Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding
Authors: Ye Wang
Ziheng Wang
Boshen Xu
Yang Du
Kejun Lin
Zihan Xiao
Zihao Yue
Jianzhong Ju
Liang Zhang
Dingyi Yang
Xiangnan Fang
Zewen He
Zhenbo Luo
Wenxuan Wang
Junqi Lin
Jian Luan
Qin Jin
Venue: NeurIPS 2025 poster ()
Keywords: large vision language model
temporal video grounding
reinforcement learning
post-training
Primary area: applications
TL;DR: We present Time-R1, a reinforcement learning-based post-training framework that achieves state-of-the-art performance across large vision language models for temporal video grounding.

Abstract: Temporal Video Grounding (TVG), the task of locating specific video segments based on language queries, is a core challenge in long-form video understanding. While recent Large Vision-Language Models (LVLMs) have shown early promise in tackling TVG through supervised fine-tuning (SFT), their ability to generalize remains limited. To address this, we propose a novel post-training framework that enhances the generalization capabilities of LVLMs via reinforcement learning (RL).
Specifically,  our contributions span three key directions:
(1) Time-R1: we introduce a reasoning-guided post-training framework via RL with verifiable reward to enhance capabilities of LVLMs on the TVG task. 
(2) TimeRFT: we explore post-training strategies on our curated RL-friendly dataset, which trains the model to progressively comprehend more difficult samples, leading to better generalization.
(3) TVGBench: we carefully construct a small but comprehensive and balanced benchmark suitable for LVLM evaluation, which is sourced from available public benchmarks.
Extensive experiments demonstrate that Time-R1 achieves state-of-the-art performance across multiple downstream datasets using significantly less training data than prior LVLM approaches, while improving its general video understanding capabilities. 
Project Page: https://xuboshen.github.io/Time-R1/.

Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding
Ye Wang Ziheng Wang**
Boshen Xu
Yang Du
Kejun Lin Xiangnan Fang' Zewen He?
Zihan Xiao
Zihao Yue
Jianzhong Ju²
Liang Zhang'
Dingyi Yang'
Zhenbo Luo²
Wenxuan Wang
Junqi Lin
Jian Luan²
Qin Jin
AIM3 Lab, Renmin University of China Project Page:
MiLM Plus, Xiaomi Inc .it..h.tthh.i... R1//
Abstract
Temporal Video Grounding (TVG), the task of locating specific video segments based language queries, is core challenge in long-form video understanding. While recent Large Vision-Language Models (LVLMs) have shown early promise in tackling TVG through supervised fine-tuning (SFT), their ability generalize remains limited. To address this, we propose a novel post-training framework that enhances the generalization capabilities of LVLMs via reinforcement learning (RL). Specifically, our contributions span three key directions: (1) Time-R1: we introduce reasoning-guides post-training framework via RL with verifiable reward to enhance capabilities of LVLMs on the TVG task. (2) TimeRFT: we explore post-training strategies on our curated RL-friendly dataset. which trains the model to progressively comprehend more difficult samples, leading to better generalization. (3) TVGBench: we carefully construct small but comprehensive and balanced benchmark suitable for LVLMM evaluation, which is sourced from available public benchmarks. Extensive experiments demonstrate that Time-R achieves state-of-the-arr performance across multiple downstream datasets using significantly less training data than prior LVLM approaches, while improving its general video understanding capabilities.
Introduction
Understanding long-form videos has long been a core ambition in computer vision [14, 25, 9]. A critical step toward this goal is Temporal Video Grounding (TVG) [15, 66], which enables models to localize video segments corresponding to natural language queries (e.g., "Find the segment where a person walks into the living room.") "). Such capability is fundamental for real-worke applications, including smart home assistants [60, 16, 49] and video retrieval systems online platforms [5, 3].
Traditional TVG approaches adopt feature-based paradigm, where pretrained models (c.g.. CLIP [45], I3D [6]) extract text and video features, followed by task-specific grounding mod- els [31, 23, 30). However, these methods suffer from error accumulation due imperfect pretrained features. To overcome these limitations, recent efforts have shifted toward end-to-end Large Vision- Language Models (LVLMs) [47, 65], which directly process long-form videos and text queries. Despite being pretrained on datasets 100x larger than domain-specific benchmarks [49], LVLMs (with 7B+ parameters) often underperform compared much smaller feature-based models (e.g.. 9M parameters [23]). This raises a critical question: Why do LVLMs, despite their vast pretrained knowledge, fail excel on TVG?
Corresponding author: Qin Jin; Equal contribution, listed in alphabetical order; $ Project lead.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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TimeRFT Training training strategy. dataset
Time-R1 Framework data-efficient, effective, Ri-like
TVGBench Evaluation
800 data, 11 types, halanced distributions
semantics
human
object
environment
Thanking
format
muiti- spoch
lofl Eltering
2.58
data
rewurd
Answer
distribution
length
video
domain
vidco
center
query
Improved Temporal Video Grounding (video length: 148s)
Question:
cutting board".
Truth
TimeSulte
23.0s
Gemini-2.5-Pru
Os
VideoCCtat-Plash
16.4s
19.2s
Time-81 (ours)
thinking (ours):
cutting
seconds.
person
placing the cutting board counter
Improved Short Video QA (videa length: 25s)
model thinking:
Given this sequence, the next logical letter would be the next letter in the alphabet after 'F., which is 'G. The letters 'D, 'E, and "G' are now on the table, lotter shown
D DEFG
formingg sequeecce 'DEPG: Therefore, be
Question: What letter likely to be shown next?
Time- R1 thinling (ours):
The sequence
suggests puttern showing the next letter in the alphabet. The letters are being placed specific order, and the next logical letter after "G" would be 'H'.
shown
video
(A)
[B]
(
Improved Long Video QA (video length: 59m)
Base model thinlcing:
The map used illustrate historical journey Du Ru, famous poel, as indicated by the test "Du Pu's ruute to Xi'nn and the histocical contest provided narratoc Given analysts, the expanding lines map mote a'an...
Question:
What do the expanding red lines on the map
Time-RI thinling (ours):
In the first fow minutes of the video stand for?
The map shown with red lines expanding from contral point, which likely Xi'an, major city the Silk Road, 3. The map is libeled with "Silk Road"
(A) The Yellow River
The Silk Road
confirming
represent
(
Xilan
Yangtze
Road...
Figure I: Our contributions include novel post-training framework for LVLMs via reinforcement learning, Time-R1; an RL fine-tuning strategy along with associated training dataset, TimeRFT: and new benchmark, TVGBench, for evaluating LVLMs on the TVG task. Our Time-R1 model not only achieves SoTA TVG performance, also enhances performance on both short- and long-form multi-choice video question answering tasks.
We attribute the suboptimal performance of LVLMs over werrpenalizztion of false negatives during supervised fine-tuning (SFT). For instance, when the ground truth timestamp [2s, 4s}, even when the model makes reasonable prediction of timestamp [1.9s, 3.9s], the autoregressive loss would still be undesirably high. Such disproportionate penalties on reasonable predictions result in overfitting and poor generalization. While previous solutions have attempted to address this introducing new timestamp tokens into the vocabulary [19, 17, 58] or by appending a regression head to predict timestamps [70], they often sacrifice the pretrained language capabilities of L.MMs.
Inspired by recent success in reinforcement learning (RL) for post-training LLMs [41, I] with chain-of-thought (CoT) prompting, especially in domains with deterministic answers, such as code generation and mathematical reasoning, we explore whether RL can serve as an effective alternative for TVG. Unlike SFT, RL allows direct optimization of task-specific metrics (c.g., IoU), thereby reducing rigid penalties autoregressive losses and encouraging plausible timestamp predictions. In this work, present an RL-based framework Time-RI R1 that effectively post-trains LVLMs for TVG and pushes the performance frontier. Our contributions include:
RL-based framework for temporal video grounding. We introduce Time-RI reasoning- enhanced post-training framework via RI. with verifiable rewards, where the LVLM first generates chain-of chain-of-though descriptions and then predicts timestamps. The post-training process is optimized

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target: min 19 yl GT y: [3.8, 4.2]
target: min ylogg
target: max [tloU[y 9) +)
GT y: [3.8, 4.2]
GT y: [3.8, 4.2] tio +
^ Pred 9: [3.7, 4.3]
Pred 5: [3.7, 4.3]
Pred 9: thinking; [3.7, 4.3]
Moment Decoder
Large Language Model
Large Language Model
Cross-Modal Fusion
t
Feature Encoder
Text
Visual Encoder
Visual
Encoder
Encoder
f 000... D00 video features
t
where my
where are my
where are my shoes?
shoes?
frames
shoes?
frames
(a) VLP
(b) SFT
(c) RL (ours)
Figure 2: Comparison of different approaches for the TVG task, including feature-based video- language pretraining (VIP) [39, 23], supervised fine-tuning (SFT) [44, 65], and RI. (ours).
using Generalized Reinforcement Policy Optimization (GRPO) with a novel reward function, incor- porating both a structured template reward and a timestamp-awarr tloU reward.
Time-aware reinforcement fine-tuning. We propose TimeRFT, a reinforcement fine-tuning strategy with dynamic hard sampling, which mines hard samples on a curated dataset and progres- sively selects low-IoU samples for multi-epoch training. To ensure stable reasoning and reduce hallucinations, we adopt a cold-start approach to generate CoT with video captions. To support RL-friendly training, we curate an RFT dataset with difficulty annotations on the TVG task.
Comprehensive benchmark for LVLMs on TVG. Existing TVG benchmarks are designed for the large-scale evaluation of small models. Considering the inference speed bottlenecks and general-purpose role of LVLMs, we construct TVGBench, a compact yet comprchensive benchmark for TVG. We carefully balance the video distribution, query distribution, and design specific query semantics to ensure that the benchmark is well-suited for evaluating LVLMs.
Aftt results and generalization. Compared with 7B LVLMs the temporal video grounding task, our method outperforms all prior SFT-based methods. After fine-tuning on downstream benchmarks, it surpasses many previous feature- based approaches. Furthermore, Time-R1 also improves the model's general video understanding on video QA benchmarks.
2 Related Works
Temporal video grounding. The TVG task [15, aims to localize temporal segments in untrimmed long videos given natural language queries. Previous works can be broadly categorized into feature- based video-language pretraining and frame-based LVLM methods, as shown in Figure 2. Feature- based methods first extract video and language features using pre-trained encoders (c.g.. I3D [6]. EgoVLP [30], CLIP [45], BERT [12] etc.), then build timestamp prediction modules based on multimodal fused features. Existing methods differ mainly in their design of the multimodal fusion module and timestamp prediction module. For example, SnAG [39] adopts late fusion strategy and regresses timestamps directly in a single forward pass without proposal generation. While effective, these approaches are fundamentally limited by the quality of the pretrained features. Recent efforts have shifted toward end-to-end frame-based methods by fine-tuning LVLMs using SFT with autoregressive losses [28, 47, 65, 20, 55, 19, 26]. For instance, TRACE [19] treats each event as combination of timestamp, saliency score, and caption, and fine-tunes the LVLM to generate event scquences autoregressively. However, such methods still underperform on even simple TVG benchmarks like Charades [49], often lagging behind cature--bsed approaches. In this work, we propose a novel RL-based post-training framework that establishes new state-of-the-an performance for LVLMs TVG tasks, even surpassing many feature-based methods.
RL in LLMs and LVLMs. RL is a foundational machine learning paradigm applied in diverse domains such as game playing [50], robotics [36], and increasingly, language and vision vision-language models [41]. RL updates models by interacting with environments and maximizing reward signals. In recent years, RL has profoundly affected the field of LLM and LVLM post-training which falls into two main categories: Reinforcement Learning with Human Feedback (RLHF) [42, 62] and Reinforcement Learning with Verifiable Reward (RLVR) [1. 41, 7J. Early works find that RLHF
3

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can align LLM to generate human preference data, which primarily reduces the safety risks in LLM and hallucination problems LVLM. For example, RLHF-V [62] collects fine- aie-grained pairs of incorrect and corrected captions and leverages direct preference optimization to optimize the model to generate correct captions, thus mitigating hallucinations. More recent works have explored RLVR in tasks with deterministic answers, which not only benefits mathematical problem solving and code generations in LLMs (e.g. DeepSeek-R [1], but also enhances the generalization of LVLMs across range of applications, such as visual grounding [34] and visual reasoning [51]. As a pioneer among open-source LLMs, DeepSeek-R RI [1] adopts GRPO to enhance reasoning capabilities by designing rule- based rewards that incorporate both reasoning templates and final answers. In the context of LVLMs, AM-Eureka [37] investigates multimodal image reasoning using GRPO, introducing an online filtering mechanism and two-stage training strategy to stabilize the optimization process. However, existing approaches remain confined to language [1, 61], image understanding [7, 51, 34, 37], and short video understanding [69, 29]. It remains unclear whether and how reinforcement learning impacts long-form video understanding. To bridge this gap, we take first step by introducing RLVR into LVLMs for the temporal video grounding task.
3 Method
The TVG task aims to temporally localize video segments within long-form videos based on natural language queries. Given a video of duration & seconds, which is represented as a sequence of T frames {. .T ). and language query q, the goal is to identify the temporal boundaries [t., Le]
of the segment that best corresponds to g, where l. le E IR+, In this work, we introduce Time-R1, a framework designed unleash the potential of LVLMs for the TVG task using RL. We first provide background on RL-based training for LLMs in Section 1. then detail the training procedure of Time- RI in Section 3.2. Next, we describe specific training techniques used in TimeRFT in Section 3.3, and finally, we present the construction of our evaluation benchmark TVGBBench in Section 3.4.
3.1 Background of GRPO: RL for LLM
As pioneer among open- sourese RI RI-style LLMs, Deepseek-R1 [1] leverages GRPO train the policy model Tyy (i.e., the LLM) to think before answering, making it particularly well-suited for tasks with well-defined answers, such mathematical reasoning. In the GRPO framework, given an input question the L.M samples G candidate responses o (or,-.... oG), ). and a reward function r((o)). GRPO encourages the LLM to generate responses that maximize a weighted sum reward R(o), defined by:
r(.) assigns reward score to each response, yielding (r(o1),-.. (er)),-..r((G)))
r(o:) - mcan((r(o.)) To(o:) Te (o.)
R(o)
std(tr(o.))
where (o) denotes the probability of LLM generating the response o, and Te represents the LLM parameters from recently optimized state. To ensure training stability and avoid large deviations from the original language model behavior, the final training objective incorporates KL-divergence regularization term [1], penalizing divergence between Ty and Tre
max T Rownosa (p) RR()) - 33DL (To [[et)
(2)
where 3 is scaling coefficient that balances reward maximization and policy stability. We omit the clipping operation for simplicity.
3.2 Time-R1: RL for Temporal Video Grounding
Since the TVG task has defined answers and well-established evaluation metrics, RL can optimize LVLMs for task-specific performance through tailored reward design. To enhance interpretability and align with human-like reasoning, additionally incorporate explicit "thinking process" prior to timestamp prediction. This process encourages the model to produce contextualized video descriptions that support its final decision. We detail reward modeling and training process below.
Reward modeling. The reward r; plays a crucial role in guiding the model's learning objective. To encourage effective temporal grounding with an explicit reasoning process, we design composite

