Uncertainty-quantified Rollout Policy Adaptation for Unlabelled Cross-domain Video Temporal Grounding
Authors: Jian Hu
Zixu Cheng
Shaogang Gong
Isabel Guan
Jianye HAO
Jun Wang
Kun Shao
Venue: NeurIPS 2025 poster ()
Keywords: Knowledge Transfer; Video Understanding; Temporal Grounding
Primary area: applications
TL;DR: We introduce a Uncertainty-quantified Rollout Policy Adaptation method, which uses rollout variance to estimate pseudo label uncertainty and enables test-time adaptation for cross-domain temporal grounding without target labels.

Abstract: Video Temporal Grounding (TG) aims to temporally locate video segments matching a natural language description (a query) in a long video. While Vision-Language Models (VLMs) are effective at holistic semantic matching, they often struggle with fine-grained temporal
localisation. Recently, Group Relative Policy Optimisation (GRPO) reformulates the inference process as a reinforcement learning task, enabling fine-grained grounding and achieving strong in-domain performance. However, GRPO relies on labelled data, making it unsuitable in unlabelled domains. Moreover, because videos are large and expensive to store and process, performing full-scale adaptation introduces prohibitive latency and computational overhead, making it impractical for real-time deployment. To overcome both problems, we introduce a Data-Efficient Unlabelled Cross-domain Temporal Grounding method, from which a model is first trained on a labelled source domain, then adapted to a target domain using only a small number of {\em unlabelled videos from the target domain}. This approach eliminates the need for target annotation and keeps both computational and storage overhead low enough to run in real time. Specifically, we introduce \textbf{U}ncertainty-quantified \textbf{R}ollout \textbf{P}olicy \textbf{A}daptation (\textbf{URPA}) for cross-domain knowledge transfer in learning video temporal grounding without target labels. URPA generates multiple candidate predictions using GRPO rollouts, averages them to form a pseudo label, and estimates confidence from the variance across these rollouts. This confidence then weights the training rewards, guiding the model to focus on reliable supervision. Experiments on three datasets across six cross-domain settings show that URPA generalises well using only a few unlabelled target videos. Codes are given in supplemental materials.

Uncertainty-quantifeel Rollout Policy Adaptation for Unlabelled Cross-domain Video Temporal Grounding
Jian Hu', Zixu Cheng' Shaogang Gong', Isabel Giane,Janyye Hao² Jun Wang*, Kun Shao
QQeen Mary University of London, 'Hong Kong University of Science and Technology. Huawei Noah's Ark Lab, UUniversity College London
https///wpyyhhiihuuhooUUURPPP
Abstract
Video Temporal Grounding (TG) aims to temporally locate video segments match- ing natural language description (a query) in long video. While Vision-Language Models (VLMs) are effective at holistic semantic matching, they often struggle with fine-grained temporal localisation. Recently, Group Relative Policy Opti- misation (GRPO) reformulates the inference process as reinforcement learning task, enabling fine-grained grounding and achieving strong in-domain performance. However, GRPO relies on labelled data, making it unsuitable in unlabelled domains. Moreover, because videos large and expensive store and process, performing full-scale adaptation introduces prohibitive latency and computational overhead, making impractical for real-time deployment. To overcome both problems, we introduce Data-Efficient Unlabelled Cross-domain Temporal Grounding method, from which model first trained on a labelled source domain, then adapted a target domain using only a small number of unlabelled videos from the target domain. This approach eliminates the need for target annotation and keeps both computational and storage overhead low enough to run in real time. Specifically, we introduce iiccertinty-quantiffe Rollout Policy Adaptation (URPA) for cross- domain knowledge transfer in learning video temporal grounding without target labels. URPA generates multiple candidate predictions using GRPO rollouts, av- erages them to form a pseudo label, and estimates confidence from the variance across these rollouts. This confidence then weights the training rewards, guiding the model to focus reliable supervision. Experiments three datasets across six cross-domain settings show that URPA generalises well using only a few unlabelled target videos.
Introduction
Temporal Grounding (TG) localises the exact temporal segment in untrimmed video that semanti- cally corresponds a given natural-language query [2]. Accurate TG fundamental to high-level
applications such as activity detection [ and embodied human computer interaction M
While VLMs are effective capturing holistic video semantics [22 18] they often struggle with fine-grained localisation, leading to suboptimal performance in temporal grounding. Some recent works apply Supervised Fine-Tuning Tuning (SFT) better align video-query pairs 39 58
However, since relevant segments typically cover only small portion of the video, the model often overfits to redundant context, limiting its ability to perform precise grounding [60]. To improve temporal reasoning, Shain-of-Thoughs (CoT) post-training introduces explicit intermediate reasoning steps
before prediction 37 50) While effective, this approach depends on manually annotated prompts,
"Corresponding author: @aookun2@hawwele
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Selgptoochpp Jebanen Training Samplet Perfomance
Mux shafl
Dropout Net
Dropout Net
Dropour Net
Sampled Sincture!
Sumplet Stricture
Sampled Strecture
Dropont
GRPO Rallout
2000
1000
7000
10000
23000
13300
17880
Samping difloent adbastoerll vix deopeut meaks
Sumpplay undipp dructaal sequiness policy
(a) Motivation of data-efficient CTG setting.
(b) Comparison between MC Dropout and GRPO rollout.
Figure 1: (a) A comparison between full-data and data-efficient adaptation in Cross-domain Temporal Grounding (CTG). Existing methods adapt on thousands of unlabelled target videos (grey), which is slow and resource-heavy. We propose a data-efficient CTG setting using only 100 200 randomly selected target videos. Despite the limited data, our method matches or exceeds performance on the TaCoS + ActivityNet task. (b) Conceptual comparison between MC Dropout and GRPO rollout. MC Dropout samples subnetworks via stochastic neuron dropout and estimates uncertainty from output diversity. GRPO rollouts similarly sample diverse structural sequences from the policy. URPA leverages this property to generate averaged pseudo labels and estimate uncertainty via rollout standard deviation, enabling uncertainty-quantinn
adaptation without ground-truth labels.
which are expensive to collect and hard to scale across domains and tasks. More recently, GRPO 14 formulates temporal grounding as a policy learning problem, where reinforcement learning used to directly optimise segment predictions [47] [26). This removes the need for hand-crafted prompts used in post-processing and achieves strong results on in-domain benchmarks. However, GRPO relies on ground-truth temporal boundaries to compute reward signals. requiring extensively labelled training videos. It limits its practical usefulness in real-world scenarios where annotations are unavailable. Moreover, GRPO-based temporal grounding performance drops significantly across domains due to distribution shift, revealing its poor generalisation to unseen data. In addition annotation constraints, the sheer scale of video data poses practical challenges. A target domain often contains thousands of videos, which are expensive to store, and time-consuming to adapt. As shown in Fig. aa), existing adaptation methods require full retraining on the entire target set. Such latency and resource-hungry makes them unsuitable for Line-sensitive applications like online or on-device deployment. We consider a more practical and generalisable approach to cross-domain temporal grounding where model trained on a labelled source domain can be efficiently adapted to an unlabelled target domain using only sparse unlabelled target videos, with minimal latency and resource demands.
To address these challenges, we introduce a Grounding approach. A model trained on a labelled source domain is adapted at test-time in deploy- ment using only unlabelled videos from the target domain (K = 100 ~ 200 in our experiments), allowing for real-time adaptation on limited compute resources. The main challenge in this approach lies in effectively leveraging limited unlabelled target data. Without annotated temporal boundaries, pseudo-labels are noisy and highly uncertain, resulting in poor cross-domain performance. To miti- gate this, we introduce explicit uncertainty quantification to assess the reliability of pscudo-labels. As illustrated in Fig [[b,, MC Dropout [[]] estimates uncertainty by sampling multiple sub-network, via stochastic dropout and computing the variance across their outputs. Similarly, GRPO rollouts produce diverse predictions by sampling from stochastic policy. Inspired by this parallel, we estimate pseudo label confidence by measuring the standard deviation across multiple rollouts per <video, query> pair, and use it to guide adaptation with uncertainty ceterraintt-qaannficattto quantification weighted model adaptation.
Unlabelled Cross-domain Temporal
To this end, we propose Unnertainty-quuatife
Rollout Policy Adaptation (URPA). After supervised training of GRPO backbone on the source domain, we perform test-time adaptation by generating G stochastic rollouts for each of the K (K=100/200 in our experiments) randomly selected unlabelled videos in the target domain. A pseudo label is constructed by averaging the predicted boundaries across rollouts, and a small margin relaxation is applied to reduce the impact of outliers. To estimate the reliability of each pseudo label, we follow the Bayesian view of MC Dropout and use the variance across rollouts as an estimate of uncertainty. Samples with lower variance, indicating higher confidence, given larger weights during lightweight gradient update. This enables fast and effective adaptation without introducing significant latency. Sec. 4 provides a theoretical analysis of how rollout-based variance quantifies uncertainty.
Our contributions are threefold: (i) We introduce an unlabelled cross-domain temporal grounding approach to rest-time model adaptation using only small number of unlabelled target-domain
2

