to ensure that all variables are explicitly defined and clearly explained."}]

Author rebuttal:

Meta-review: This paper proposes an R1-style training method for video MLLMs, adapting the GRPO algorithm to enhance temporal-based video reasoning.

The reviewers are generally positive about the work, acknowledging that: 1) R1-style training for video MLLMs is a timely and important topic, 2) the approach yields noticeable gains across multiple benchmarks, and 3) the paper is generally well-written and easy to follow.

The authors' rebuttal successfully addressed the few initial concerns raised by the reviewers. After carefully reading the reviews, the rebuttal, and all discussions, this meta-reviewer finds no reason to overturn the reviewers' unanimous recommendation. This paper is a clear accept.
Decision: Accept (poster)