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reward function comprising two components: the timestamp- aware Intersection over Union (IoU)
PetoU and the reasoning template reward forr-
Timestamp-aware IoU reward (+). The TVG task primarily uses IoU [63] to evaluate the quality of predicted segments against the ground-truth [t', (e). computed as:
t2, te] tt., tel 1 t.
IoU
where A B and AU B denote the union and intersection between sets and B, respectively. Optimizing for the IoU inherently encourages the LVLM to produce predictions that fall within permissible range of variation e, such that or c S t. e S t', or + e still yields high IoUs. This encourages the LVLM to focus more the semantic understanding of the event within possible temporal boundaries, rather than rigidly requiring exact temporal alignment like SFT. However, standard IoU may fail to accurately reflect the quality of temporal alignment in certain scenarios. For example, when the ground truth span is [0. 30] (i.e., the full video), any prediction covering more than 30% of the video would result in IoU greater than 0.3. A prediction like [10, 25] would yield an IoU of 0.5, which overestimates its quality despite incorrect timestamps. To address this issue, we introduce the timestamp-aware IoU (tloU) as corrective measure. tloU augments the standard IoU with penalties on timestamp deviations, defined as:
(o) = IoU -01 1. 11-11
L/ K.
(4)
This modification penalizes predictions that deviate from the reference timestamps relative to the video duration t. In the earlier example, the reward value changes from 0.5 (IoU) to 0.28 (tloU), providing more realistic signal for learning, Overall, tloU acts as a stricter and more informative reward signal, encouraging the I.L.M to develop deeper temporal understanding of events, rather than relying superficial shortcuts.
Reasoning template reward Tform (-). In TVG, the video segments relevant to a textual query typically comprise only small portion of the entire long video. For LVLMs, it is therefore suboptimal to directly predict timestamps without first engaging in reasoning process to identify the relevant content. Instead, the model should allocate its computational capacity toward reasoning over visual and linguistic cues to better understand the temporal context before making predictions. For instance, given the query "the man washes dishes", reasoning that the person likely a kitchen can improve temporal localization. Such context-aware inference supports more accurate and semantically aligned predictions. To encourage this behavior, we introduce template- based reasoning reward, which incentivizes the model to generate intermediate reasoning steps (structured in predefined format) prior to timestamp localization. The reasoning template reward requires the LVLM to structure its response like "<thiin>>.. </think> <answer><t, <t, to f </answe>>", formulated as:
0, if has wrong fromat 11 if o has correct fromat
Tform (0)
(5)
The overall reward is the sum of the two:
r(o) (o) + (ooem (0)
(6)
GRPO training. The LVLM F(.) takes the video frames FI,..., and the language query input and generates G candidate responses oI,..., oG. where each response is computed as F(z1...... Ix; 9). The reward for each response is calculated using Equation 1, and the model is optimized with the GRPO objective in Equation 2. To focus learning on the reasoning and localization
capabilities, we freeze the visual encoder and update only the parameters of the LLM during training.
3.3 TiT: Tiae-Aware RL-Friendly Fine-Tuning
Due to the high computational cost associated with RL training, explore data-efficient strategies to reduce sample requirements. To this end, we propose TimeRFT, which incorporates time-aware, RL-friendly dataset curation and fine-tuning techniques aimed at enhancing generalization while minimizing training overhead.
RL-friendly dataset curation. We construct the TimeRFT dataset by leveraging only TVG samples, and assign difficulty score to each sample based on the hase model's performance. small subset is then selected for subsequent RL training.
5

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Query Center
Video Length
bumnn pose
The old vittrnn's Sias.
human action
(umple)
Aperica vallas its
buman action
lhe zets dows on the
201
(comples)
ground and licks
The penon takoe our
human action
laik: from the drawer,
(precedural)
rissec it, then cats
of fix.
26
atrbbre
What color waethe
10.0 (9.6.)
103.04)
(8.4
0.5
07)
(0.000
1088
(09.1)
(C00. 1E2 MA0
(146) (160,
object counting
Rourteen devertam horn.
Query Semantic
Dataset
objjct
Neet placed intto
100
163
(simple)
licycke?
200
abject assthrre
Where the fastmet after
190
(comples)
Eiooeed it with my log!
190
change
covironment
acroengoss black.
call center showm.
HAP
HP
0C
060 055
10RIET 130
Figure 3: Statistics of TVGBench. TVGBench comprises 11 types of queries covering aspects related to humans, objects. and environments. As illustrated in the figure on the right, the distributions of query center, video length, and dataset source are designed to be as balanced as possible. This balanced construction allows for comprehensive evaluation of model performance across different dimensions, enabling fine-grained analysis along each axis during benchmarking.
Source data collection. Our training videos are sourced from Internet video datasets including YT-Temporal [59], DiDeMo [3], QuerYD [40], Intern [52], and HowTo 1 100M [38]. We obtain grounding data with annotations from VTG-IT [17], TimelT [47], TimePro [65], HTStep [2], and LongVid [28]. This process yields 339K temporal grounding samples.
RFT data filtering. We propose data selection strategy based on training difficulty to significantly reduce training costs while preserving strong generalization performance. Models trained only on easy samples (e.g., ToU 2 0.7) tend overfit, whereas training overly difficult samples (e.g., ToU = 0) often suffers from sparse reward signals, making it hard for the model to receive positive feedback. To strike balance, we select samples of moderate difficulty that more conducive to generalization during reinforcement fine-tuning. We first estimate difficulty score for each sample based on the performance of the base model. For grounding tasks, difficulty is quantified using the IoU between the predicted and ground-truth temporal regions. We then filter out samples that are either too easy or too hard. Specifically, we sample subset of data from a Gaussian distribution over the IoU axis centered at 0.3, resulting in set of 2.5K moderately difficult samples for RL training.
RFT training strategy. For selected difficult samples, the model may struggle to learn them in single pass. However, we argue that effectively mastering these challenging cases is essential for improving overall model performance. To this end, we employ multi-epoch training approach combined with per-epoch sample filtering, allowing the model to repeatedly focus on harder samples and gradually improve its understanding.
Dynamic hard sampling. We adopt multi-epoch training strategy coupled with per-epoch sample filtering to enhance learning from difficult examples. The model is trained over multiple epochs, and after each epoch, we exclude easy samples with an IoU greater than 0.7 that have become easy. This dynamic curriculum discourages overfitting easy instances while ensuring consistent exposure to harder samples, ultimately promoting stronger generalization.
Cold start fine-tuning with few CoT data. For smaller models (e.g., 3B parameters), we observe that directly training with RL to generate CoT responses often results in reasoning steps that are either unintelligible or hallucinated, which impairs answer quality. Additionally, the length of generated reasoning during early training stages is difficult to control, leading to an unstable training process. To address these issues, we introduce a cold-start fine-tuning strategy using a small set of CoT-ffrmmated examples that encourage grounded reasoning aligned with video content. Specifically, we guide the model to produce structured sequential captions with associated timestamps. The SFT template is defined as:
< think >< top to to C15 tey to teg C2 >< fo</thhng /think >< answer > to to to /answer > (7)
where C2 represent captions corresponding to video segments (la logJ, respectively.

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3,4 TVGBench: Evaluation Benchmark for LVLM on Temporal Video Grounding
Existing benchmarks for temporal video grounding either focus on large-scale datasets tailored for smaller models within specific domains (e.g.. human activities in ActivityNet) or consist of small, limited test sets (e.g., the 2K home activity samples in Charades) typically used for LVLM evaluation due to their slower inference speed. However, these benchmarks fall short in capturing the evaluation needs of LVLMs, which, despite slower inference, exhibit strong generalization capabilities. To bridge this gap, we introduce TVGBench, a lightweight yet comprchensive evaluation benchmark specifically designed for assessing the performance of LVLMs on temporal video grounding tasks.
Data sources. To ensure comprehensive evaluation, we construct our TVGBench with curating samples from five public benchmarks with a balanced distribution of data source: Charades- -Srra [49], ActivityNer-Captionn [5], HiREST [64], EgoNI.Q [16), and TaCoS [46].
Benchmark statisties. We carefully balance the video duration, video domain, query center and construct query semantics in TVGBBench to construct 800 instances, as shown in Figure 3.
Distribution statistics. Video durations in the dataset have balanced range from short clips up to 3 minutes long. To ensure temporal diversity, the center timestamps of queries are designed to be approximately uniformly distributed across the entire span of each video.
Query semantics. Since the original datasets do not provide semantic labels for queries, we manually define 11 semantic categories grouped under three major types: human, object, and environment. We leverage DeepSeek-V V3 [11] to annotate the semantic type of each query and ensure approximate balance across these categories. While most categories are evenly represented, the Human Action Simple (HAS) category is slightly overrepresented due to inherent dataset bias (simple indoor actions are more common). In such cases, we prioritize achieving a balance across datasets from different domains while maintaining semantic diversity, accepting skew in HAS.
4 Experiments
We first present experimental setup in Section 4.1. Then, evaluate our model from three key perspectives: (I) Comparison with state-of-the-art methods in Section 4.2: We evaluate our model across multiple TVG benchmarks to assess its performance against existing approaches; (2) Ablation studies and analyses in Section 4.3: We examine the individual contributions of each component in framework to better understand their roles in overall performance. We also compare RL and SFT strategies across TVG, short video QA, and long video QA tasks.
4.1 Experimental Setup
Benchmarks. We evaluate our model on wide range of benchmarks covering both temporal video grounding and general video understanding tasks, including: (1) Charades-STA [49] contains 6,672 long videos capturing indoor human activities. The official split for the TVG task includes 12,408 clip-query pairs for training and 3,720 for testing. (2) ActivityNer [5] comprises 20K long videos with an average of 3.65 clip-query pairs per video. Following previous work in fine-tuning setting [67, 23] for the TVG task, we use the standard dataset splits with 37.421 training, 17,505 validation, and 17,031 test samples. (3) MVBench [27] is a short video QA benchmark focused on temporal reasoning. It includes 4K QA pairs for 20 types of tasks. (4) TempCompass [33] assesses fine-grained temporal understanding with 410 short videos. We use all multi-choice QA tasks except for the video captioning task. (5) EgoSchema [35] features 5K egocentric video clips, each approximately minutes long, with temporally demanding QA pairs. (6) VideoMME [13] is a
general video QA benchmark covering diverse domains. It contains 2.7K QA samples over videos of varied lengths, ranging from 11 seconds to I hour. We use the long video split for evaluation.
Implementation details. Unless otherwise specified, we use Qwen2.5-VL-77 [4] as the base model. To strike a balance between training efficiency and memory consumption, we sample video frames at 2 FPS and adaptively resize each video input to contain approximately 2.8 million pixels. For instance, a 50-second video yields 100 frames, each with a resolution of roughly 96x96x3. During the reinforcement fine-tuning phase, we train for 5 epochs using a batch size of 8 and select the final checkpoint for evaluation. For fine-tuning on downstream benchmarks, we train for 2 epochs. More implementation details are provided in Appendix B.
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Table 1: Performance of temporal video grounding Charades-STA, NetivityNet, and TVGBench. The methods marked in gray" represent fine-tuning on corresponding benchmarks, while those in black indicate zero-shot settings. We compare Time-RI against existing 7B open-source LVLMs, as well as state-of-the-art VLP models.
Charades- STA STA
Activity Net
TVGBench
Type
Method
R1803
R1@0.5
R1607
R1603
R1600.5
R160.7
R180.3
R1605
R160.7
2D-TAN' [68]
57.3
45.8
25.0
UniVTG" [31]
72.6
60.2
38.6
56.1
43.4
24.3
VLP
SSRN' [71]
63.5
42.6
54.5
33.2
SnAG" [39]
30.6
EaTR' [23]
68.4
44.9
58.2
37.6
Gemini-2.5-Pro [10]
39.1
24.4
12.8
Momentor [43]
42.6
26.6
11.6
42.9
23.0
12.4
ChatVTG [44]
52.7
33.0
40.7
22.5
9.4
TimeChat [47]
32.2
13.4
36.2
20.2
9.5
22.4
11.9
VTG-LLM [18]
33.8
15.7
SFT
HawkEye [53]
10.7
VMinsL.LM [22]
51.0
27.5
11.4
44.0
27.8
14.3
VideoChat-TPO [57]
58.3
40.2
18.4
VideoExpert [70]
61.5
40.3
TimeSuite [65]
69.9
48.7
24.0
31.1
18.0
8.9
VidenMind [32]
73.5
59.1
31.2
48.4
30.3
15.7
VideoChat--lasn [28]
74.5
53.1
32.8
19.8
10.4
TRACH [19]
40.3
19.4
37.7
24.0
37.0
25.5
14.6
HawkEye" [S3]
72.5
58.3
28.8
55.9
34.7
17.9
TimeSuite" [65]
79.4
67.1
VidenChat- TPO" [57]
77.0
65.0
40.7
VideoExxent [70]
74.3
60.8
Time- (ours)
78.1
21.4
RL
Time- R1 (ours)"
82.8
72.2
50.1
73.3
55.6
34.0
(mtou Accurscy)
LORA
65.2
65.265.6
60
58.1
53.0
56.1
55.255.4
51.7
54.2
150
80
40.5
30
Evaluetion
22.9
25.9
20
10
ActivityNet
TVGBench
TempCoomess
EgoSchema
VideoMIME
TVG
short QA
long video
Figure 4: Comparison between post-training paradigms across various tasks, including short video QA, long video QA and temporal video grounding. Both "SFT" and "RL" full- full-finetune the LLM, while "SFT-LORA" denotes finetuning the LLM with LoRA [21]. The "Base" is Qwen2.5-VL-7B.
Evaluation metries. For TVG, following [47, 65], adopt the "Rl @m" evaluation protocol to compare with state-of-the-art models, which computes the percentage of samples where the top-1 predicted segment has an IoU greater than threshold m, with m (0.3, 0.5, 0.7). . For brevity, we also adopt mloU, which calculates the average IoU on all testing data as an alternative metric. For video QA, report accuracy as the evaluation metric.
4.2 Comparison with State-of-the-Art
We compare Time-RI R1 with tateeoffthheart TVG methods, including both traditional video-languse pre-training models (VLP) and recent large video- aadeo-language models fine-tuned via SFT.
Time-R1 surpasses SFT-based models in zero-shot settings. As shown in Table 1, in the zero-shot setting, Time-R1 demonstrates strong performance, outperforming SFT-based models that rely on large-scale supervision. Despite using only 2.5K samples for RL, Time-R1 achieves leading results across multiple benchmarks. On Charades-STA, Time-R1 attains an @0.3 score of 78.1, surpassing range of strong SFT models such VideoChat-Flaase (74.5), VideoMind (73.5), and TimeSuite