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videos. (ii) We propose ceerttaint--uanniffen Rollout Policy Adaptation (URPA), the first rein- forcement learning-basee self-learning adaptation method that combines pseudo-label generation with uncertainty- weighteed rewards. URPA enables effective GRPO-based temporal grounding across domains without requiring ground-truth labels to compute reinforcement signals. (iii) We theoreti- cally show that rollout variance approximates Bayesian predictive variance, quantifying epistemic uncertainty, and empirically demonstrate that URPA consistently outperforms strong baselines across six cross-domain video temporal grounding benchmarks.
2 Related Works
Test-time Adaptation. Test-time domain adaptation (TTDA) adapts pre-trained model to unlabelled test data exhibiting distribution shift, prior to prediction 42 19 35]. TTDA approaches can be broadly divided into backward-free and wordwward-bee methods. Backward-free methods adapt the model on-the-fly during inference, typically by updating batch normalization (BN) statistics without backpropagation. Representative works include DUA [322, which applies running average to BN layers, and DIGA [45), which aligns distributions for semantic segmentation. While efficient per sample, such methods require adaptation for every test input, which may increase inference latency and introduce instability across samples. In contrast, backward-baa using target data before inference begins, enabling faster and more stable test-time prediction. These methods often rely on self-supervised objectives like entropy minimization [42 20 However, video data is large and difficult to store, and performing test-time adaptation on all videos is time-consune time-consuming. Hence, we leverage only a small number of videos from the target domain to perform cata-efficient test-time adaptation via backpropagation, and then apply the adapted model to evaluate the full set test video without further updates. This design ensures both adaptability and real-time efficiency, making it well-suited for practical scenarios with limited target domain data.
methods adapt the model once
Temporal Grounding. Temporal grounding [12] 24 is a lanionn task that aims to localise the snippet that corresponds to given natural language query with start and end timestamps in video. Existing methods fall into two main categories: proposal-based approaches 12 [5 6T 48 [7]. which generate candidate segments before matching them with the query; and proposal-
33 52). which directly predict the temporal boundaries in an end-to-end manner. Recently, VLM- methods (39 21
free approaches [51 [6 59 34
25
31 [5] have shown competitive performance by leveraging generalised knowledge from VLMS pre-training, while also maintaining the conversational capabilities of language models. However, both traditional and VLM- based approaches rely heavily on large amounts of labelled data and struggle to generalise to unseen domains [54] 22 43 3 Although some work has explored cross-dataset generalisation [7]47] 26),
28
16
58
this setting remains challenging due to two key issues: (i) target domain videos are often large, incurring high storage and adaptation costs; and (ii) temporal boundary annotation is labour-intensive. Moreover, our experiments show that the recent works, e.g., TimeZero 477 and Temporal-Ri (26). still perform poorly in cross-domain generalisation despite utilising GPRO improve the model's generalisability with reinforcement learning. To address this, we propose a data-efficient cross- domain temporal grounding setting and method that achieves performance comparable to fully supervised training using only small number of unlabelled target domain videos. This significantly reduces resource requirements while improving cross-domain generalisability.
Uncertainty Estimation. Uncertainty estimation aims to capture either data-inherent noise (aleatoric uncertainty) or model-driven ambiguity (epistemic uncertainty) 5 23). In vision tasks, epistemic uncertainty is commonly approximated via Bayesian neural networks such as MC Dropout [00 20),
or through approximate reasoning methods [46 36 For VLMs, existing approaches quantify
uncertainty through logit entropy [13). verbalized confidence enhanced by CoT prompting [49].
or consistency-baatt based diagnostics 633 Recent work also explores semantic-level uncertainty by
clustering outputs and computing entropy in embedding space (9]. In contrast, our URPA estimates epistemic uncertainty by computing the standard deviation across multiple rollout predictions, offering a lightweight and task-aligned signal to guide self-learning in unlabelled target domains.
3 Uncertainty-quantffee Rollout Policy Adaptation
3.1 Problem Definition
In training model for new target domain video temporal grounding, we consider a labelled source domain D, {V., N and target domain with small number of unlabelled videos
3

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Predict
thinkk Athink
Ryornat
<think..., Sithinkk
Expected
Sanswer Sanswer?
<answer....
VLM
Source Video V.
Output: <think-
The given query "stari time": "0.00", "end time": "11 slanswer
the video. <ithinks
<answer>
Query: person is stanling at a slove cooking
pred [0.00, 11.56]
1 [2.71, 10.34)
Source Model Training
Raoo
Fermut
A
Target Video V.
VLL
O2
std
gt
Group Computation
A2
Query: camera zooms dragon
Og
Ratsur
Target Adaptation
Figure 2: thoertainty-quantiee Rollout Policy Adaptation (URPA): During source model training. we perform supervised GRPO training using labelled videos V,- Specifically, the format reward Riomaa encourages the model to "think first and then answer," while accuracy reward Rao aligns the predicted temporal grounding I with the relaxed ground truth I." for supervised learning. In target model knowledge adaptation, we adapt the model using unlabelled target videos. For each video Vi, we first compute the average output over G rollouts to obtain pseudo label 7ę" Then calculate the standard deviation across these rollouts and transform it into confidence score to quantify uncertainty on pseudo labels, which then used to weight different pseudo-labels when constructing a weighted reward function for test-time target model adaptation.
D, = D (i where 1 is the annotated interval for V the number of training videos in the source and target domains are N and K respectively, with K < N. A distribution shift exists between the source and target domains, i.c. Ps * P,. The model first pre- trainrr on the labelled source domain to learn some general knowledge of video temporal grounding. It then learns adapt at test-time without labelled training in the target domain through a data-efficient unlabelled target domain videos. This real-time dromin unlabelled adaptation approach video temporal grounding aims to optimise cross-domain knowledge transfer with only a few target videos without any annotations.
3.2 Remark on Group Relative Policy Optimization
In reinforcement learning, Group Relative Policy Optimization (GRPO) is a variant of Proximal Policy Optimization (PPO) that eliminates the need for critic function. Instead, directly evaluates the quality of predictions using group of sampled responses. Given question the model generates G candidate rollout responses computes scores = fri..... rG} } by comparing each candidate with the ground truth. To normalize these rewards, GRPO computes their mean and standard deviation. The quality of each response estimated by:
{or, O2...., OG through policy sampling. reward function then
A F; me
(I)
std(r)
where A; denotes the normalized advantage of the i-th response. GRPO optimizes the policy सe to maximize Aç, thereby encouraging beneficial deviation from the initial policy. KL.-divergence regularization term is further incorporated to constrain excessive deviation:
max 11
PuNd on (P)
Σ
Ta(as) Toas (o:)
A.) DEL (To ll (rd)
(2)
where 3 is regularization coefficient controlling the strength of the penalty. Although GRPO has shown strong performance on math reasoning tasks, it struggles to generalise under domain shifts, especially for temporal grounding on unlabelled vidcos. This is due to both the difficulty of label-free optimisation and the high storage and adaptation cost associated with large-scale video data. To overcome these limitations, we introduce a data-efficient cross-domain temporal grounding setting, which consists of source domain model training and lightweight target domain adaptation.
3.3 Source Model Training
VL.Ms focus on coarse-grained understanding but lack fine-grained temporal localization capability. Therefore, post-training is necessary to equip the model with temporal reasoning and grounding

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abilities. To this end, we pursue two main objectives. First, we aim to stimulate explicit reasoning chains within the model, enabling to perform structured temporal inference. Second, we design task-specific reward functions to guide the model towards learning accurate temporal grounding, encouraging outputs that precisely align with relevant video segments.
Format Reward. To facilitate explicit reasoning, following (47. we introduce a format reward that encourages the model to structure its outputs according specified template. Specifically, we require the model to present its thought process within <think> </think> tags, and its final answer within <answer>... </answer> tags. We use regular expression matching to determine whether the model's 's output adheres to the required format as follows:
1, if output matches format,
Rormatt
0,
if output doesn' 't match format.
Accuracy Reward. A core objective for source training is to design well-crafted reward that guides the model to perform reasoning based on reward variations, thereby improving the quality of temporal grounding predictions. To guide the model towards this goal, design accuracy reward based on the overlap between the predicted and ground-truth temporal intervals.
However, event boundaries in videos are inherently ambiguous, and ground-truth annotations across datasets often exhibit labelling biases due to human subjectivity. Such biases can lead to performance degradation when models trained on the source domain applied to the target domain. To mitigate this issue, we first relax the ground-trum truth interval I5 = [-_- -_ by extending both boundaries by a fixed proportion a of the event duration, yielding the relaxed ground-truth 1
jst = sttt pend]
[max(0;
T - a(T end -rr"")) min(1, ,ndd + a((T and
T
star
(4)
where --taat and 7-ndd are normalized timestamps within [0, 1]. and is set to 0.1. Let 1! (p _ denote the predicted temporal interval. We then compute the relaxed temporal Intersection over Union (toOU) between prred and 7 as:
17 good max(0, min sond) - max (ystar, (.saart) max(pend fgnd) min
RdoU
(5)
grod
minn (patart 7 7start)
where iglam and Fend are the relaxed ground-truth boundaries defined in Eq.4 A higher RuoU indicates better alignment between the predicted and relaxed ground-truth segments. Thus, the overall supervised reward function for source domain training is defined as:
R₂ = 0.5 x Riomaat + 0.5 x RIOUU
Supervised training the source domain equips the model with basic spatio-temporal reasoning capability, but still suffers from performance degradation on the target domain due to the domain shift between source and target domains, making target unsupervised adaptation essential.
3.4 Target Adaptation
GRPO requires ground-truth labels compute rewards, which are unavailable in the target domain. Meanwhile, traditional domain adaptation methods typically assume full access to target domain data during training. Nevertheless, this assumption becomes impractical for large-scale video tasks due to substantial storage and computational costs. Thus we consider more realistic setting where only a small number of K unlabelled target videos are available for adaptation, with K << N.
While pretraining on the source domain provides the model with general temporal grounding ability, performance still degrades under domain shift. To bridge this gap, leverage the multiple candidate responses generated during GRPO optimization. For each target sample, the policy model samples G rollout response candidates. Although each individual response may be noisy, their aggregated statistics offer a more stable approximation. We therefore construct pseudo temporal labels by averaging predicted start and end timestamps:
1
aend 1 G E
Astart T
(p)))))))
P
start
10)
(v )())
T
where (1 (3) and (1gnd) denote the predicted timestamps from the j-th sampled response. These pseudo labels act as soft supervision after processed with Eq 4 for test-time adaptation. However,
5

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not all pseudo labels are equally reliable. To evaluate their quality, we estimate prediction uncertainty.
Inspired by Bayesian deep learning. we treat the G rollout responses as approximate samples from predictive distribution, analogous to the Monte Carlo Dropout approach. This allows us to use the standard deviation of the predicted timestamps as proxy for uncertainty:
a ( (((P (())"))))]= )());=)) +
(1))))))))=))
(8)
The uncertainty is then converted into confidence score e via an exponential decay function:
=exp(-nu),
where y is hyperparameter that controls the influence of uncertainty on confidence. Finally, this confidence score is used to weight the contribution of the pseudo labels during reward computation. Specifically, we scale the temporal grounding reward (Buov) by the confidence, while keeping the format reward unweighted:
Rg = 0.5 x Raorma + 0.5 x Raou X c.
(10)
This confidence-awarr reward formulation allows the model to better exploit informative pseudo labels while mitigating the effects of noisy or uncertain predictions, leading to more robust test-time adaptation in the absence of ground-truth labels.
Theoretical Analysis
This section proves that the empirical standard deviation obtained from multiple GRPO rollouts converges the Bayesian predictive standard deviation, thereby quantifying epistemic uncertainty.
Theorem 4.1. Fix an input x. Let (T x) be the rollout policy learned by GRPO and let pr (x) denote the Bayesian predictive distribution of the same model class trained on the source data. Assume (i) rollouts drawn from no are ii.dd: (ii there exists constant M oc such that E,, [72] S M
and Epp 1-- M; (iii) the Kullback-Leibler divergence is finile, & KI(To) To l| 00. Draw G independent rollouts {-(
J ~ TT(. (x) and define
TG
1 G
(
To(x)
1 < TTW) 1 N
As - 50 and GRPO training drives + the estimator (z(2) converges to the Bayesian predictive standard deviation Std [T z), providing a consistent measure of epistemic uncertainty.
Proof. For clarity we give the main steps; full details are in the supplementary material Sec. A.6
Step Consistency under the GRPO policy. Let f4r Ex [r] and o (2) Std,, z). Because the G rollouts {7( are i.i.d. and E [72] < M (Thcorem assumption (ii)), the weak law of large numbers yields
7G
ōo(x) & (x
(G-
(11)
Step 2: Relating the GRPO and Bayesian standard deviations. With = = (L(TTlll")) (assumption (ii)), Pinsker's inequality gives total-variation bound
|| p- IV S 1/c.
(12)
Using 12). the bounded-momen assumption and Var [a] [172]-((([T)) one obtains the variance gap bound:
|o](x) - d3(2)) 4MVE, x3(x)) Var,- [r x]-
(13)
Applying the identity (a bl Vla² - b2 for neg-negetive b, get:
|x~(x) a,(=) S VAMVE.
(14)
Step 3: Convergence to Bayesian predictive standard deviation. GRPO optimisation decreases
over training, so Ve Combining (1I) and the above yields
[ōa(z) o. (x)) < [a(()) dx (x)
(x)
(15)
vanishes by
vanishes