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(69.9), On ActivityNet, its RI @0.3 score reaches 58.6, which is also superior to other leading methods like VideoMind (48.4) and HawkEye (49.1). On our proposed TVGBench, its R1@0.3 score of 41.8 significantly exceeds all baselines, including TRACE (37.0) and the powerful closed-source model Gemini-2.5-Pro (39.1). These results highlight the data efficiency and strong generalization capabilities of our RL-based post-training approach.
Time-R1* outperforms all SFT-based LVLMS and many traditional VLP-based models. When fine-tuned on downstream benchmarks, Time-RI" consistently outperforms both traditional VLP- based and SFT-based models on the TVG task. On the Charades-STI dataset, its RI @0.7 score reaches 50.1 and its RI @0.5 score reaches 72.2, comprehensively outperforming all other methods, including highly competitive models like VideoChat-TPO" (40.7, 65.0) and TimeSuite" (43.0, 67.1). Notably, this superior performance is achieved using far fewer samples than the 349K SFT examples used by TimeSuite". On the more challenging ActivityNet dataset, Time-RI RI" also achieves state-of- the-art results, with an RI @0.5 score of 55.6 that is superior to fine-tuned SFT models like TRACE (37.7) and classic VLP models like SSRN" (54.5). This indicates that our framework not only generalizes well but also achieves exceptional performance through task-specific adaptation.
4.3 Ablation Study
We conduct a series of ablation studies to val- idate our key design choices. We first analyze our training strategies and the framework's ' gen- erality, and conclude with an analysis of our reward design. More extensive experiments and detailed results are available in Appendix D.
Table 2: Ablation of Time-R1-7B trainning. GF, ME, SF refers to Gaussian Filtering, Multi-Epoch, and Sample Filtering per epoch, respectively.
TVGBench R1@05
tloU
GF
ME
SF
R160.3
R160.7
Utility of TimeRFT and Time-R1 compo- nents. As shown in Table 2. we analyze the con- tributions of various training components within our framework. We observe that both Gaus- sian Filtering (GF) and Multi-Epoch training (ME) individually improve performance com-- pared to the baseline (Row 1). Multi-Epoch training (Row 4) provides particularly substan- tial gain, lifting the R1 @0.7 score from 13.2 to 14.2. Notably, the value of tloU supervision becomes most prominent when combined with multi-epoch training. This combination (Row 6) leads to a significant leap across all metrics. It boosts the R1 @0.7 performance substantially to 16.4. The progressive integration of these strategies culminates in our full model. The final addition of Sample Filtering (SF) in Row 8 further refines the results. This achieves our best performance with an RI @0.5 29.4 and an RI @0.7 of 16.4.
X
38.0
24.8
13.2
x
36.0
23.6
12.9
/
37.2
25.0
13.4
4
X
x
d
39.9
26.0
14,2
V
38.4
25.6
14.1
X
39.4
26.5
16.4
V
V
X
41.6
28.5
15.6
V
41.8
29.4
16.4
Generalization of RL vs. SFT. As shown in Figure 4, full fine-tuning with SFT on small dataset significantly degrades generalization across all tasks, whereas RL con- sistently preserves While LoRA-basee fine-tuning (SFT- LoRA) alleviates this issue, RL still demonstrates stronger overall performance and generalization. This advantage is particularly evident in data efficiency. Our RL model trained on only 2.5K samples consistently outperforms an SFT-LoRA A model trained on massive 339K dataset, as detailed in our appendix. For example, on ActivityNet, RL improves the mloU from 16.3 to 29.2, while SFT-LoRA with the small dataset only reaches 25.9. Furthermore, RL video QA benchmarks. On
110
Time-Iti-30
Time R1-78
28.
Thinking Length
mloU
23
24-
20.3
18-
18.0
G2.6
80
w/a cold start
w/ cold start
also boosts performance VideOMME, it increases performance from 53.0 to 54.2, while SFT-LoRA causes decline to 51.7.
Figure 5: Impact of SFT-based cold start on IoU performance and thinking token count, with token counts marked by dashed lines.
Impact of cold start. As shown in Figure 5, the impact of a cold start is particularly pronounced for the smaller 3B model, significantly boosting its mloU performance from 18.0 to 20.3. While the larger 7B model shows only

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marginal improvement in mloU, the cold start provides a notable efficiency benefit for both models. reduces the thinking token length of the 3B model from 88.7 7 68.4 and the 7B model from 84.5 to 62.6. We attribute these effects to the cold start's function in suppressing hallucinations, tendency more prevalent in weaker models.
Effectiveness of the Time-RI framework on different base models. To demonstrate that our framework is broadly effective and not lim- ited a specific base model, we apply Time- RI post-trainn training to diverse set of LVLMs. in- cluding the Qwen-2.5 5-VL [4], MiMo- VL [56], and Intern VL3 [72]. As shown in Table 3, our approach consistently yields substantial perfor- mance gains across various model architectures and sizes on TVGBeecc. The improvements particularly striking for models like MiMo- VL-7B, where the RI @0.7 score is more than doubled from 6.6 to 15.1. Similar significant boosts are observed for both smaller and larger models, with Qwen- 2.5- improving from 3.8 to 10.5 and InterVL3-3 from 3.4 to 9.2. This consistent effectiveness across different model families and sizes demonstrates the versatility and general applicability of our RL-based paradigm.
Table 3: Ablation on TVGBench across different base models and sizes.
Model
Type
RI@0.3
RI
@1
Qwen- 1.en-2.5-VL.33B [4]
Base
11.5
6.5
3.8
Time-RI
33.5
21.0
10.5
Qween-25-VL-TB [4]
Base
24.9
8.0
Time-RI
41.6
28.5
15.6
MiMo-VL-7B [56]
Base
22.4
12.6
6.6
Time-R1
41.2
27.8
15.1
VL3-2B [72]
Base
16.3
6.3
2.3
Time-R1
21.8
9.5
4.1
Base
17.4
8.3
3.4
V1. [72]
Time-RI
38.0
22.5
9.2
Ablation on reward designs. As shown in Ta- ble 4, we compare the timestamp-aware loU reward (T-oo)) with several alternatives, includ- ing standard IoU (r1ou), sparse exact-match re- ward (Tem), distance-based metrics (absolute er- ror Tabs and RMSE Trmse), and variant using only the center-alignment term (the second term) of the tIoU (Fceater). The results indicate that strong, fine-grained localization signals are nec- essary, sparse rewards (e.g., Tem) format- only constraints (fformat only) are insufficient. While distance-based metrics perform reason- ably, loU-based designs provide more stable and informative learning signals for temporal localization. Among these, our Fito outperforms V1oU across all metrics. When combine with the Tformat. the full reward (Tito + Tformas) achieves the best overall performance, reaching 16.4 RI @0.7, surpassing the r1oU + Fformat baseline (15.8) and all other alternatives. This confirms the superiority of our final reward design in precise temporal alignment and structured reasoning guidance.
Table 4: Ablation on different reward designs on TVGBench.
Reward Design
R100033
R1600.5
R1 @00.7
Pstot: + Tformat (Ours)
41.8
29.4
16.4
Flormat only
27.1
18.0
10.1
Pstot: (wlo format)
40.5
27.6
15.4
+
41.4
28.0
15.8
Tem + Flocuas
26.5
16.8
9.1
Tabe P'orrmat
39.1
27.8
Frum
38.9
27.0
15.8
Feenter + Flormat
37.6
25.9
15.0
Conclusion
In this work, we present Time-R1, reinforcement learning based post-training framework that significantly improves the generalization of Large Vision-Language Models for Temporal Video Grounding. Unlike prior methods that rely on large-scale supervised fine-tuning, Time-R R1 leverages a verifiable reward signal to unlock strong temporal reasoning from pretrained models using limited data. Our contributions include: (1) Time-R1, reasoning-guider post-training framework that enhances TVG via RL; (2) TimeRFT, a curated dataset and training strategy that fosters temporal grounding: (3) TVGBench, small yet comprehensive benchmark for evaluating LVLMs on TVG. Extensive experiments show that Time-RI achieves SoTA performance across TVG benchmarks in both zero-shot and fine-tuned settings, surpassing prior LVLMs and traditional VLP--based models, while also improving general video understanding. We hope this work inspires future directions in data-efficient and generalizable video-language understanding via reinforcement learning.
Limitations. Our Time-RI framework currently struggles to process ultra-long videos. We plan to address this long-horizon context challenge in future work.
10

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Acknowledgements
This work was partially supported by the Beijing Natural Science Foundation (No. L233008).
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A Limitations
23
Implementation Details
23
Additional Analyses
24
Ablation Studies
25
E Qualitative Result
27
A Limitations
Despite achieving notable improvements on the TVG task, our approach still has several limitations. First, Time-RI suffers from slower training and inference speeds, primarily due to its large model size and reliance on autoregressive text generation. Second, to manage GPU memory consumption, use relatively low frame sampling rate, which may result in the loss of fine-grained motion information across frames. Finally, Time-RI currently cannot handle ultra-long videos, limiting its applicability in scenarios such full-length movie understanding.
B Implementation Details
Details of Time-R1 framework. Inspired by DAPO [61], we adopt its token-level loss for training. rather than the sample-level loss used in GRPO. Apart from minor changes the loss, all setting identical to GRPO. Besides, we find that other techniques introduced in DAPO do not benefit the TVG task, thus aborting other techniques. We full-finetune the LLM parameters at every step, thus Te(a.)
1. The sample number G' is set to 8. The coefficient 3 is set to 0.04.
10 [o.]
Gaussian distribution with fixed variance of 0.2, while varying the mean to control sample selection. In our cold start phase, we construct 150 samples from our training data sources (e.g., YT-Temporal [59]) to fine-tune the LLM using LoRA [21], with a LoRA rank 64 and a LoRA alpha 128. All our results are reported based on the final training epoch. For RL,
Details of TimeRFT training. For RFT data filtering,
learning rate of 1e-6 with the AdamW optimizer with 3,=0.9, B2 = 0.999, and linear scheduler to decay the learning rate from 1e-6 to We use a batch size of with gradient accumulation set 2.
Details our evaluation prompts. shown in Figure 11, for temporal video grounding, the prompts used for training and testing are designed to encourage the model to reason before responding, following a template-bast based answer format. For VideoQA, we have two versions of prompts: one with CoT and one without COT.
Details of TVG baseline methods and implementations. We evaluate the baselines on TVGBench using their original best-performing setting, focusing primarily on video input and prompt design.
TimeChat [47] is built upon the InstructBLIP [8] architecture and introduces video Q-former encode video tokens. It operates at a resolution of 224 and samples 96 frames.
TRACE [19] treats each combination of timestamp, saliency score, and caption discrete event and enables the LVL.M to autoregressively generate event sequences. It operates at higher resolution of 336 and samples 128 frames.
TimeSuite [65] introduces a token shuffling strategy to compress long video token sequences and incorporates positional encoding enhance visual understanding. adopts resolution of 224 and samples 128 frames.
VideoChat-Flaan [28] proposes a progressive visual token dropping mechanism within intermediate
LLM layers to compress video inputs and extend the effective context length. It uses resolution of 448 and samples video at fps, with a maximum of 512 frames.
*
Gemini-2.5- [10]: Gemini-2.5-Prr is state-of-the--ar video understanding model capable of reasoning over videos exceeding one hour length. It supports video question answering and temporal localization tasks.
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Table 5: Comparison of different approaches TVGBench for all types.
We use mloU metric.
Method
EC
ES
HAC
HAP
HAS
HP
OA
OC
OEC
OES
TimeChat [47]
22.3
32.8
16.6
9.8
14.6
35.1
15.0
9.2
2.4
18.0
10.2
TimeSuite [65]
27.3
39.6
14.2
12.8
24.9
39.6
14.6
13.9
6.7
32.6
14.3
TRACE [19]
57.1
66.8
25.9
17.5
26.5
45.1
17.8
22.1
12.5
36.8
24.9
VideoChat-Flash [28]
38.3
47.2
12.9
13.9
27.1
39.4
14.9
12.7
6.5
24.3
12.9
Gemini-2.5-Pro [10]
46.7
45.3
21.1
27.6
30.9
39.9
23.0
31.1
14.1
35.9
17.8
Time-RI (ours)
49.3
65.3
28.3
24.3
39.3
56.2
26.3
21.8
9.0
32.7
21.8
Details of our implemented SFT baselines. We implemented two versions of SFT fine-tuning: one is full-parameter fine-tuning of the LLM (SFT), and the other is LoRA-basee fine-tuning of the LLM (SFT-LoRA). For SFT-LoRA, the LoRA rank is set to 64, and the LoRA alpha is set to 128. Both configurations use the following settings: learning rate of 2e-5, the Adam W optimizer with 3109, 82 0.999, weight decay of 0, the batch size of 8, and accumulation steps of 2. We fine-tune for epochs on our 2.5K data, and use a linear scheduler to gradually decay the learning rate to 0.
C Additional Analyses
In-depth comparisons of different approaches on TVGBench by semantic type. Table 5 provides detailed performance comparison of various methods on the TVGBench across different semantic categories. Specifically, the abbreviations represent: EC (Enviromment Change), ES (Environment State), HAC (Human Action Complex), HAP (Human Action - Procedural), HAS (Human Action - Simple), HP (Human Pose), OA (Ohject Attribute), OC (Object Counting). OFC (Object Existence Complex), OES (Object Existence Simple), and OT (Object Transition). Detailed definition and construction process can found in Figure 15.
Time-RI demonstrates strong competitiveness across multiple semantic categories. First, particularly in the four tasks of HAC, HAS, HP and OA, Time-RI achieved the highest scores among all compared methods, showcasing its excellent ability in understanding the details of human actions and identifying object features. For example, Time-R RI achieves an mIoU of 56.2 on HP,, which is 11.1 points higher than the second-best method, TRACE with mloU of 45.1. On HAS, Time-RI R1 reaches 39.3, outperforming Gemini-2.5-Pro- 30.9 by 8.4 points. Second, in the three tasks of ES, EC and OT, Time-RI demonstrates strong performance comparable to the top model TRACE, with its performance being very close or immediately following. the HAP task, Time-R1 also performs excellently, with its performance heing in the same tier as Gemini-2.55PPro. Last, all models still show a noticeable gap compared to Gemini understanding complex instructions such as in HAP, OC, and OEC. For example, in HAP, which involves procedural activity localization, Gemini achieves 27.6, while our model ranks second with a score of 24.3. In object counting, Gemini attains 31.1, substantially outperforming our model's 21.8. In summary, Time-RI I performs well on both non-human simple instructions and human-related instructions, but there is still room for improvement in complex instruction grounding and object-related grounding.
Table 6: Inference speed comparison between HuggingFace Transformers and vLLM librarics.
Speeds are reported as (with CoT / without CoT) with GPUs.
TVGBench
Charades
ActivityNet
Domain Sample Count
Mixed
Indoor
Human Activity
800
3,720
17,031
vLLM Speed
6.9 min
11.7/112 min
36.1 / 33.2 min
Transformers Speed
42.0 27.3 min
3.3 / 1.2 hour
15.0 7.5 hour
Comparison of speed and accuracy between inference library transformers and vLLM. We observe that the inference speed of the implementation in the transformers [54] library is very slow. To address this, we implemented an accelerated inference version using vLLM [24] for all related
7 downstream benchmarks. As detailed in Table 6, vLLM delivers substantial performance gains across all datasets. For example, on the TVGBench benchmark, inference time with CoT is reduced from 42 minutes to just 8.3 minutes, achieving over 5x speedup.
24