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Table I: Performance comparisons three cross-domain temporal grounding benchmarks. The best
in bold.
Charades ActivityyNe
ActivityNet - STACoS
TACoS Charades
Method
R@0
R.7.7
R@0,3
R@0.
R@05
R@0.7
dull-dataset Unsupervised Doman
Adaplation
CBP [44
27.46
15.37
25.33
21.79
22.38
11.95
SCDM 55
28.02
15.84
22.68
17.45
35.95
25.18
CMIN
34.25
18.63
20.51
15.04
28.06
18.22
CSMGAN 29
36.92
20.04
29.63
18.07
36.45
22.86
2DTAN 61
39.17
21.76
33.72
21.16
25.81
17.37
DRN 57
41.39
24.27
32.07
19.96
36.16
24.52
MMN 48
44.06
24.98
36.94
22.08
33.73
20.04
UDA-TSL
30
49.48
32.15
42.40
29.83
41,39
28.63
Domain
Generalisation
Qwen2.5-7B S
15.18
7.90
7.70
2.77
32.93
15.35
GRPO (Source Training only)
35.61
16.61
20.80
9.87
53.51
28.69
URPA (Source Training only)
36.46
18.78
21.58
10.26
54.40
29.81
Data- Efficient Unsupervised Domain Adaptation
URPA (with 100-shot Target Adaplation)
40.13
20.88
21.82
10.16
54.78
30.08
URPA (with 200-shot Target Adaptation)
42.57
21.25
21.97
10.38
55.54
32.04
TACoS + ActivityNet
Charades +TACoS
ActivityNet
+Charades
Method
R@0.5
R@0.7
R@0.3
R@0.5
&@0.
R@0.7
Full-dataset Unsupervised Domain Adaptation
CBP
18.94
11.93
22.88
19.26
32.82
14.39
SCDM 55
19.65
11.80
18.97
16.82
52.56
34.82
CMIN
22.17
13.72
19.38
15.34
45.03
31.74
CSMGAN
23.88
14.67
25.43
16.12
45.60
32.28
2DTAN 61
24.90
16.38
30.12
19.81
36.34
22.61
DRN 57
24.93
18.52
28.60
16.73
50.47
29.02
MMN 48
28.
20.86
34,09
19.17
50.78
23.17
UDA-TSL 30
33,54
26.16
36.42
25.48
60.26
41.03
Domain
Generalisation
Qwen2.5-7B [53
15.18
7.90
7.70
2.77
32.93
15.35
+ GRPO (Source Training only)
34.82
17.02
12.55
5.13
62.13
36.06
+ URPA (Source Training only)
18.13
13.91
6.27
63.36
37.47
Data- Elficient Unsupervised Domain Adaplation
URPA (with 100-shot Target Adaptation)
38.41
19.80
14.49
7.23
64.35
38.76
URPA (with 200-shot Target Adaptation)
41.83
21.84
16.62
8.25
65.12
39.57
which tends in probability G 0o and 8 Hence
(a()) (x),,
(16)
showing that the rollout standard deviation is a consistent estimator of epistemic uncertainty.
Experiments
To evaluate the effectiveness of the proposed method, conduct experiments three widely used
temporal grounding datasets: TACoS [38] ActivityNet Captions [2]. and Charades-STA [40]
5.1
Experimental Setup
Datasets. We evaluate our method on three benchmark datasets: TACoS, ActivityNet Captions, and Charades-STA, ActivityNet Captions contains approximately 20000 untrimmed YouTube videos annotated with 100000 natural language descriptions. Following the standard split, use 37417 sentence-video pairs for training, and 17031 for testing. TACoS consists 127 cooking-relates videos. We adopt the public split, which includes 10146 and 4083 query-segment pairs for training and testing, respectively. Charades- is built on the Charades dataset, containing 12408 training and 3720 testing moment-query pairs. Codes are given in supplemental materials.
Baselines. evaluate both cross-domain generalisation and adaptation, we follow the standard protocol for temporal grounding under domain shift. In each experiment, one dataset serves as the labelled source domain, while remaining two act as unlabelled target domains, yielding six cross- domain configurations. We compare method under the data-efficient unsupervised adaptation

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Table 2: Ablation study on Charades + Activitynet dataset showing the impact of uncertainty estimation and soft labelling.
Variant
R@033
R@0.5
R@0.7
mloU
Qwen2.5-7B
25.11
15.18
7.90
18.47
Qwen2.5-7B + Our GRPA Target Adaptation (100-shot)
2.67
1.46
0.68
1.96
Qwen2.5-7B Source Training with GRPO
53.91
35.61
16.61
35.81
Qwen2.5-7B + Source Training with our GRPA
55.23
36.46
18.78
37.64
URPA w/o uncertaantt--quaaiiicnn
w/o relaxed HOU (200-shot)
58.58
31.15
20.75
39.86
URPA w/o uncertainty + w/ relaxed IOOU (200-shot)
63.72
41.48
20.47
43.10
URPA (200-shot)
64.61
42.57
21.25
43.65
Table 3: Ablation study of GRPO hyperparameters on the Charades + ActivityNet task: (a) effect of reward scaling factor 7, and (b) effect of rollout count G during adaptation.
7
@@0.3
R@0.5
R@0.7
mloU
(rollouts)
R@0.3
R@0.
R@0.7
mloU
2 59.01
40.04
19.98
40.09
63.41
42.27
21.39
42.55
64.61
42.57
21.25
43.65
5 59.06
40.44
20.13
40.25
16
63.19
41.78
21.73
42.40
10
59.12
40.13
20.88
40.46
32
62.56
41.65
21.54
42.15
25
58.86
39.89
19.97
40.11
64
60.08
40.98
20.86
41.37
(a) Varying 7 with 100-shot unsupervised adaptation. (b) Varying G with 200-shot unsupervised adaptation.
setting, where the source-pretrained model is adapted using only K = 100 or 200 unlabelled target videos with our URPA. This setting is contrasted with the following baselines: (1) Full-set adaptation: The model trained on the labelled source domain and adapted using the entire unlabelled target
domain. We report results for several state-of -ot-----tthhttte art unsupervised adaptation methods, including
CBP [44] SCDM (55), CMIN 662), CSMGAN (2)]. 2DTAN [61), DRN [57]. MMN
(48] and
UDA-TSL 30 (2) Domain Generalisation: We evaluate the base Qwen2.5-7B model 53 (without
any fine-tuning) a GRPO-trained model on the source domain, and a model trained using only the
source training phase of our URPA framework. All three are trained exclusively on the source domain and directly evaluated on the target domain without any access to target data.
Implementation Details. We first fine-tune Qwen2.5-7B on the labelled source domain, and then perform dlta-efficceett adaptation on 100 or 200 randomly selected samples from the unlabelled target domain. In all experiments, the maximum prompt length is set to 4096, the maximum response length to 2048, the number of rollouts to 8, batch size to 16, and we train for 1 epoch. All models are implemented in Py PoTch on 32 NVIDIA V100 GPUs. Codes in supplemental materials.
5.2 Results and Analysis
Experimental Results. Tab. [ shows the performance of our method on the unsupervised cross- domain temporal grounding tasks. We report results using our data-efficient unsupervised adaptation approach with K 100 and K 200 unlabelled target samples. We compare against several baselines, including: (1) traditional UDA methods trained on full labelled source and unlabelled target domain data; (2) zero-shot domain generalisation methods trained only on the source domain and the base Qwen2.5-7B model. Our results demonstrate that data-efficient adaptation with just small number of unlabelled target samples can significantly improve the performance of the source-pretrained model. Compared to full-set UDA methods, our method achieves competitive or even better performance in several settings. In particular, for TACoS > ActivityNet, TACoS -> Charades, and ActivityNet + Charades tasks, our method outperforms state-of-the-art UDA methods in R@0.5. These results highlight the effectiveness and efficiency of our approach in realistic cross- domain scenarios with limited adaptation data. We also compare approach with data-efficient turget-superviedd learning baselines Appendix A.5 further validating its effectiveness.
Ablation Study. In Tab. 2 we present an ablation study on the Charades Charades ActivityyNe cross- tross-domain temporal grounding task assess the impact of different components in our framework. The first row reports the performance of the base model, Qwen2.5-7B [53], directly applied to the temporal grounding task on ActivityNet without any adaptation. The poor performance indicates that the base model lacks inherent temporal localization capabilities. The second row presents the performance of Qwen2.5-7B adapted to the target domain using our method without any source domain pre-training. The subpar results suggest that source domain training provides essential task-specific knowledge

---

Query: He then pulls off the tire and begins unscrewing the back one.
41.6s
116.7s
Ground truth
22.2s
41.9s
Before Adaptation
40.1s
119.5s
After Adaptation
Query: The person bunches up dough and lays out on a board.
45.0s
129.8s
Ground truth
43.0s
51.0s
Before Adaptation
45.0s
122.0s
After Adaptation
Figure Qualitative Analysis on Charades ActivityNet.
necessary for effective adaptation. Rows three and four show the results of models pre-trained the source domain using the original GRPO algorithm and our improved method, respectively, and then directly evaluated the target domain. Both outperform the base model significantly, with method achieving better results than the original GRPO. This demonstrates that supervised training on the source domain aids the model in learning temporal grounding, and our soft accuracy reward approach mitigates biases introduced by manual annotations during pre-training. The last three rows analyse different modules our target domain adaptation strategy, starting from a source- traice model. In the fifth row, pseudo labels are used directly for adaptation without modification in E4. and uncertainty quantification in TO), resulting the lowest performance. In the sixth row, we apply Eq.44) to the pseudo labels, which significantly improves results, indicating that soft label learning helps mitigate the effects of label noise. Finally, the last row introduces our ceertainnt-quanttffen reward weighting mechanism (Eq with soft labelling, yielding further improvements. This confirms the effectiveness of our GRPO-based uncertainty estimation strategy in guiding test-time adaptation.
Parameter Analysis.
We analyse the effect of key hyperparameters on Charades ActivityNet task performance. The reward scaling factor (used (9) controls the sensitivity to uncertainty: a larger y increases the influence of predicted uncertainty on reward weighting. Tab. B(a) reports the results on the 100-shot transfer setting with varying values of 7. We observe that = 10 yields the best performance, but the results are relatively stable across different y values, indicating that our method is robust to this hyperparameter. Tab. ()) investigates the impact of the number of rollouts G in GRPO during 200-shot adaptation. The performance peaks when G and further increasing the number of rollouts does not lead to consistent gains, while incurring higher computational and memory costs. They suggest that our method both effective and efficient under practical settings.
Qualitative Analysis. Fig. iillustrate several qualitative examples from the Charades -> ActivityNet transfer setting. Despite using any labelled data during target adaptation, our method produces significantly more accurate temporal grounding than the baseline. Notably, in these cases where the before adaptation model completely fails to localise the correct segment, our approach is able to capture meaningful behavioural patterns in the target domain and make precise predictions. These results highlight the effectiveness of our method in adapting to unseen domains without supervision.
6 Conclusion
In this paper, we propose URPA, data-efficien method for cross-domine temporal grounding that leverages the rollout mechanism of GRPO to estimate predictive uncertainty and assess the reliability of predictions on unlabelled target-domain videos. This allows effective test-time adaptation using only a small number of target samples. We theoretically demonstrate that the standard deviation across rollouts approximates epistemic uncertainty. Extensive experiments across six cross-domain benchmarks validate the effectiveness of URPA in improving temporal grounding under domain shift.

---

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A Appendix
A.1 Effeet of Shot Number on Test-time Adaptation
13

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As shown in Fig. 4 we evaluate how model performance changes on the Charades+AActivinn adaptation samples increases. The red curve represents our method, while the blue curve corresponds to self-learning baseline that directly uses pseudo labels for adaptation. We use R @0.55 as the evaluation metric. The results show that our method consistently outperforms the baseline by significant margin. Interestingly, as the number adaptation shots increases, the performance of temporal grounding first improves and then drops. This is because the pseudo labels contain noise; while the model initially learns useful temporal grounding signals, it gradually starts to overfit to noisy labels. As the adaptation proceeds, the negative impact of label noise outweighs the benefits of additional supervision, leading to decline in performance.
Perferanct Compertion
setting as the number of test-time
Figure 4: URPA Vs. base-
line performance with varying adaptation shots Charades + ActivityNet.
A.2 More Visualisation Results
To further illustrate the effectiveness of our method, provide qualitative results. The top two examples in Fig. | show successful cases where our method significantly refines the temporal grounding boundaries after adaptation, aligning more closely with the ground truth compared to the pre-adaptation predictions. The third example in Fig. @ishows a failure case where the model's prediction deviates from the ground truth after adaptation. Although the query describes a sequence of swimming actions, the model mistakenly grounds a broader segment that includes irrelevant preparatory scenes. This error may stem from domain-specific visual differences and uncertainty in the pseudo labels, which lead to incorrect supervision during adaptation.
Query: A 3rd boy skateboards through a street.
64.1s
160.2s
Ground truth Refore Adaptation
50.9s
60.9s
60.9s
130.5s
After Adaptation
Query: He goes down the street, flipping the board.
Ground truth 26.9s
97.6s
Before Adaptation
44.0s
52.0s
After Adaptation
29.2s
119.7s
Figure 5: Successful Adaptation Cases on Charades - ActivityNet.
Query: He jumps off the diving board into the swimming pool and starts swimming laps.
Ground
155.2s
180.8s
Before Adaptation
134.5s
159.5s
After Adaptation
139.5s-199.5s
Figure 6: Failure Example on Charades AetivityNet..
14