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Table 7: Ablation of data filtering strategies.
Table 8: Ablation of KL and CoT in GRPO.
Mothod
R1@00.3
@10.5
R1@0.7
mloU
KL
CoT
RI@0.3
R1@0.5
RI @007
mloU
randomn
39.4
26.5
16.4
27.4
X
x
40.4
29.1
14.9
28.1
gaussian
gaussion (0.5)
40.6
28.2
15.6
16.0
28.3
V
x
40.8
27.4
15.0
27.7
gaussian
X
42.9
29.5
15.0
29.1
uniform
40.4
28.5
15.9
28.3
41.6
15.6
28.6
Table 9: Comparison of the token-level loss design used by DAPO [61] and the sample-level loss design used by GRPO [48].
Churades-STa
ActivityNet
TVGBench
Loss
k1
GRPO
76.7
59,8
34.4
$7.0
55.9
37.1
20.3
37.8
40.8
28.0
16.5
28.4
DAPO
77.4
60.0
34.1
57.2
56.2
37.
20.4
38.0
41.6
28.5
15.6
28.6
D Ablation Studies
Ablation of different RFT data filtering strategies. As shown in Table 7, different data filtering strategy in the initial round affects model's performance. First, appropriate Gaussian filtering outper- forms both uniform and random filtering methods. Among the Gaussian filtering settings, a standard deviation of 0.3 yields the best results, followed by 0.5 and then 0.7. These findings suggest that incorporating moderately challenging samples during RFT helps improve the model's generalization capability more effectively than using either overly easy or extremely difficult examples.
Ablation of KL and CoT during GRPO training. As shown in Table 8, incorporating CoT reasoning during training leads to improved performance compared to the No-CoT setting, suggesting that CoT enhances the model's 's temporal video grounding capabilities. When KL divergence omitted (No-KL), performance slightly decreases under the No-CoT setting but unexpectedly improves when CoT is present. However, we find that in the No-KL+CoT setting, the model often fails to produce thinking process, directly jumping to answers. In contrast, using KL divergence helps maintain more logical reasoning that is easier to follow. To balance performance and interpretability, we adopt training setup that includes both KL and COT.
Comparison of tlu and IoU during multi-epoch training, As shown in Figure 6, tloU consistently outperforms standard IoU during both the early and late stages of training over the first 5 epochs. Notably, while tloU steadily improves as training progresses, IoU shows a decline performance by the fifth epoch. This highlights the advantage of using tloU as more stable and reliable reward for temporal video grounding.
Ablation of sample filtering in multi-epoch training. As shown in Figure 7, applying sample filtering (SF) to remove simpler training samples yields consistent performance improvements across epochs. This suggests that easy samples with high-IoU may introduce noise or reduce the effectiveness of learning, and filtering them helps focus the model on more informative and challenging instances.
Ablation of DAPO & GRPO. The sample-level loss used by GRPO computes the loss by averaging over each individual sample. This approach leads to unequal loss contributions for tokens when dealing with CoTss of varying lengths. DAPO addresses this issue by employing a token-level loss. The underlying principle is that the token-level loss can effectively guide the model in the process of CoT generation, allowing it to learn useful patterns from CoTss of different lengths sampled during training. Table 9, we compare these two loss designs. empirically find that DAPO outperforms GRPO on the majority of metrics, thus we adopt DAPO's loss design.
Table 10: Performance comparison between the base model, RL, and different SFT settings.
Mathod
R1@9,3
TVGBench RIG0.5
R1G0.7
mloU
R @0.3
RI@005
Charades
R160.7
mloU
R1G033
Activaty-Net RI G00.5
R1@077
mloU
Base Model
24.9
16.0
16.3
58.7
16.6
37.9
34.3
21.6
12.9
22.9
SFT-2.5K
25.9
14.4
5,8
15.4
49.5
31.3
12.7
31.2
27.7
13.6
5.8
15.7
SFT-LORA-2.5K
SFT-LoRA-3:9K
38.9
28.2
15.2
27.4
72.4
54.7
26.9
51.7
45.7
29.3
16.4
30.5
Time-R R1-2.
41.8
29.4
16.4
29.2
78.1
50.8
35.3
58.1
58.1
39.0
21.4
40.5
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Table 11: Ablation study SFT-LORA hyperparameters (rank/alpha).
LoRA rank alpha
6/0.3
R1@0.5
TVGBeach
RI6077
R160.3
Charades RI@0.5
mloU
RI60.3
RI60.5
ActivityNet
R1
mloU
16/32
31.4
22.0
11.5
21.8
58.7
44,9
25.6
40.4
32 /64
35.0
24.2
14.4
24.8
72.4
54.0
29.4
48.8
64 128
38.5
24.5
14.6
26.2
74.0
55.7
29.9
49.9
52.3
34.3
18.8
36.5
128 /56
38.4
25.4
14.2
26.4
76.5
58.3
32.9
52.2
256 512
39.1
27.1
15.1
26.9
75.9
57.4
32.7
51.6
Time-RI-78
41.6
28.5
15.6
28.6
77.4
60.0
34.1
57.2
58.1
39.0
21,4
40.5
Table 12: Ablation study on the number of training epochs for SFT-LoRA.
LoRA rank/ rank/alpha
Epochs
R1600.3
R1000.5
TVGBench
R10007
mloUl
R1
RI605
Charades
RI R1807
mlol."
R1600.3
R1000.
ActivityNet
R1660.
mloll
73.5
57.3
31.3
50.5
54.8
37.1
21.7
39.0
64/
73.5
52.4
20.5
37.4
5
38.5
24.5
14.6
26.2
74.0
55.7
29.9
49.9
52.3
34,3
1888
36.5
1
27.4
18.1
8,9
18.3
47.8
33.4
16.7
31.7
128/256
2
37.9
36.2
14.4
26.2
76.4
17.0
30.7
51.3
5
38.4
25,4
14.2
26.4
76.5
58.3
32.9
52.2
Comparison between RL and SFT. We conduct a comprehensive comparison between our Rein- forcement Learning (RL) approach and various Supervised Fine-Tuning (SFT) settings. As shown in Table 10, -ullmparameter SFT on our curated 2.5K dataset leads to severe overfitting and signifi- cant performance drop compared to the base model. Using LoRA for parameter-efficient fine-tuning (SFT-LoRA) mitigates this issue and improves performance, but it still falls short of the results achieved with our RL method. To further demonstrate the data efficiency of our RL paradigm, we train an SFT-LoRA model on the full 339K TVG dataset. Even with access to over 100 times more data, the SFT-LoRA model underperforms our RL model, which trained on only 2.5K samples. This highlights the superior generalization ability and data efficiency of the proposed RL framework.
Further ablation studies on SFT-LoRA are presented in Table 11 and Table 12. We find that while tuning hyperparameters like LoRA rank / alpha and the number of training epochs can lead to better SFT results, none of the tested configurations match the performance of our RL approach. These extensive comparisons firmly establish the advantages of our RL-based post- post-training framework over SFT for the TVG task.
Ablation of cold-start strategy. We investigate the effect of using a cold-start strategy, where initialize the model with SFT on dense video captioning data before starting RL training. This approach motivated by our empirical observation that the model naturally generates reasoning similar video descriptions. The cold-start strategy helps accelerate convergence towards the desired response format and provides a better initialization for the RL policy. As shown in Table 13, this initialization provides significant performance boost, partieularly for smaller models like the 3B variant, improving its ability to learn effectively during the subsequent RL phase.
Effectiveness of the Time-R1 framework on different base models. To demonstrate the effec- tiveness of our Time-R1 framework, we apply it to a diverse set of Large Vision- Vision-Languee Language Models with varying architectures and sizes. As shown in Table 14, our RL-based post-training consistently yields substantial performance improvements across all tested models, including Qwen-Vi VL (3B, 7B), MiMo-VL-7B, and Intern VL (2B, 8B). The results confirm that our method is not limited to a specific backbone and can effectively enhance the temporal grounding capabilities of different LVLMs. We also observe that larger models generally achieve better performance both before and after post-training, supporting the idea that scaling model capacity is beneficial for TVG tasks. The Time-R1* results indicate further fine-tuning on downstream datasets, which yields additional performance gains.
Ablation of different reward designs. We perform comprehensive ablation study to validate choice of the reward function. As shown in Table 15, We compare the timetamp-awar IoU (?uoo) combined with the reasoning format reward against several alternatives. For clarity, we first define each reward component used in our experiments.
Reasoning format reward (format(.) (.). A binary reward that is if the model's output adheres to the required reasoning template format, and otherwise. This reward encourages the model generate responses in the correct structure.
26

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Table 13: Performance of the 3B model with and without the SFT-based cold-start strategy.
TVGBench
Charales R160.5
ActivityNet
Methed
R1@0033
R1600.5
R1
R1800.3
R1@007
mloU
R16003
R1600.5
R1@0.7
w/o Cold-Start
29,0
17.1
8.0
18.0
67.0
44.9
21.5
44.4
36.6
18.8
7.8
21.1
Ctad-Start
51.2
Table Performance comparison across different base models and sizes.
Model
Method
Churades
K1000.
ActivityNet
R1
R1
TVGBench R16F0.5
R1607
mioU
Base
24.2
7.1
9.8
11.5
6.5
3.8
8.3
Quen-2,5-VI-588
Time-R1
74.6
53.1
26.0
51.2
40.0
21.0
8.7
23.2
33.5
21.0
10.5
21.7
Time R1*
78.7
61.1
36.9
59.9
66.8
16.8
24.7
46.1
Base
58.7
38.3
16.6
37.9
34.3
21.6
12.9
22.9
24,9
16.0
3.0
16.3
Qwen-25-VL-778
Time R1
60.8
21.4
40.5
29,4
16.4
29.2
52.1
ML-T-VLL-BB
Base
Time R1
79.9
48.5
27.0
63.9
33.4
53.9
45.6
27.2
12.1
14.2
24.2
31.9
22.4
41.2
27.8
12.6
15.1
6.6
27.4
15.7
LaternVL-22
Time-R1
Base
20.9
24.0
11.5
7.8
3.5
1.9
15.4
15.7
20.6
18.6
9.5
8.5
3.1
3.9
14.2
14.2
21.8
16.3
6.3
9.5
4.1
2.3
11.7
14.8
IsterVVL-8B
Time
Base
27.8
11.9
3.7
20.6
33.1
18.4
10.3
24.0
17.4
8.3
3.4
11.8
Standard IoU reward (-). The standard Intersection over Union between the predicted segment [t., fe] and the ground-trun segment [t',, E.l, computed as:
max(0, min(te (2) max((s, (.))
TioU max(tes (2) min((,,( ('))
8
Timestamp-awe aware IoU reward (-) The timestamp-awe aware IoU reward augments the standard IoU with center alignment term that penalizes discrepancies between the centers of the predicted and ground-truth segments:
(t. + t../2 / - 222
TtloU = rloU + Teenters
where
Toenter
1/
This modification provides a more fine-grained grounding signal by encouraging both boundary alignment and temporal center consistency.
Exact matching reward Tem (.). A sparse binary reward that is only if the predicted timestamps exactly match the ground truth, and 0 otherwise:
Tem I[tt, t, A =t)
(10)
Absolute error reward (-). The negative L1 distance between the predicted and ground-truth boundaries:
Tabs = -14, 1'' + 1/e (2))
(11)
RMSE reward Trmse (-). The negative Root Mean Square Error, which penalizes larger boundary errors more heavily:
(t. 12) + (1. 12)2
Trusse
2
(12)
The results in Table 15 clearly demonstrate the necessity of strong, fine-grainee grounding signal combined with a structural incentive. Using the reasoning format reward alone (Tformas only) or sparse rewards like Tem is insufficient for effective learning. The comparison between Tito (without format) and our full approach (Fuoo + Tiormat) shows that the format reward consistently improves performance by guiding the model's generation structure. While distance-based metrics perform reasonably well, IoU- bosed approaches provide more informative and stable learning signal. Among these, our proposed Tuoo consistently outperforms the standard FloU, validating the superiority of our final reward design. The combination of a precise grounding signal and a structured reasoning incentive yields the best temporal grounding performance.
E Qualitative Result
Case study temporal video grounding on Charades and ActivityNet. As shown Figure 8, in the example above, given relatively complex language instruction, Time-RI R1 demonstrates more
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Table 15: Ablation study different reward functions and their combinations three datasets.
TVGBench
Charades
Activity
Reward
R16003
R160.7
R1600.3
R1
R160.3
R1@0.7
+ Vomma (Ouns)
41.8
29.4
16.4
29.2
78.1
60.8
35.3
58.1
58.1
39.0
21.4
40,5
Tiemer only
27.1
18.0
10.1
18.8
60.4
39.2
17.4
39.3
35.1
22.2
13.1
26.0
7axi (wia format)
78.4
36.1
Tomer
41.4
28.0
15.8
28.4
77.6
58.9
32.7
56.4
58.8
38.8
20.7
39.4
Fanir
37.6
25.9
15.0
26.3
75.6
57.7
31,9
51.3
52.5
34.1
18.5
36.6
7am + Taama
26.5
9.1
18.7
Ful
Thesna
39.1
27.8
14.8
27.4
76.2
59.5
33.9
52.3
51.4
32.9
17.8
35.7
38,9
27.0
15.8
27.2
75,4
58.6
33.1
51.5
51.2
32.7
17.8
35.5
Comparison of tloU and !oU reward
Ablation of using sample filtering (SF) epoch
tloU
29
w/ SF
IoU
w/o SF
mlou
27
27
26
26
1
2
5
Epoch
Epoch
Figure 6: Performance comparison of tloU and IoU in multi-epoch training.
Figure 7: Ablation of sample filtering in multi- epoch training.
accurate localization than all baselines, successfully capturing the initial event "goes back the pink bucket" within the timestamp, while other SoTA models like Gemini-2.5-Pret fail. In the example below, the model accurately localizes the event, excluding "a person is lying on the couch" and correctly distinguishing between sitting and lying, unlike other models which either localize only small segment (TimeSuite and VideoChat-Fiie
or the entire segment (TRACE and Gemini-2.5-Proi
Case study of short video QA on VideoMME and TempCompass. As shown Figures 9 and 10, the base model in tasks requiring positional
Time-R1 demonstrates improved performance judgment, scene storyline reasoning, and visual reasoning. For example, in Figure 9, Time-RI correctly identifies that in the video is missing its right-front wheel, detail that the base model fails to recognize. This reflects that Time-RI likely possesses stronger video localization capabilities, which in turn enhance its visual reasoning ability. In Figure 12, output CoT when answering the QA task, providing some interpretability. This example shows that Time-Rl's reasoning process more concise, whereas the base model often reasons correctly but arrives the wrong answer. This suggests that Time-RI reasoning may be more effective in guiding the final answer, possibly benefiting from the outcome-driven RL of GRPO.
Case study of long video QA on EgoSchema and VideoMME. Figure presents long egocentric video QA example focused summarizing task steps. In the "Hanging the Dress" the base model fails to identify all key steps, while our Time-RI model correctly selects the answer by generating
accurate chain-of-thought (CoT). In Figure 14, the task involves identifying the primary tools and ingredients used in the video and understanding their respective contributions. An additional example in Figure involves animated scene reasoning, where Time-RI correctly infers-based on elements like the presence of a guitar-that the heroes defeated the enemies using the power of music, whereas the base model misinterprets the cause. Across these complex reasoning tasks involving long videos, Time-RI consistently demonstrates superior performance compared to the base model.
Illustration of our prompt at training and inference time. Figure 11 presents the prompts used for the temporal video grounding and video QA tasks both training and inference time.
Illustration our prompt to annotate query semantics on TVGBench. Figure presents the prompt used to annotate query semanties in the TVGBench dataset. The prompt is designed to guide
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the DeepSeek LLM in classifying each input query into one of 11 predefined semantic categories. To improve annotation quality, we refer to the construction of existing benchmarks and carefully select both positive and negative examples for each semantic type. These examples are chosen to include queries that are either easy or difficult for the model to answer, helping to refine the model's understanding and improve labeling accuracy.
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Question:
man goes pink to another clothing, wrings
pink bucket, throws the yellow bucket, then wrings his hands together few times while staring at the camera"?
(videe length: 74s)
Ground Truth
33.1s +
73.5s
TimeSuite
30.0s
Gemini-25-PPo
VideoChat-Plash
0.0s
17.2s
Trace
54.9s
74.1s
Time (ours)
35.06
thinking [ours]: "The goes back the pink bucket wash another dark item clothing, wrings out the pink
bucket, the yellow bucket, wrings his together staring at video.
and
Question: Where is the event "a person is seen sitting couch??
(video length: 31s)
Ground Truth
0.0s
12.8s
TimeSuite
0.0s
30.0s
VideoChat-Plash
0.0s
5.0k
Trace
(ours)
10.0s
Time-R1 thinking (ours): The event "a person is seen sitting on a couch" occurs the beginning of the video. The person seated on couch, holding a phone, and appears to be engaged with it. This aligns with initial frames the video.
Figure 8: Temporal video grounding cases from Charades and ActivityNet benchmark. Time-R R1 outperforms other SoTA models.
(video length: 38m) Question: What do heroes oflegend use to defeat the enemy on video?
(g) Their wisdom
big robot
(c) Their superpower
Power music
(video length: 17s) Question: What's wrong with this car?
(A) It doesn't I have a left rear wheel.
(i) doesn' have a right front wheel.
(C) headlamp is
(D) right broken.
Figure 9: Case study on VideoMME (w/o CoT), demonstrating that Time-RI achieves better perfor- mance than the base model.
30