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Table 4: Comparison of GRPO and URPA under varying adaptation settings target domain across six cross-domain benchmarks.
Bharades-AActivee
ActivityNett-rAACm
TACOS
Charades
Method
R@0
R
R@0.3
R@0.5
R@0.5
R R@0.7
Target Data-Efficien Supervised
Learning
Target Supervised GRPO (200-shot)
42.75
22.42
22.20
11.17
59.38
33.36
Data-Efficient Unsupervised Domain Adaptation
URPA (with 100-shot Target Adaptation)
40.13
20.88
21.82
10.16
54.78
30.08
URPA (with 200-shot Target Adaptation)
42.57
21.25
21.97
10.38
55.54
32.04
TACoS ActivityNet Charades 7 TACoS Activity Setivityyett=CChaaeee Seeett-Charee Chaaaddes
Method
R@0
R@0.7
R@0.3
R@0.5
R@0.5
R@0.7
Target Data Data-Eflicient Supervised Learning
Target Supervised GRPO (200-shot)
44.78
24.36
18.83
9.65
66.77
41.56
Data-Efficient Unsupervised Domain Adaptation
URPA (with 100-shot Target Adaplation)
38.41
19,80
14.49
7.23
64.35
38.76
URPA (with 200-shot Target Adaptation)
41.83
21.84
16.62
8.25
65.12
39.57
A.3
Limitation
While URPA demonstrates strong generalisation in data-efficient cross-domait temporal grounding, several limitations remain. First, our method assumes access to a small number of unlabelled videos from the target domain during deployment. In scenarios where abundant target data is available, the model may overfit to noisy pseudo labels during adaptation. Although our uncertainty quantification via rollout variance helps mitigate this issue, it cannot fully eliminate the risk of overfitting noise.
A.4 Codes Release Information
Our codes can be found in the supplemental material and will be released upon publication.
A.5
More Comparison
In Tab. 4 we further compare the performance our source-domain pre-trained model under different adaptation scenarios the target domain. Specifically, evaluate three setups: (i) fully supervised GRPO with 200 labelled target samples, and (ii) our unsupervised URPA approach using 100-shot and 200-shot unlabelled target videos. Across all six cross-domain settings, URPA with 200-shot adaptation achieves comparable performance to the 200-shot supervised GRPO baseline, highlighting its data- data-efficiency. Notably, even with only 100 unlabelled target samples, URPA consistently narrows the performance gap, demonstrating strong generalisation and adaptation capability in low-resource settings.
A.6 Detailed Theoretical Analysis
Theorem A.I. Fix an input x. Let TT(T x)) be the rollout policy learned by GRPO and let p" xx)
denote the Bayesian predictive distribution the same model class trained on the source data. Assume (i) rollouts drawn from no are ii..d.: (üi there exists constant M oc such that E,, [72] S M
and Epp [ M; (ii) the Kullback- divergence is finite, E KI(To (1') Draw G independent rollouts j ~ no(. (1) and define
1 G
1
FG
Σ
oa(x)=
Σ
((() -T))²
V
As G-+ 3o and GRPO training friivese+i O, the estimator (x(x) converges to the Bayesian predictive standard deviation Stdpp [T x:] and thus provides consistent measure of epistemic uncertainty.
Proof. Let pm = Erg(T] and a. = St a). and define μl, a. analogously for y*.
Consistency under the policy. Because (0) are i.i.d. with E., [7?] M, the weak law of large numbers gives
Fg 4 Pa
Gc(x) & on.
(17)
15

---

Connecting policy and Bayesian moments. variation,
Pinsker's inequality converts the KL bound to total
- p I'v $ V
(18)
and for any measurable g with g(r)) M,
Exo [g(T)] - Ep- (9())] & 2M, 2E.
(19)
Choosing g(T) = T and g(T) = 7² yields
1 Hal 2M V 1 e,
B [r9] Ep [T²] I 2M c.
(20)
Bounding the standard deviation gap.
Using Var[r] = E[T²] - μ², we get:
|o oo. S | Ep. [r]]| + | pll-
(21)
Applying (20) and [1T1:1/44 S M gives:
lo 022 S 4M VE.
(22)
Then by the inequality la- bl S Via 1 for o, 2 0, we obtain:
0x - - $ AMVE.
(23)
Convergence to Bayesian standard deviation. Combining this with (17) and (23):
GRPO optimisation drives + 0, so
+ o.
[ōa(z) o.1 s [o aml + l0, - -o.1 & 0,
which proves that o (x) & Stdp- [T æ]. Hence the rollout standard deviation consistently estimates the Bayesian predictive uncertainty.
A.7 Border Impact
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of work, none which we need highlight.
16