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(video length: 10s)Question: Which sentence better captures the essence of the video?
A First, two hand are holding the clay pot then three hands are holding it.
In the video, three hands are holding the clay pot, then two hands holding the clay pot.
shuf
shutter
(video length: 14s) Questiin:: What the sequence actions performed by the girl?
Putting clothes, jumping water, taking off clothes
(B) jumping into water, putting clothes, ta taking off clothes
( Taking clothes, putting on jumping water
Figure 10: Case study on TempCompass (w/o CoT), demonstrating that Time-RI achieves better performance than the base model.
Temporal Video Grounding
System Prompt
You are video analysis expert.
Input Prompt
To accurately pinpoint the event "[EVENT]" in the video, determine the precise time period of the event. Output your thought process within the <thinlo> </thinl tags, including analysis with either specific time ranges (xx.xx to xxxx) in <timestep> < </timestep> tags. Then, provide the and end times (in seconds, precise to two decimal places) in the format "start time to end time" within the <answer> </answer> tags. For example: "12.54 17.83"
Video QA
System Prompt
You are video analysis expert.
Input Prompt (w/o CoT)
Carefully watch the video and pay attention the and sequence of events, the detail and movement of objects and action and pose of persons. Based your observations, select the option that accurately addresses the question. Provide the letter of the best option wrapped in parentheses within the <answer> < /aanswer> tags. Question: (} ()"""
Input Prompt (w/ CoT)
Carefully watch the video and attention to the cause and sequence of events, the detail and movement of objects and the action and pose of persons. Based your observations, select the best option that accurately addresses the question. Output your thought process within the <think </think> tags, including analysis with either specific timestamps (xo.x)) or time ranges (xx.x xx.xo) <timestep> </timestep> tags. Then, provide letter the best option wrapped in parentheses within the <answer> </answer> tags. Question: ( ()
Figure 11: Illustration prompts at both training and inference time.
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shutterstock
shutter stock knsckk
shutterstsck
(video length: 8s) Question: Is the pineapple being pushed forward by person?
(ALYes Yes
No
Base model thinking:
determine pineapple being pushed forward by a person, let's analyze video:
1. The video shows pineapple pink surface.
2. hand appears in the frame and interacts with the pineapple.
3. The hand lifts the pineapple, indicating that it being moved by the person.
Given this analysis, the pineapple is indeed heing pushed forward by person.
Time-R1 R1 thinking (ours):
The video shows hand reaching into the frame and grasping the pineapple. The then lifts the pineapple, indicating that the pineapple is not being pushed forward by a person rather being moved by hand.
Shitli
shot
(video length: 9s) Question: In which sequence does the man perform the actions?
(A) Gliding the skateboard and then jumping
BB) jumping and then tying his shoelaces
Tying his shoelaces and then gliding the skateboard
Gliding the skateboard and then tying his shoelaces
Base model thinking:
Let's analyze the sequence of actions performed by the man in the video:
1. The man is initially seen standing on a skateboard.
2. He then proceeds to tie his shoelaces.
3. After tying his shoelaces, he glides on the skateboard.
Given this sequence, the correct answer is: (D) gliding on the skateboard and then tying his shoelaces
Time-R1 thinking (ours): :
The sequence of actions performed by the man in the video is follows: first, he ties his shoelaces, and then he
glides the skateboard. This can be observed from the video where the man is seen tying his shoelaces before he starts gliding on the skateboard.
Figure 12: Case study on TempCompass (w/ CoT), demonstrating that Time-RI achieves better performance than the base model.
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(video length: 3m) Question: Summarize the steps taken by c to complete the tasks in this video. don't list every action narrated, but rather focus on major components and how they're executed.
(A) folds the dress, places on the ironing board, and then hangs it up.
(B) folds the dress, places the ironing board, and then packs it.
(C) folds the dress, places it on the ironing board, and then washes it.
D folds the dress, places the ironing board, and then irons it and hangs it up.
B C folds the dress, places on the ironing board, irons it, and then folds it again.
Base model thinking:
Let's analyze the video step by step to determine the correct sequence of actions:
1. Folding the Dress: The video starts with folding blue dress on the ironing board. This the first major action.
2. Placing the Dress on the Ironing Board: After folding the dress, C places the ironing board.
3. Ironing the Dress: C then irons the dress, ensuring it is smooth and free of wrinkles.
4. Hanging the Dress: Finally, hangs the dress up, completing the task.
Now, let's evaluate the options:
(A) folds the dress, places on the ironing board, and then hangs it up.
(B) C folds the dress, places it on the ironing board, and then packs it.
(C) folds the dress, places the ironing board, and then washes
(D) folds the dress, places it on the ironing board, and then irons it and hangs it up.
(E) folds the dress, places on the ironing board, irons it, and then folds it again.
The correct sequence of actions matches option (D).
Time-Ra R1 thinking (ours):
The video shows C folding dress, placing the ironing board, and then ironing it. The sequence of actions is clear and follows a logical order. The dress folded, placed on the ironing board, and then ironed, which is common sequence for completing task like ironing dress.
Figure 13: Case study EgoSchema (w/ CoT), demonstrating that Time-RI achieves better perfor- mance than the base model.
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(video length: 3m) Question: Identify the primary tools and ingredients с used throughout the video, and discuss how they contributed to the overall dessert preparation process.
(A) The primary tools and ingredients used throughout the video were a spatula, a strainer,
dessert bowl, cocoa powder, and tissue paper. the spatula was used to spread the batter in
the bowl. the strainer was used to sieve the cocoa powder into the bowl. the dessert bowl was used hold the batter. the
used add flavor and color the batter. the tissue paper was used to wipe the edges of the bowl.
powder
(B) The primary tools and ingredients used throughout the video were mixing bowl, whisk, a baking dish, flour, sugar, eggs, and butter: the mixing bowl was used to mix the batter: the
whisk was used beat the eggs. the baking dish was used bake the cake. the flour was used to add structure to the cake. the sugar was used to add sweetness to the cake. the eggs were used add moisture the cake. the butter was used add flavor and richness to the cake. (C) The primary tools and ingredients с used throughout the video were a muffin tin, a spoon, chocolate chips. flour, sugar, eggs, and butter: the muffin tin was used bake the muffins. the spoon was used to scoop the batter into the muffin tin. the chocolate chips were used to add flavor and color the muffins. the flour was used to add structure to the muffins. the sugar was used to add sweetness to the muffins. the eggs were used to add moisture to the muffin.. the butter was used add flavor and richness the muffins.
(D) The primary tools and ingredients c used throughout the video were a cake pan, spatula, chocolate frosting, flour, sugar, eggs, and butter, the cake pan was used bake the cake, the spatula was used to spread the frosting the cake. the chocolate frosting was used to add flavor and color the cake. the flour was used add structure to the cake. the sugar was used to add sweetness to the cake. the eggs were used to add moisture to the cake, the butter was used add flavor and richness to the cake.
(E) The primary tools and ingredients used throughout the video were a mixing bowl, a whisk, a baking sheet, flour, sugar, eggs, and butter. the mixing bowl was used to mix batter. the whisk was used to beat the eggs. the baking sheet was used to bake the cookies. the flour was used add structure the cookies. the sugar was used add sweetness the cookies. the eggs were used to add moisture to the cookies. the butter was used to add flavor and richness cookies.
Figure 14: Case study EgoSchema (wo CoTT, demonstrating that Time-R1 achieves better performance than the base model.
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## Task
Classity input queries into evactly the following categories based their semsnttc content::
1. Human Action (Simple)
Definition: Singular physical movements or basic Interactions.
Examples: person opens a book over their head. The person gets out some ginger. who did talk to in the shopping mall?
2. Human Action (Complex)
Definition: Single continuous event with intricate components concurrent elements.
Examples:
He talking while several people using rowing machines.
wearing blue shirt activity of faxing cam ping tent?
jumping leg extension and another wearing red pants play field.
3. Human Action (procedural)
Definition: contains multiple sequential events with explicit temporal boundaries, contains multiple actions, each with clear start and end.
Examples: The person procures condiment from the pantry, takes spoon from the drawer which he uses scoop into the then
returns the condiment the pantry, places the spoon the sink and stirs the pan.
takes spoon from the drawer, scoops sugar glass, with the julie,, and returns the
package pantry.
-Nggative Examples:
man juices some
juicer: only action
She pets out cutting board and knifer only one action
He then finishes by doing tricks only one action
She removes bits of shell until there is a small hole: only one action
4. Human Pose
Definition: Static body positions group configurations. Posture descriptors, positional prepositions
Examples: Several other people
background working equipment. monkey
young child seen standing
5. Object Existence (Simple)
Definition: Current location/sti /status queries. Simple location prepositions.
Examples:
Where is the tap?
where the chupsticle??
In what location did the blue tent?
6. Object Existence (Complex)
Definition: Queries about historical object positions changed by human actions, requiring tomporal action context (eg. /before [action]"]. "]
Examples:
Where was the spatula after first used it?
Where was the steve beforel picked it?
what bolt did pick?
What mushroom
7. Object Attribute
Definition: Physicl/ /abstract property inquiries. Property deseriptors (color/size/mant)
Examples:
- what material did pick from the shelf?
what color the toillct bin?
8. Object Counting
Definition:
Quantitative object presence queries. Numeric quantifiers, plural objects
Exampless how many rolls are in the tray
many tissue paper were
9. Object Transition
Definition: State /pusition change confirmation. Transformation verbs, completion checks Examples: The bulb brokee apart.
close fridge?,
10. Environment Change
Definition: Dynamic scene modifications. Transient elements, overlay content
Examples: video ends with clothes/captiee scrolling down
11. Environment State
Definition:
elements. overlays, permanent
Exampless Intro states "Progression: First
Trend Routing Technology' logo appears
## Output Format
Return ONIY the category name from: [Human Action (Procedural), Human Action (Comples), Human Action (Simple), Human (Complex).Objees
Object (Simple), Object
Object Counting, Object Transition, Environment Change,
Environment State]"
INPUT PROMPT "Gvvvn the query below, classtfy into one categories mentioned ahove.Query: (query) Your response:
Figure 15: Prompts for LLM used to annotate the semantics of each query on TVGBench.
35