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Question:
Answer: [Yes]
Justification: We claimed the Uncertainty- Rollout Policy Adaptation helps cross- domain temporal grounding, which is validated in the experimental part.
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The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or
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Justification:
We discuss the limitation in Sec
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Justification: We included all these information at the beginning of section 4, including dataset, metrics and other implementation details.
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The answer NA means that the paper does not include experiments.
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Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
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Guidelines:
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Reviews:
[{"review_id": "M0rMNp5UM1", "reviewer": "Reviewer_vGBE", "summary": "This paper proposes URPA, a data-efficient method for unlabelled cross-domain temporal grounding. It adapts a GRPO-trained model using a small number of unlabelled target videos via rollout-based pseudo-labels and confidence-weighted rewards. The method is theoretically grounded and achieves strong results on six benchmark transfer settings.", "questions": "1. Comparative scope: Most baselines are from 2020 or earlier. Could the authors include comparisons with more recent methods from 2024-2025?\n2. Model generality: The current experiments are based solely on Qwen2.5-7B. Can the authors clarify whether URPA generalizes to other VLMs or LLM-based temporal grounding models?\n3. Novelty clarifications: Since URPA builds on existing techniques (GRPO, MC Dropout, pseudo-labeling), can the authors better articulate what constitutes the core methodological novelty beyond this integration?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- Clear problem motivation: The paper addresses a practical and under-explored setting, data-efficient unsupervised cross-domain temporal grounding, where only a small number (100–200) of unlabeled target videos are available.\n- Rich experimental results: Extensive evaluations are conducted on three datasets across six cross-domain settings, including detailed ablation studies and visualizations.\n- Theoretical support: The authors prove that rollout variance converges to Bayesian predictive uncertainty under mild assumptions, offering a principled justification for using rollout standard deviation as confidence.\n\nWeaknesses:\n- Comparative methods are dated: Most baseline methods are from 2020 or earlier, and a few others are recent. This limits the completeness of the comparisons.\n- Limited methodological novelty: The approach combines existing techniques, i.e. GRPO, MC Dropout-style uncertainty estimation [11], and pseudo-labeling with rollout averaging. While the combination is elegant and practical, it lacks fundamentally new learning formulations.\n- No adaptation to strong modern VLMs beyond Qwen2.5, and it is unclear whether it generalizes to other foundation models.", "was_revised": true, "final_justification": "I have carefully read all the reviewers' comments as well as the authors' response. The authors have provided a comprehensive and thoughtful reply, which addresses all of my concerns.", "rebuttal": "We thank the reviewer for the careful reading and insightful comments. Following are the responses regarding your concerns.\n> W1/Q1: Could the authors include comparisons with more recent methods?\n\nOur compared UDA-TSL (2024) is the current state-of-the-art method for cross-domain temporal grounding. To further validate URPA, we compare our URPA  with recent supervised grounding models including HawkEye [1], TimeChat [2], TRACE [3] and VideoChat-T [4] on Charades dataset , all of which are fully trained on the Charades dataset. In contrast, URPA is pretrained on source datasets such as TACoS or ActivityNet and adapted using only 200 unlabelled videos from Charades. Despite this data-efficient and unsupervised setting, URPA achieves comparable or even better performance, highlighting its strong effectiveness in low-resource scenarios.\n\n Model                             | R@0.5 | R@0.7 |\n|-------------|----------------|----------------|\n| Qwen2.5-7B                       | 7.7           | 2.8           |\n| URPA (TACoS→Charades)     | 55.5          | 32.0          |\n| URPA (ActivityNet→Charades) | 65.1          | 39.6          |\n| **Supervised learning approach**                           |  |  |             \n| HawkEye[1]                          | 58.3           | 28.8           |\n| TimeChat[2]                         | 46.7           | 23.7           |\n| TRACE[3]                            | 61.7           | 41.4           |\n| VideoChat-T[4]                      | 67.1           | 43.0           |\n\n[1] Yueqian Wang, Xiaojun Meng, Jianxin Liang, Yuxuan Wang, Qun Liu, and Dongyan Zhao. Hawkeye: Training video-text llms for grounding text in videos, 2024.\n\n[2] Shuhuai Ren, Linli Yao, Shicheng Li, Xu Sun, and Lu Hou. Timechat: A time-sensitive multimodal large language model for long video understanding. In CVPR, pages 14313–14323, 2024.\n\n[3] Yongxin Guo, Jingyu Liu, Mingda Li, Qingbin Liu, Xi Chen, and Xiaoying Tang. Trace: Temporal grounding video llm via causal event modeling. arXiv preprint arXiv:2410.05643, 2024.\n\n[4] Xiangyu Zeng, Kunchang Li, Chenting Wang, Xinhao Li, Tianxiang Jiang, Ziang Yan, Songze Li, Yansong Shi, Zhengrong Yue, Yi Wang, et al. Timesuite: Improving mllms for long video understanding via grounded tuning. arXiv preprint arXiv:2410.19702, 2024.\n\n> W2/Q3: What constitutes the core methodological novelty beyond integration?\n\nOur core methodological novelty lies in reinterpreting the rollout variance from GRPO as a principled uncertainty signal for test-time adaptation. Unlike MC Dropout, which injects randomness at the neuron level, our approach leverages policy-level stochasticity to estimate epistemic uncertainty without requiring architectural changes. This insight enables the first unsupervised reinforcement learning–based temporal grounding framework, where uncertainty guides adaptation without any temporal annotations. Furthermore, we formalise and address a new and practical setting of data-efficient unsupervised cross-domain temporal grounding, which remains underexplored in prior work.\n\n> W3/Q2: Whether URPA can generalize to other VLM-based temporal grounding models?\n\nTo demonstrate the generality of URPA, we conducted additional experiments using two different temporal grounding backbones: LLaVA-ST and Qwen2.5-3B. As shown in the table below, applying URPA to LLaVA-ST yields substantial performance improvements, indicating that our adaptation strategy is effective across different architectures. Similarly, URPA also improves the performance of Qwen2.5-3B, though the overall results are lower than those of the 7B model due to its more limited reasoning capacity. These results confirm that URPA generalizes well across various vision-language models, regardless of model size or backbone design.\n\n| Model               | Charades→ActivityNet |      ActivityNet→TACoS |       \n|---------------------|----------------------|-------|\n|                     | R@0.5                 | R@0.5              |\n| Qwen2.5-7B          | 15.2                |  7.7               |\n| URPA+Qwen2.5-7B                | 42.6                |  22.0              |\n| Qwen2.5-3B          |      9.6                |    4.5                | \n| URPA+Qwen2.5-3B     | 33.9               | 14.3              |\n| LLaVA-ST-7B         | 13.6                 | 6.5               |\n| URPA+LLaVA-ST-7B    | 36.2                |  16.9              |"}, {"review_id": "HLejCl5YnH", "reviewer": "Reviewer_noQ9", "summary": "This paper proposes a method for cross-domain temporal grounding, addressing the challenges of domain shifts and the lack of annotations in unlabelled target domains. By leveraging Group Relative Policy Optimization rollouts and uncertainty estimation, the method enables efficient adaptation using only a small number of target domain videos. The authors introduce a pseudo-labelling mechanism, where predictions are averaged across multiple rollouts, and confidence scores are computed based on variance. Experiments across six cross-domain settings on three benchmark datasets demonstrate the effectiveness of URPA, achieving competitive or superior performance compared to full-dataset adaptation methods while maintaining resource efficiency.", "questions": "1. How does the choice of the exponential decay function in Eq. (9) affect the confidence score? Have alternative formulations for scaling uncertainty been tested?\n\n2. In scenarios where the target domain exhibits significant visual or semantic differences from the source domain, how robust is the method? Does the variance metric still reliably estimate epistemic uncertainty?\n\n3. The results show that performance peaks at G = 8 rollouts. Could you provide more insight into why increasing rollouts beyond this threshold does not yield better results? Is it due to computational inefficiency, or does it introduce noise?\n\n4. Although the method is unsupervised in the target domain, how does it perform when compared to fully supervised target training (beyond the 200-shot scenario)?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "# Strengths:\n\n1. The introduction of URPA as a data-efficient unsupervised domain adaptation method is a novel contribution to the temporal grounding task, particularly for real-world applications where computational and storage resources are limited.