Reviews:
[{"review_id": "L0T3LSxOZo", "reviewer": "Reviewer_XTMy", "summary": "The work introduces RL post-training framework into Temporal Video\nGrounding and achieves SOTA performance across various TVG benchmarks.\nIn addition, the work also curates a dataset and benchmark for TVG task,\ncontributing to the development of the research community.", "questions": "1. The paper only uses Qwen2.5-VL-7B as the base model. Can the same conclusion be reached by using other series models or models of different sizes?\n2. Figure 4 shows that the VQA capability is improved after RL post training. Is this because the dataset used in the TimeRFT stage contains VQA data? If only TVG CoT data is used for training in the post training stage, can the VQA capability be kept on par with the base model?\n3. The paper does not conduct ablation experiments on the two rewards of the TVG task. Is it possible to use only one of the rewards to observe the experimental results?", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths：\n1. This work achieves SOTA performance across TVG benchmarks \n2. This work effectively integrates RL into TVG task and innovatively proposes two rewards curated for TVG task to assist the post training\n\nWeaknesses：\n1. TVGBench has only 800 samples and comes from existing datasets, so the\nnecessity of building this benchmark is questionable\n2. This work is solid, but it is just the RL post-train paradigm commonly used in LLM\nadapted to the TVG task, which is novel but not much", "was_revised": true, "final_justification": "Most of my concerns have been addressed. I recommend the authors include the relevant experiments in the revised version. I maintain my current vote.", "rebuttal": "We appreciate the time and effort you invested in providing these detailed observations, questions, and comments. We have carefully considered your comments and outlined our responses and proposed revisions below. We hope these clarifications and revisions address your concerns and further enhance the manuscript.\n\n**W1: The necessity of designing TVGBench**\n\nAs stated in Figure 3 and Section 3.4 of our paper, existing benchmarks are often either excessively large or overly domain-specific, which poses challenges for effectively evaluating large multimodal models. In contrast, TVGBench is deliberately designed to be compact yet comprehensive. To show its advantage, we illustrate the comparison between benchmarks used in this work:\n\n| test with/without CoT using 8×H100 | TVGBench| Charades| ActivityNet|\n|-----------------------------------------|---------------|-----------------|-----------------|\n| Domain| Mixed| Indoor| HumanActivity|\n| Sample Num| 800| 3720| 17031|\n| vLLM inference speed| 8.3min/6.9min | 11.7min/11.2min | 36.1min/33.2min |\n| transformers inference speed| 42min/27.3min | 3.3hour/1.2hour | 15hour/7.5hour  |\n\nNoting that TVGBench encompasses a broader range of data sources, we built our implementation on top of the vLLM library to achieve faster inference speed. As our framework would be fully open-source, it provides a useful suite for the community.\n\nMoreover, although TVGBench is constructed by curating samples from existing datasets, we re-annotated each sample with consistent semantics and re-balanced the distribution. This ensures the benchmark's reliability and representativeness, allowing it to serve as a useful evaluation suite.\n\n**W2: Solid work, novel but not much**\n\nThank you for recognizing the solidity and novelty of our work. We address a crucial and underexplored challenge in long-video understanding, proposing a new RL paradigm that fundamentally shifts how this problem is approached. Our method not only introduces a fresh perspective but also achieves state-of-the-art performance, demonstrating both its practical effectiveness and scientific contribution. Furthermore, we will publicly release all our training data, training code, and inference code, which we believe will be valuable to the research community.\n\n**Q1: Will improvements be made on different base model and model sizes?**\n\nYes, they would! Following your suggestions, we extend our evaluation to include **Qwen-2.5-VL-3B, MIMO-VL-7B, InternVL-2B, InternVL-8B, and TimeSuite-7B**. Due to the time limit, we post-train all base models using our 2.5K filtered by Qwen-2.5-VL-7B. The results are presented as follows:\n\n|||| TVGBench  |||| Charades  |||| ActivityNet |||\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| Model | Size | R1@0.3| R1@0.5| R1@0.7| mIoU | R1@0.3| R1@0.5| R1@0.7 | mIoU| R1@0.3| R1@0.5 | R1@0.7| mIoU  |\n| Qwen-2.5-VL-3B (Base) | 3B | 11.5 | 6.5  | 3.8  | 8.3  | 24.2  | 15.5 | 8.1| 16.3| 13.0 | 7.1| 3.3| 9.8  |\n| Qwen-2.5-VL-3B (Time-R1) | 3B| 33.5 | 21.0  | 10.5 | 21.7 | 74.6 | 53.1  | 26.0 | 51.2 | 40.0 | 21.0  | 8.7| 23.2|\n| Δ ↑  |  | **+22.0** | **+14.5** | **+6.7** | **+13.4** | **+50.4** | **+37.6** | **+17.9** | **+34.9** | **+27.0** | **+13.9**| **+5.4** | **+13.4** |\n| Qwen-2.5-VL-7B (Base)    | 7B| 24.9  | 16.0  | 8.0  | 16.3  | 58.7 | 38.3 | 16.6 | 37.9      | 34.3 | 21.6  | 12.9  | 22.9 |\n| Qwen-2.5-VL-7B (Time-R1) | 7B   | 41.6| 28.5 | 15.6| 28.6  | 77.4 | 60.0   | 34.1 | 57.2  | 56.2  | 37.4 | 20.4 | 38.0 |\n| Δ ↑ |  | **+16.7** | **+12.5** | **+7.6** | **+12.3** | **+18.7** | **+21.7** | **+17.5** | **+19.3** | **+21.9** | **+15.8**| **+7.5** | **+15.1** |\n| MiMo-VL-7B (Base) | 7B   | 22.4 | 12.6| 6.6  | 15.7  | 48.5 | 27.0 | 12.1  | 31.7 | 31.3  | 19.7  | 12.1  | 24.2 |\n| MiMo-VL-7B (Time-R1)  | 7B   | 41.2   | 27.8 | 15.1| 27.4| 79.9| 63.9| 33.4 | 53.9  | 45.6| 27.2  | 14.2 | 31.9 |\n| Δ ↑  || **+18.8** | **+15.2** | **+8.5** | **+11.7** | **+31.4** | **+36.9** | **+21.3** | **+22.2** | **+14.3** | **+7.5**    | **+2.1** | **+7.7**  |\n| InternVL-2B (Base)  | 2B | 16.3 | 6.3| 2.3 | 11.7  | 20.9   | 7.8  | 1.9  | 15.4 | 18.6  | 8.5   | 3.1 | 14.2 |\n| InternVL-2B (Time-R1)    | 2B   | 21.8 | 9.5  | 4.1 | 14.8 | 24.0 | 11.5 | 3.5  | 15.7 | 20.6  | 9.5  | 3.9  | 14.2 |\n| Δ ↑ | | **+5.5**  | **+3.2**  | **+1.8** | **+3.1**  | **+3.1**  | **+3.7**  | **+1.6**  | **+0.3**  | **+2.0**  | **+1.0**| **+0.8** | **+0.0**  |\n| InternVL-8B (Base) | 8B| 17.4  | 8.3  | 3.4 | 11.8 | 27.8| 11.9 | 3.7 | 20.6  | 33.1| 18.4| 10.3| 24.0 |\n| InternVL-8B (Time-R1)| 8B| 38.0 | 22.5 | 9.2  | 24.2 | 70.0| 45.1 | 18.3| 44.1| 46.8 | 25.9 | 11.7  | 31.1  |\n| Δ ↑  | | **+20.6** | **+14.2** | **+5.8** | **+12.4** | **+42.2** | **+33.2** | **+14.6** | **+23.5** | **+13.7** | **+7.5**| **+1.4** | **+7.1**  |\n| TimeSuite-7B (Base) | 8B| 31.1  | 18.0| 8.8| 21.6 | 70.1 | 48.5 | 24.2 | 45.7 |-|-|-|-|\n| TimeSuite-7B (Time-R1)| 8B| 33.3 | 19.7 | 9.2 | 22.3 | 71.7| 49.5 | 24.2| 47.2 |-|-|-|-|\n| Δ ↑  | | **+2.2** | **+1.7** | **+0.4** | **+0.7** | **+1.6** | **+1.0** | **+0.0** | **+1.5** |-|-|-|-|\n\n\nWe observe several key findings, revealing that our approach is generally applied to many base models. \n\nFirst, **models of different sizes can also benefit from our approach**. For instance, the Qwen-VL-3B model achieves a +13.4 mIoU improvement on TVGBench and a +34.9 mIoU improvement on Charades. This is attributed to the fact that our training data is distilled based on filtering from Qwen-VL-7B, demonstrating that our RL-based method can effectively learn from the data.\n\nSecond, **models with different backbones also benefit**. For example, InternVL-8B achieves a +12.4 mIoU improvement on TVGBench and +23.5 on Charades, while MiMo-VL-8B improves by +11.7 and +22.2 on TVGBench and Charades, respectively.\n\nAdditionally, we notice that when using a smaller model with a different backbone, the transferability of the training data becomes less effective, but still beneficial. For example, training InternVL-2B on data filtered by Qwen-2.5-VL-7B only yields marginal gains, improving only +3.1 on TVGBench and +0.3 on Charades. Similar phenomenon is observed on the TimeSuite-7B. This is mainly due to architectural differences, and the data filtered by Qwen-2.5-VL-7B are not well-suited for RL training on InternVL-2B. However, we expect that re-filtering the data using InternVL itself could yield improvements comparable to those observed when transferring from Qwen-2.5-VL-7B to Qwen-2.5-VL-3B.\n\n**Q2: Does the improvement in VQA performance come from using VQA data during RL?**\n\nNo, and we sincerely apologize for the confusion. As clarified in the Appendix, there was a typo in the main text—we did not use any VQA data during reinforcement learning. The improvement is solely attributed to our proposed method, without reliance on external VQA supervision.\n\n**Q3: Could you provide an ablation study on the reward design?**\n\nYes, certainly. In response to your request, we provide an comprehensive ablation on the reward design as follows: \n- the CoT template: $r\\_{\\rm{format}}$\n- original IoU reward: $r\\_{\\rm{IoU}}=\\frac{[t\\_s,t\\_e]\\cap[t\\_s',t\\_e']}{[t\\_s,t\\_e]\\cup[t\\_s',t\\_e']}$\n- only the second term of the tIoU-based reward: $r\\_{\\rm{tIoUsec}}=1-\\frac{|t\\_s-t\\_s'|}{t}\\cdot\\frac{|t\\_e-t\\_e'|}{t}$\n- Exact Matching: $r\\_{\\rm{em}}=1 \\rm{if\\ t\\_s=t\\_s'\\ and\\ t\\_e=t\\_e'\\ else\\ 0}$ \n- Absolute Error: $r\\_{\\rm{abs}}=1 - \\frac{|t\\_s-t\\_s'| + |t\\_e-t\\_e'|}{2t}$\n- RMSE: $r\\_{\\rm{rmse}}=1 - \\sqrt{(\\frac{t\\_s-t\\_s'}{t})^2 + (\\frac{t\\_e-t\\_e'}{t})^2}$\n\nThe results, shown below, demonstrate the effectiveness of the tIoU reward design. \n\n| |   | TVGBench |        |      |        | Charades |        |      |        | ActivityNet |        |      |\n|-----------------------------------|--------|----------|--------|------|--------|----------|--------|------|--------|-------------|--------|------|\n| Model                             | R1@0.3 | R1@0.5   | R1@0.7 | mIoU | R1@0.3 | R1@0.5   | R1@0.7 | mIoU | R1@0.3 | R1@0.5      | R1@0.7 | mIoU |\n| $r\\_{\\rm{tIoU}}+r\\_{\\rm{format}}$ (ours) | **41.8**   | **29.4**     | **16.4**   | **29.2** | 78.1   | 60.8     | 35.3   | **58.1** | **58.1**  | **39.0**        | **21.4**   | **40.5** |\n| $r\\_{\\rm{format}}$| 27.1   | 18.0     | 10.1   | 18.8 | 60.4   | 39.2     | 17.4   | 39.3 | 35.1   | 22.2        | 13.1   | 26.0 |\n| $r\\_{\\rm{tIoU}}$| 40.5   | 27.6     | 15.4   | 28.0 | **78.4**   | **61.6**     | **36.1**   | 54.0 | 55.2   | 36.8        | 20.2   | 38.4 |\n| $r\\_{\\rm{IoU}}+r\\_{\\rm{format}}$|41.4 |28| 15.8| 28.4| 77.6   | 58.9| 32.7| 56.4| 58.8| 38.8| 20.7|39.4|\n| $r\\_{\\rm{em}}+r\\_{\\rm{format}}$| 26.5   | 16.8     | 9.1    | 18.3 | 61.6   | 40.3     | 18.7   | 40.4 | 35.9   | 22.5        | 13.3   | 26.4 |\n| $r\\_{\\rm{abs}}+r\\_{\\rm{format}}$| 39.1   | 27.8     | 14.8   | 27.4 | 76.2   | 59.5     | 33.9   | 52.3 | 51.4   | 32.9        | 17.8   | 35.7 |\n| $r\\_{\\rm{rmse}}+r\\_{\\rm{format}}$| 38.9   | 27.0     | 15.8   | 27.2 | 75.4   | 58.6     | 33.1   | 51.5 | 51.2   | 32.7        | 17.8   | 35.5 |\n| $r\\_{\\rm{tIoUsec}}+r\\_{\\rm{format}}$| 37.6   | 25.9     | 15.0   | 26.3 | 75.6   | 57.7     | 31.9   | 51.3 | 52.5   | 34.1        | 18.5   | 36.6 |\n\nExperimental results demonstrate that our combination of tIoU and format reward achieves the best overall performance. Without the format reward, the model can still perform reasonably well even without CoT. However, without the tIoU reward, it becomes difficult to effectively activate the model’s TVG capabilities.  While training with $r\\_{\\rm{tIoU}}$ would lead to better performance on R1@0.3/0.5/0.7 on Charades, the actual mIoU of training with $r\\_{\\rm{tIoU}}+r\\_{\\rm{format}}$ outperforms it."}, {"review_id": "LE0cgjntD4", "reviewer": "Reviewer_1QoR", "summary": "This paper tackles the problem of Temporal Video Grounding (TVG) with Large Vision-Language Models (LVLMs). The authors claim that existing LVLM-based TVG methods are sub-optimal (sometimes even perform worse than conventional feature-based small models) due to the over-penalization of false negatives during supervised fine-tuning (SFT). Therefore, to effectively supervise the model through task-specific metrics (e.g., temporal IoU), the authors proposed to leverage RL to allow direct optimization, thereby reducing rigid penalties of autoregressive losses and encouraging plausible timestamp predictions. The contributions of this work include Time-R1, TimeRFT, and TVGBench.", "questions": "Please refer to the weakness section for my questions. My major concern is the lack of clarifications and important baselines.", "limitations": "The limitations are addressed in the supplementary material, but the potential negative societal impacts are not.", "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\n1. Overall, the paper is well-written and easy to follow.\n2. The motivation for using RL is clear and reasonable -- SFT overly penalizes false negatives even when the predicted timestamps are close to the ground truth, and adopting additional timestamp prediction heads would sacrifice the pretrained language capabilities of LLMs.\n3. I like the idea of dynamic hard sampling strategy during training. The experimental results also demonstrated the effectiveness of this scheme.\n\nWeaknesses:\n\n1. It is unclear why applying GRPO (DAPO) with temporal grounding data & tIoU rewards can improve general video QA performance. To my understanding, training solely on such data would improve performance on TVG, but would also lead to lower performance on other tasks. The authors should provide more clarification for it.\n2. In Figure 4, why does SFT significantly underperform even the base model on all tasks?\n3. For the SFT-based cold start, the authors did not provide an in-depth analysis on why using dense video captioning-style thinking as CoT.\n4. Several recent important baselines / benchmarks shall be carefully discussed / compared to justify both the effectiveness of Time-R1 and TVGBench. To name some, the missing methods in Table 1 and related work are [1-5], the similar benchmarks requiring further discussions are [2, 7, 8].\n\n[1] Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning. ICML 2024.\n[2] E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding. NeurIPS 2024.\n[3] Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment. CVPR 2025.\n[4] VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding. AAAI 2025.\n[5] VideoMind: A Chain-of-LoRA Agent for Long Video Reasoning. arXiv 2025.\n[6] VideoExpert: Augmented LLM for Temporal-Sensitive Video Understanding. arXiv 2025.\n[7] ReXTime: A Benchmark Suite for Reasoning-Across-Time in Videos. NeurIPS 2024.\n[8] CG-Bench: Clue-grounded Question Answering Benchmark for Long Video Understanding. ICLR 2025.", "was_revised": true, "final_justification": "After reading the rebuttal and discussing with the authors, my concerns about this paper have been resolved. Given the good quality and detailed experiments of this paper, I would recommend acceptance of the paper and encourage the authors to include these discussions in their final version.", "rebuttal": "We appreciate the time and effort you invested in providing these detailed observations, questions, and comments. We have carefully considered your comments and outlined our responses and proposed revisions below. We hope these clarifications and revisions address your concerns and further enhance the manuscript.\n\n**W1: Why does the TVG task help improve VQA performance?**\n\n**From the data perspective**, the TVG task equips the model with the ability to localize relevant segments based on a textual query. This capability is crucial for long-video VQA, as it helps the model focus on the most pertinent moments and reduces the distraction from irrelevant or redundant information.\n\n**From the learning perspective**, our RL-based training mitigates the risk of overfitting to noisy or false negative data by allowing the model to learn from its own generated samples, enhancing the generalization ability of the model. It is the RL loss design and training paradigm that incentivizes model's capability without sacrificing its original ability.\n\nFurthermore, prior work such as TimeSuite[ICLR'25] has shown that jointly training on TVG + VQA data via supervised fine-tuning (SFT) significantly boosts VQA performance. This indirectly validates the utility of the TVG task. Our RL-based paradigm achieves similar benefits without relying on any VQA data, highlighting its ability to enhance general-purpose video understanding.\n\n**W2: Why does SFT underperforms base model?**\n\nWe have provided some clarifications and additional results in Appendix Figure S1. Specifically, we implemented two versions of supervised fine-tuning: Full-parameter fine-tuning of the LLM (denoted as SFT) and LoRA-based fine-tuning of the LLM (denoted as SFT-LoRA). Our RL training full-finetunes the LLM.\nWe present the detailed experiment results below:\n\n| Model    |        | TVGBench |        |      |        | Charades |        |      |        | ActivityNet |        |      |\n|----------|--------|----------|--------|------|--------|----------|--------|------|--------|-------------|--------|------|\n|          | R1@0.3 | R1@0.5   | R1@0.7 | mIoU | R1@0.3 | R1@0.5   | R1@0.7 | mIoU | R1@0.3 | R1@0.5      | R1@0.7 | mIoU |\n| Base     | 24.9   | 16       | 8      | 16.3 | 58.7   | 38.3     | 16.6   | 37.9 | 34.3   | 21.6        | 12.9   | 22.9 |\n| SFT      | 25.9   | 14.4     | 5.8    | 15.4 | 49.5   | 31.3     | 12.7   | 31.2 | 27.7   | 13.6        | 5.8    | 15.7 |\n| SFT-LoRA | 38.6   | 24.5     | 14.6   | 25.9 | 74     | 55.7     | 29.9   | 53.2 | 52.3   | 34.3        | 18.8   | 35.1 |\n| RL       | **41.8**   | **29.4**     | **16.4**   | **29.2** | **78.1**   | **60.8**     | **35.3**   | **58.1** | **58.1**   | **39.0**        | **21.4**   | **40.5** |\n\nWe observed that full fine-tuning via SFT leads to severe overfitting. **While SFT-LoRA partially mitigates this issue and improves performance on TVG tasks, it still leads to a slight degradation in VQA performance as shown in Figure S1**.\n\nIn contrast, **RL consistently preserves generalization and improves performance**. For example, on ActivityNet, RL boosts mIoU from 16.3 to 29.2, whereas SFT drops it to 15.4, and SFT-LoRA only improves it to 25.9. On the VideoMME VQA benchmark, RL improves performance from 53.0 to 54.2, while SFT-LoRA decreases it to 51.7.\n\n**W3: Why use dense video captions as cold-start data?**\n\nWe observed that, without any cold-start initialization, the model gradually learns to generate video descriptions through Chain-of-Thought (CoT) reasoning. Building on this empirical observation, we employ SFT-based cold-start using dense video captions to accelerate convergence toward the format reward and to reduce the length of the reasoning chains.\n\n**W4: The paper lacks baselines and benchmark comparisons.**\n\nThanks for your suggestions, we will expand our related work section to include discussion and citation of the suggested methods.\nIn addtion, we now provide comparisions against additional baseline methods with corresponding evaluation metrics on Charades and TVGBench. As shown below, our Time-R1 still achieves state-of-the-art results: \n|                          |      |           | Charades|          |           |           ActivityNet |          |           ||\n|--------------------------|------|-------------------|--------|--------|------|---------------|--------|--------|------|\n| Model                     | Size | R1@0.3 | R1@0.5 | R1@0.7 | mIoU | R1@0.3 | R1@0.5 | R1@0.7 | mIoU |\n| **ZeroShot**             |      |                   |        |        |      |               |        |        |      |\n| Momentor                 | 7B    | 42.6              | 26.6   | 11.6   | 28.5 | 42.9          | 23.0   | 12.4   | 29.3 |\n| VideoChat-TPO | 7B    | 58.3              | 40.2   | 18.4   | 38.1 | -             | -      | -      | -    |\n| VTG-LLM                  | 7B    | -                 | 33.8   | 15.7   | -    | -             | -      | -      | -    |\n| VideoMind-2B             | 2B   | 67.6              | 51.1   | 26.0   | 45.2 | 44.0          | 26.5   | 12.6   | 30.1 |\n| VideoMind-7B             | 7B   | 73.5              | 59.1   | 31.2   | 50.2 | 48.4          | 30.3   | 15.7   | 33.3 |\n| VideoExpert | 7B    | 61.5              | 40.3   | 20.9   | 41.1 | -             | -      | -      | -    |\n| Time-R1-3B | 3B   | 74.6              | 53.1   | 26.0   | 51.2 | 40.0          | 21.0   | 8.7    | 23.2 |\n| Time-R1-7B | 7B   | **78.1**              | **60.8**   | **35.3**   | **58.1** | **58.1**          | **39.0**   | **21.4**   | **40.5** |\n| **FineTune**             |      |                   |        |        |      |               |        |        |      |\n| VideoChat-TPO| 7B    | 77.0              | 65.0   | 40.7   | 55.0 | -             | -      | -      | -    |\n| VideoExpert | 7B    | 74.3              | 60.8   | 36.5   | 52.2 | -             | -      | -      | -    |\n| Time-R1-3B | 3B   | 78.7              | 64.1   | 36.9   | 59.9 | 66.8          | 46.8   | 24.7   | 46.1 |\n| Time-R1-7B | 7B   | **82.8**              | **72.2**   | **50.1**   | **60.9** | **73.3**          | **55.6**   | **34.0**   | **52.1** |\n\n\n**Regarding the benchmark comparison**: \n- CG-Bench[8] is a multiple-choice benchmark, which by nature is not a timestamp prediction task. \n- E.T.Bench[2], as shown in Table 4 on Page 11 of its paper, uses TVG data limited to Charades, EgoNLQ, and QVHighlights. \n- RexTime[7] only includes ActivityNet and QVHighlights. \n\nIn contrast, our benchmark covers Charades, ActivityNet, EgoNLQ, TaCoS, and HiREST, with balanced distribution on video length, video domain and query center, making it potentially a more comprehensive benchmark for the true TVG task. Besides, we re-annotated each sample with consistent semantics and re-balanced the distribution. This ensures the benchmark's reliability and representativeness, allowing it to serve as a useful evaluation suite."}, {"review_id": "fAAt6hkBjy", "reviewer": "Reviewer_iVA4", "summary": "This paper introduces Time-R1, a post-training framework based on reinforcement learning (RL) designed to enhance the temporal video grounding (TVG) capabilities of large vision-language models (LVLMs).\nTo enable efficient RL-based training, the authors propose an appropriate data curation and filtering strategy, as well as a reward model, and further introduce a compact benchmark to validate their approach.\nThe method achieves state-of-the-art performance across various benchmarks.", "questions": "1. Efficiency of Training: SFT vs. Time-R1\nHow does the computational cost and convergence speed of training a model using large-scale supervised fine-tuning (SFT) compare to training with the curated RL-friendly data in Time-R1? Specifically, is Time-R1 more efficient in terms of GPU hours, memory usage, or time to convergence, given the smaller yet carefully selected dataset?\n\n2. (Minor) In Supplementary Table S5, does “TimeZero-7B” refer to the Time-R1 method with the Qwen2.5-VL-7B backbone, or is it a different model?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. Time-R1 and TimeRFT\n\nThis work pioneers the use of reinforcement learning (RL)-based post-training for large vision-language models (LVLMs) in the context of temporal video grounding (TVG). \n\n2. Data Efficiency and Strong Generalization\n\nThe proposed method demonstrates that with a curated, moderately sized RL-friendly dataset (TimeRFT), it is possible to surpass the performance of large-scale supervised fine-tuning approaches.\n\n3. TVG Benchmark\nThe paper constructs a new, systematic benchmark (TVGBench) specifically designed for temporal video grounding. TVGBench reflects real-world task complexity and includes a balanced distribution of query types (human, object, environment, etc.), supporting objective and reproducible evaluation across diverse model architectures.\n\nWeaknesses\n\n1. Attribution of SOTA Performance to Backbone Quality\nIt remains unclear whether the observed SOTA performance is primarily due to the use of a stronger LVLM backbone (Qwen2.5-VL-7B), rather than the proposed training and reward strategies. In contrast, the SFT baselines utilize a variety of vision encoders and language models (e.g., TimeChat: ViT-G/14 from EVA-CLIP + LLaMA-2-7B; TIMESUITE: UMT-L + Mistral-7B; VideoChat-Flash: UMT-L + Qwen2-7B; TRACE: ViT-L/14 + Mistral-7B). The main results do not directly demonstrate whether Time-R1 yields significant performance improvements over these baselines when using the same vision encoder and language model architecture. A controlled head-to-head comparison on identical backbones would be necessary to fairly isolate the effectiveness of the proposed method.\n\n2. Effectiveness of the tIoU Reward\nSection 3.2 introduces the tIoU reward to promote deeper temporal understanding. However, in the ablation study (Table 2, Row 1 vs. Row 2), adding tIoU appears to decrease performance. The authors should provide a more detailed analysis or hypothesis as to why incorporating tIoU does not consistently yield gains, and under what circumstances (e.g., data distribution, filtering, w/ Multi-Epoch) it becomes effective.\n\n3. Ablation of Reasoning Template Reward\nWhile the paper claims that the reasoning template reward encourages better intermediate reasoning and thus benefits temporal grounding, it is not empirically clear to what extent this mechanism enhances the model's reasoning capability or TVG performance. A direct ablation comparing models with and without the reasoning template reward on reasoning quality, as well as an assessment of its impact across various benchmarks (as in Table 1 and Table S1), would greatly strengthen the claims.\n\n4. Quantitative Comparison of Inference Speed\nThe supplementary material mentions the slow inference speed as a limitation. However, a quantitative comparison of inference time or throughput between Time-R1 and relevant baselines is missing. Such a comparison would help practitioners assess the real-world trade-offs between accuracy and efficiency.\n\n5. (minor) typo in Table 2, trainning -> training", "was_revised": true, "final_justification": "The rebuttal has addressed most of my concerns. While I recognize some limitations based on other reviews, I acknowledge the various contributions of the paper and therefore give a borderline accept. I hope the authors incorporate the rebuttal feedback to produce a strong final version.", "rebuttal": "We appreciate the time and effort you invested in providing these detailed observations, questions, and comments. We have carefully considered your comments and outlined our responses and proposed revisions below. We hope these clarifications and revisions address your concerns and further enhance the manuscript.\n\n**W1: Could the SOTA performance be attributed to a stronger backbone rather than the proposed training method and reward design?**\n\nIn the following experiments, we aim to show that (1) open-sourced academic models, like TimeSuite, can benefit from our RL post-training; and (2) many well-pretrained model across model sizes and backbones can be substantially improved via our training procedure.\n\nWe conducted additional experiments with RL-based post-training on other base models, including **TimeSuite-7B, Qwen-2.5-VL-3B, MIMO-VL-7B, InternVL-2B, and InternVL-8B**.  We post-train all base models using our 2.5K data filtered by Qwen-2.5-VL-7B. The results are presented as follows:\n\n|||| TVGBench  |||| Charades  |||| ActivityNet |||\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| Model | Size | R1@0.3| R1@0.5| R1@0.7| mIoU | R1@0.3| R1@0.5| R1@0.7 | mIoU| R1@0.3| R1@0.5 | R1@0.7| mIoU  |\n| Qwen-2.5-VL-3B (Base) | 3B | 11.5 | 6.5  | 3.8  | 8.3  | 24.2  | 15.5 | 8.1| 16.3| 13.0 | 7.1| 3.3| 9.8  |\n| Qwen-2.5-VL-3B (Time-R1) | 3B| 33.5 | 21.0  | 10.5 | 21.7 | 74.6 | 53.1  | 26.0 | 51.2 | 40.0 | 21.0  | 8.7| 23.2|\n| Δ ↑  |  | **+22.0** | **+14.5** | **+6.7** | **+13.4** | **+50.4** | **+37.6** | **+17.9** | **+34.9** | **+27.0** | **+13.9**| **+5.4** | **+13.4** |\n| Qwen-2.5-VL-7B (Base)    | 7B| 24.9  | 16.0  | 8.0  | 16.3  | 58.7 | 38.3 | 16.6 | 37.9      | 34.3 | 21.6  | 12.9  | 22.9 |\n| Qwen-2.5-VL-7B (Time-R1) | 7B   | 41.6| 28.5 | 15.6| 28.6  | 77.4 | 60.0   | 34.1 | 57.2  | 56.2  | 37.4 | 20.4 | 38.0 |\n| Δ ↑ |  | **+16.7** | **+12.5** | **+7.6** | **+12.3** | **+18.7** | **+21.7** | **+17.5** | **+19.3** | **+21.9** | **+15.8**| **+7.5** | **+15.1** |\n| MiMo-VL-7B (Base) | 7B   | 22.4 | 12.6| 6.6  | 15.7  | 48.5 | 27.0 | 12.1  | 31.7 | 31.3  | 19.7  | 12.1  | 24.2 |\n| MiMo-VL-7B (Time-R1)  | 7B   | 41.2   | 27.8 | 15.1| 27.4| 79.9| 63.9| 33.4 | 53.9  | 45.6| 27.2  | 14.2 | 31.9 |\n| Δ ↑  || **+18.8** | **+15.2** | **+8.5** | **+11.7** | **+31.4** | **+36.9** | **+21.3** | **+22.2** | **+14.3** | **+7.5**    | **+2.1** | **+7.7**  |\n| InternVL-2B (Base)  | 2B | 16.3 | 6.3| 2.3 | 11.7  | 20.9   | 7.8  | 1.9  | 15.4 | 18.6  | 8.5   | 3.1 | 14.2 |\n| InternVL-2B (Time-R1)    | 2B   | 21.8 | 9.5  | 4.1 | 14.8 | 24.0 | 11.5 | 3.5  | 15.7 | 20.6  | 9.5  | 3.9  | 14.2 |\n| Δ ↑ | | **+5.5**  | **+3.2**  | **+1.8** | **+3.1**  | **+3.1**  | **+3.7**  | **+1.6**  | **+0.3**  | **+2.0**  | **+1.0**| **+0.8** | **+0.0**  |\n| InternVL-8B (Base) | 8B| 17.4  | 8.3  | 3.4 | 11.8 | 27.8| 11.9 | 3.7 | 20.6  | 33.1| 18.4| 10.3| 24.0 |\n| InternVL-8B (Time-R1)| 8B| 38.0 | 22.5 | 9.2  | 24.2 | 70.0| 45.1 | 18.3| 44.1| 46.8 | 25.9 | 11.7  | 31.1  |\n| Δ ↑  | | **+20.6** | **+14.2** | **+5.8** | **+12.4** | **+42.2** | **+33.2** | **+14.6** | **+23.5** | **+13.7** | **+7.5**| **+1.4** | **+7.1**  |\n| TimeSuite-7B (Base) | 8B| 31.1  | 18.0| 8.8| 21.6 | 70.1 | 48.5|24.2| 45.7|-|-|-|-|\n| TimeSuite-7B (Time-R1)| 8B| 33.3 | 19.7 | 9.2 | 22.3 | 71.7 | 49.5 | 24.2| 47.2|-|-|-|-|\n| Δ ↑  | | **+2.2** | **+1.7** | **+0.4** | **+0.7** | **+1.6** | **+1.0** | **+0.0** | **+1.5** |-|-|-|-|\n\nWe observe several key findings, revealing that our approach is generally applied to many base models. \n\nFirst, **models of different sizes can also benefit from our approach**. For instance, the Qwen-VL-3B model achieves a +13.4 mIoU improvement on TVGBench and a +34.9 mIoU improvement on Charades. This is attributed to the fact that our training data is distilled based on filtering from Qwen-VL-7B, demonstrating that our RL-based method can effectively learn from the data.\n\nSecond, **models with different backbones also benefit**. For example, InternVL-8B achieves a +12.4 mIoU on TVGBench and +23.5 on Charades, while MiMo-VL-8B improves by +11.7 and +22.2 on TVGBench and Charades, respectively.\n\nAdditionally, we notice that **when using weaker model with a different backbone, the transferability of the training data becomes less effective, but still beneficial**. For example, training InternVL-2B on data filtered by Qwen-2.5-VL-7B only yields marginal gains, improving only +3.1 on TVGBench and +0.3 on Charades. Similar phenomenon is observed on TimeSuite-7B backbone. This is mainly due to architectural differences, base model's intelligence, and the data filtered by Qwen-2.5-VL-7B are not well-suited for RL training on InternVL-2B. However, we expect that filtering the data using base model itself could yield improvements comparable to those observed when transferring from Qwen-2.5-VL-7B to Qwen-2.5-VL-3B.\n\n**W2: What is the role of tIoU and under what conditions is it most effective?**\n\n**tIoU is particularly effective when combined with multi-epoch (ME) training. Further including Gaussian filtering and sample filtering consistently contribute to the performance gains.**\n\nComparing Row 2 and Row 6 in Table 2, the combination of tIoU and ME achieves the best performance on R1@0.7, with a score of 16.4. In contrast, using IoU with ME (Row 4) yields lower performance on R1@0.7 of 14.2.  \n\nIn Appendix Figure S2, tIoU consistently improves performance as the number of training epochs increases, and tIoU outperforms IoU in the multi-epoch training. We attribute this to the superior boundary sensitivity of tIoU.\n\nIn addition, Gaussian filtering (GF) consistently improves model performance, and its effectiveness is further amplified when combined with tIoU. Specifically, comparing Row 1 and Row 3 (IoU + GF), as well as Row 2 and Row 6 (tIoU + GF), we observe that tIoU leads to a performance gain of +3.5%, whereas IoU yields only a marginal improvement of +0.2%. Furthermore, sample filtering, as shown in the last row, provides additional stability to the model’s performance, demonstrating its complementary benefit in our training pipeline.\n\n**W3: The paper lacks ablation studies on the template reward function.**\n\nBelow, we provide an ablation of the format reward.\nComparing row 1 and row 2, the format reward consistently improves the performance. While training without reasoning format reward would lead to better performance on R1@0.3/0.5/0.7 on Charades, the actual mIoU of training with format reward outperforms training without format reward.\n\n|||TVGBench||||Charades||||ActivityNet|||\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| Reward | R1@0.3 | R1@0.5| R1@0.7 | mIoU | R1@0.3 | R1@0.5| R1@0.7 | mIoU | R1@0.3 | R1@0.5| R1@0.7 | mIoU |\n| $r\\_{\\rm{tIoU}}+r\\_{\\rm{format}}$ (ours) | **41.8**| **29.4**| **16.4**| **29.2** | 78.1| 60.8| 35.3| **58.1** | **58.1**  | **39.0**| **21.4**   | **40.5** |\n| $r\\_{\\rm{tIoU}}$| 40.5| 27.6| 15.4| 28.0 | **78.4**   | **61.6**| **36.1**| 54.0 | 55.2| 36.8| 20.2   | 38.4 |\n| $r\\_{\\rm{format}}$| 27.1| 18.0| 10.1| 18.8 | 60.4| 39.2| 17.4   | 39.3 | 35.1| 22.2| 13.1   | 26.0 |\n\nMoreover, if you have interest, we provide a more comprehensive ablation study on the choice of tIoU in response to Reviewer okJ6.\n\n**W4: The inference speed of this method seems slow. Is there any comparison available?**\n\nTo address this, we conduct a comparison of inference speeds with and without CoT reasoning process. We adapt our model for implementing both vLLM and HuggingFace Transformers library, and evaluate the inference speed under both frameworks. The results below demonstrate that with vLLM library, our method achieves substantial improvements in inference efficiency, significantly reducing latency while maintaining strong performance.\n\n| test with/without CoT using 8×H100 | TVGBench| Charades| ActivityNet|\n|-|-|-|-|\n| Sample Num| 800| 3720| 17031|\n| vLLM inference time| 8.3min/6.9min | 11.7min/11.2min | 36.1min/33.2min |\n| transformers inference time| 42min/27.3min | 3.3hour/1.2hour | 15hour/7.5hour  |\n\nTherefore, **for a 1.5-minute input video sampled at 2 fps, our model—when deployed with vLLM—achieves an average inference time of 4.98 seconds with CoT and 4.14 seconds without CoT**, which is actually not too slow.\nGiven that most models adopt similar MLLM architectures, their inference speed is comparable with ours inference without CoT.\n\n**Q1: Comparison of training efficiency between RL and SFT.**\n\nWe compared RL and SFT across various efficiency metrics, including training time, memory usage and experimental results below.\nTo further demonstrate the data efficiency of the RL paradigm, we trained an SFT model with full size 339K TVG samples and found that it still underperforms compared to our RL model trained with only 2.5K samples. The SFT-LoRA-2.5K only underperforms SFT-LoRA-339K slightly, and even slightly surpasses SFT-LoRA-339K on ActivityNet. \n\n| Model  | TrainTime | MaxGPUMem | | TVGBench ||||Charades||||ActivityNet|||\n|-|-|-|-|-|-|-|-|-|-|--|-|-|-|-|\n|||| R1@0.3 | R1@0.5   | R1@0.7 | mIoU | R1@0.3 | R1@0.5| R1@0.7 | mIoU | R1@0.3 | R1@0.5| R1@0.7 | mIoU |\n| RL-2.5K (5 epochs)| 15h|78G| **41.8**| **29.4**| **16.4**| **29.2**| **78.1**| **60.8**| **35.3**| **58.1**| **58.1**| **39.0**| **21.4**| **40.5** |\n| SFT-LoRA-2.5K (5 epochs) | 3.26h| 76G| 38.6| 24.5| 14.6| 25.9 | 74| 55.7| 29.9   | 53.2 | 52.3| 34.3| 18.8   | 35.1 |\n| SFT-LoRA-339K (1 epoch)  | 5d2h| 76G| 38.9| 28.2| 15.2| 27.4 | 72.4| 54.7| 26.9   | 51.7 | 45.7   | 29.3| 16.4   | 30.5 |\n\nThese results highlight: (1) the strong potential of RL to achieve higher performance with significantly less data. (2) our data filtering strategy is both effective for RL and SFT. While RL incurs higher computational cost, its superior performance with minimal data suggests strong potential for future advancements. \n\n**Q2: Typo in Table 2 and in Supplementary Table S5**\n\nWe apologize that \"TimeZero-7B\" is a typo. It should refer to the \"Time-R1-7B\". We will fix these typos in our revised version."}, {"review_id": "gjCF8nhytM", "reviewer": "Reviewer_okJ6", "summary": "This paper applies GRPO (Guided Reinforcement Policy Optimization) to the problem of Temporal Video Grounding, which involves identifying the precise time interval during which a specified event occurs in a video. The authors also make strong contributions on the data side by constructing a benchmark tailored to this task through careful modification of existing datasets. Their approach demonstrates strong performance across multiple downstream tasks.\n\nHowever, I feel the post-training component is less compelling. The proposed RL method is largely similar to standard GRPO, differing mainly in the introduction of a new reward function (tIOU), which is effectively a form of reward shaping—a fairly common practice in RL. This makes it feel like an application of GRPO to a new task, but with limited technical contribution and novelty.", "questions": "- Can the authors ablate the reward function proposed? Here are some simple reward functions that I can think of. In interest of time it may not be easy to complete all, but at least some ablations are important to analyze the impact of the reward function:\n   - A simple GRPO style validation, i.e., is the proposed time interval an exact match?\n   - Absolute Error or RMSE\n   - The second term in the proposed tIoU\n- After seeing the use of tIoU vs IoU in the Appendix, there seems to be no clear quantitative reason why the proposed reward function should be used. Are there any qualitative examples?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n* The paper is clearly written and easy to follow.\n* The contributions are broad, covering multiple aspects including dataset construction, benchmarking, and post-training.\n\n**Weaknesses**\nMost of my concerns are focused on the novelty and technical contributions of the post-training component:\n\n* The proposed post-training approach using RL is very similar to GRPO, differing primarily in the choice of reward function (the proposed tIOU). This is a fairly minor contribution on its own, since GRPO is designed to allow simple task-specific reward shaping through human-defined or heuristic reward functions.\n* Relatedly, no ablation or analysis is demonstrating why the proposed reward function is better than simpler, more intuitive alternatives.", "was_revised": true, "final_justification": "Overall, I believe this is solid applied research that applies GRPO to a novel problem. While the methodological novelty is limited, the authors and other reviewers have highlighted several other strengths of the paper. Based on the discussion period, I have decided to raise my score to 'Accept'.", "rebuttal": "We appreciate the time and effort you invested in providing these detailed observations, questions, and comments. We have carefully considered your comments and outlined our responses and proposed revisions below. We hope these clarifications and revisions address your concerns and further enhance the manuscript.\n\n**W1: This work's post-training strategy is almost the same as GRPO.**\n\n- Time-R1 is the first work to introduce the RLVR-style post-training paradigm into temporal video grounding. We address the critical yet underexplored challenge TVG task in this domain by proposing a novel paradigm that fundamentally redefines how long-video understanding can be approached. Our method not only offers a new perspective but also achieves state-of-the-art performance.\n- Our contribution lies in a complete post-training framework that includes Time-R1, TimeRFT, and TVGBench. GRPO serves as a key training objective within Time-R1, but is just one component of the overall comprehensive design.\n- Moreover, we provide detailed ablations to dissect GRPO's design, such as in Appendix Table S3: Ablation of KL and Thinking Process in GRPO; Table S4: Comparison of DAPO and GRPO; and Ablation Study on Reward Design added during rebuttal below.  These insights contribute to a deeper understanding of how RL paradigm impacts long-video understanding reasoning.\n\n**W2 & Q1: Missing ablation to validate the reward design.**\n\nFollowing your suggestions, we provide a more comprehensive ablation on reward design to demonstrate the effectiveness of our tIoU reward:\n\n- original IoU reward: $r\\_{\\rm{IoU}}=\\frac{[t\\_s,t\\_e]\\cap[t\\_s',t\\_e']}{[t\\_s,t\\_e]\\cup[t\\_s',t\\_e']}$\n- only the second term of the tIoU-based reward: $r\\_{\\rm{tIoUsec}}=1-\\frac{|t\\_s-t\\_s'|}{t}\\cdot\\frac{|t\\_e-t\\_e'|}{t}$\n- Exact Matching: $r\\_{\\rm{em}}=1\\ \\rm{if\\ t\\_s=t\\_s'\\ and\\ t\\_e=t\\_e'\\ else\\ }0$ \n- Absolute Error: $r\\_{\\rm{abs}}=1 - \\frac{|t\\_s-t\\_s'| + |t\\_e-t\\_e'|}{2t}$\n- RMSE: $r\\_{\\rm{rmse}}=1 - \\sqrt{(\\frac{t\\_s-t\\_s'}{t})^2 + (\\frac{t\\_e-t\\_e'}{t})^2}$\n\n\nAll ablation studies here include the format reward, we omit it for simplicity.\n\n|                                   |        | TVGBench |        |      |        | Charades |        |      |        | ActivityNet |        |      |\n|-----------------------------------|--------|----------|--------|------|--------|----------|--------|------|--------|-------------|--------|------|\n| Model                             | R1@0.3 | R1@0.5   | R1@0.7 | mIoU | R1@0.3 | R1@0.5   | R1@0.7 | mIoU | R1@0.3 | R1@0.5      | R1@0.7 | mIoU |\n| $r\\_{\\rm{tIoU}}$ (ours) | **41.8**| **29.4**     | **16.4**   | **29.2** | **78.1**   | **60.8**     | **35.3**   | **58.1** | **58.1**  | **39.0**        | **21.4**   | **40.5** |\n| $r\\_{\\rm{IoU}}$                       |41.4 |28| 15.8| 28.4| 77.6   | 58.9| 32.7| 56.4| 58.8| 38.8| 20.7|39.4|\n| $r\\_{\\rm{tIoUsec}}$                    | 37.6   | 25.9     | 15.0   | 26.3 | 75.6   | 57.7     | 31.9   | 51.3 | 52.5   | 34.1        | 18.5   | 36.6 |\n| $r\\_{\\rm{em}}$                         | 26.5   | 16.8     | 9.1    | 18.3 | 61.6   | 40.3     | 18.7   | 40.4 | 35.9   | 22.5        | 13.3   | 26.4 |\n| $r\\_{\\rm{abs}}$                        | 39.1   | 27.8     | 14.8   | 27.4 | 76.2   | 59.5     | 33.9   | 52.3 | 51.4   | 32.9        | 17.8   | 35.7 |\n| $r\\_{\\rm{rmse}}$                       | 38.9   | 27.0     | 15.8   | 27.2 | 75.4   | 58.6     | 33.1   | 51.5 | 51.2   | 32.7        | 17.8   | 35.5 |\n\nExperimental results show that our tIoU outperforms all candidate methods.\n\n**Q2: Qualitative examples to illustrate advantage of tIoU over IoU.**\n\nCompared to IoU, tIoU offers a more reasonable definition by introducing a penalty for unreasonable boundary predictions. As shown in our above table in response to Q1, tIoU consistently outperforms IoU across all three benchmarks. Since adding videos or images is not allowed during the rebuttal phase, we will include qualitative examples demonstrating the advantages of tIoU over IoU in the appendix in our revised version."}]

Author rebuttal:

Meta-review: This paper presents a reinforcement learning (RL) based post-training framework that augments large vision-language models with temporal grounding capability. Initial reviews were mixed, but after the rebuttal all reviewers recommended acceptance. Although RL-based post-training is common in LLMs, the reviewers agreed that its adaptation to vision-language models here is sufficiently innovative. The AC concurs with the reviewers' post-rebuttal assessment and recommend acceptance. The AC encourages the authors to incorporate the rebuttal materials into the camera-ready version.
Decision: Accept (poster)