\n\n2. A rigorous theoretical analysis connects GRPO rollouts to Bayesian predictive variance, providing strong justification for the use of rollout variance as a measure of epistemic uncertainty.\n\n3. The method performs well with only a small number of unlabelled target videos, which is significantly more practical than full-dataset adaptation methods.\n\n4. Results are provided on six cross-domain settings across three datasets, demonstrating the generalizability and robustness of URPA. The ablation studies clearly highlight the contributions of uncertainty quantification and soft labeling.\n\n# Weaknesses:\n\n1. While the paper demonstrates the importance of URPA components, it does not compare GRPO rollouts with other reinforcement learning approaches or variants. This could provide further insight into the unique strengths of GRPO in the temporal grounding context.\n\n2. Although uncertainty quantification is used to mitigate noisy pseudo-labels, the method still assumes that pseudo-labels with low variance are reliable. This assumption may not hold in cases where low variance predictions are consistently incorrect due to systematic bias in the source model.\n\n3. The experiments suggest diminishing returns when increasing the number of rollouts (G>8). However, the computational trade-offs of using fewer rollouts versus the impact on uncertainty estimation are not fully explored.", "was_revised": true, "final_justification": "Thank you for the detailed response from the author, which has answered most of my questions. I have decided to maintain my rating.", "rebuttal": "We thank the reviewer for the positive evaluation of our work and for acknowledging the value and insight of the proposed method. \n\n> W1: Compare GRPO rollouts with other reinforcement learning approaches.\n\nIn our setting, such a comparison is not directly applicable. URPA is designed for **unsupervised adaptation in the target domain**, where no target temporal annotations are available. In contrast, most RL methods like PPO, A2C, or SAC operate in **supervised or reward-accessible settings** and generate rollouts across different samples, not multiple diverse predictions for the **same input**, which is essential for estimating epistemic uncertainty in our framework.\n\nWe compare URPA with existing reinforcement learning-based temporal grounding methods in Table below. Prior approaches such as BAR [1], TripNet [2], MBAN [3], URL [4], and the original GRPO are all trained in a fully supervised manner using the Charades dataset. In contrast, URPA is designed for unsupervised cross-domain adaptation: it is pretrained on source datasets such as TACoS or ActivityNet, and adapted to the target domain using only 200 unlabelled videos without timestamp annotations.\n\nDespite operating without ground-truth supervision in the target domain, URPA achieves comparable or even superior performance on the Charades dataset in terms of R@0.5 and R@0.7. These results demonstrate the effectiveness of our GRPO-based uncertainty-guided adaptation strategy and its strong generalisation ability under low-resource conditions.\n\n| Model                             | R@0.5 | R@0.7 |\n|----------------------------------|----------------|----------------|\n| URPA (TACoS→Charades)     | 55.5          | 32.0          |\n| URPA (ActivityNet→Charades) | 65.1          | 39.6          |\n| **RL-based supervised learning approach**  |                             \n| Bar[1]                          | 46.5           | 22.7           |\n| TripNet[2]                         | 38.3           | 16.1           |\n| MBAN[3]                            | 56.3           | 32.3           |\n| URL[4]                      | 55.7           | -  \n| Target Full Supervised GRPO  | 72.5 | 47.9 |\n\n[1] Wu, Jie, et al. \"Reinforcement learning for weakly supervised temporal grounding of natural language in untrimmed videos.\" Proceedings of the 28th ACM International Conference on Multimedia. 2020.\n\n[2] M. Hahn, A. Kadav, J. M. Rehg, and H. P. Graf, “Tripping through time: Efficient localization of activities in videos,” in BMVC, 2020.\n\n[3] X. Sun, H. Wang, and B. He, “Maban: Multi-agent boundary-aware network for natural language moment retrieval,” IEEE TIP, vol. 30, 2021.\n\n[4] Y. Zeng, D. Cao, S. Lu, H. Zhang, J. Xu, and Z. Qin, “Moment is important: Language-based video moment retrieval via adversarial learning,” ACM TMCCA, vol. 18, 2022.\n\n> Q1: How does the choice of the exponential decay function in Eq. (9) affect the confidence score? Have alternative formulations for scaling uncertainty been tested?\n\nThe exponential decay function in Eq. (9) is designed to more aggressively suppress high-uncertainty pseudo-labels, offering sharper penalisation than linear or reciprocal alternatives and leading to more stable training. We experimented with softmax-based, sigmoid-based, and MLP-based alternatives. However, softmax requires batch-relative normalization and sigmoid compresses the variance range too aggressively, both leading to unstable training. The MLP variant adds extra computational cost while yielding inferior performance, making exponential decay a more effective and efficient choice.\n\n>  W2/Q2: Does the variance metric still reliably estimate epistemic uncertainty under large domain gap?\n\nIn scenarios with significant domain shift, we acknowledge that low variance does not always guarantee correctness due to potential systematic bias from the source model. However, our method mitigates this risk in two ways. First, GRPO's structured rollouts capture epistemic uncertainty more robustly than neuron-level dropout by exposing variability in the model's temporal predictions. Second, our adaptation framework avoids relying solely on absolute variance values. Instead, we apply a decaying weighting function to softly down-weight uncertain pseudo-labels, preventing overfitting to potentially biased predictions. Empirically, as shown in the Charades→ActivityNet setting (with large visual-semantic shift), our method still achieves substantial improvements over the source training model (R@0.5: 36.5 → 42.6), confirming the robustness of the uncertainty signal.\n\n> W3/Q3: More insight into why increasing rollouts beyond 8 does not yield better results?\n\nWe observe that increasing the number of rollouts initially improves performance by stabilizing pseudo-label estimation and better capturing uncertainty (e.g., from G=4 to G=8). However, as G continues to grow, performance degrades due to limitations of the variance-based confidence metric. When most predictions are concentrated, the impact of a few divergent samples becomes diluted, causing the variance to shrink and underestimating uncertainty. This leads the model to overestimate pseudo-label reliability, weakening its ability to distinguish between reliable and unreliable supervision and ultimately hindering adaptation.\n\n> Q4: Comparison with full-set fully supervised target training results.\n\nAlthough our method is few-shot and unsupervised in the target domain, it achieves strong performance compared to fully supervised target-domain training. We further compare URPA with recent state-of-the-art supervised grounding models including HawkEye [1], TimeChat [2], TRACE [3], VideoChat-T [4] and target fully supervised training GRPO, all of which are fully trained on the target Charades dataset. In contrast, URPA is pretrained on source datasets such as TACoS or ActivityNet and adapted using only 200 unlabelled Charades videos. Despite this data-efficient and unsupervised setting, URPA achieves comparable or even superior performance, highlighting its practical value and effectiveness in low-resource cross-domain scenarios.\n\n| Model                             | R@0.5 | R@0.7 |\n|----------------------------------|----------------|----------------|\n| URPA (TACoS→Charades)     | 55.5          | 32.0          |\n| URPA (ActivityNet→Charades) | 65.1          | 39.6          |\n| **Supervised learning approach**  |                             \n| HawkEye[1]                          | 58.3           | 28.8           |\n| TimeChat[2]                         | 46.7           | 23.7           |\n| TRACE[3]                            | 61.7           | 41.4           |\n| VideoChat-T[4]                      | 67.1           | 43.0  \n| Target Full Supervised GRPO                      | 72.5           | 47.9  \n\n[1] Yueqian Wang, Xiaojun Meng, Jianxin Liang, Yuxuan Wang, Qun Liu, and Dongyan Zhao. Hawkeye: Training video-text llms for grounding text in videos, 2024.\n\n[2] Shuhuai Ren, Linli Yao, Shicheng Li, Xu Sun, and Lu Hou. Timechat: A time-sensitive multimodal large language model for long video understanding. In CVPR, pages 14313–14323, 2024.\n\n[3] Yongxin Guo, Jingyu Liu, Mingda Li, Qingbin Liu, Xi Chen, and Xiaoying Tang. Trace: Temporal grounding video llm via causal event modeling. arXiv preprint arXiv:2410.05643, 2024.\n\n[4] Xiangyu Zeng, Kunchang Li, Chenting Wang, Xinhao Li, Tianxiang Jiang, Ziang Yan, Songze Li, Yansong Shi, Zhengrong Yue, Yi Wang, et al. Timesuite: Improving mllms for long video understanding via grounded tuning. arXiv preprint arXiv:2410.19702, 2024."}, {"review_id": "wbGkz3oMKx", "reviewer": "Reviewer_VEkt", "summary": "This paper proposes a novel approach for Unlabelled Cross-domain Temporal Grounding (UCTG), termed Uncertainty-quantified Rollout Policy Adaptation (URPA). Specifically: (1) The two tailored reward functions, combined with the introduction of GRPO, ensure robust grounding capability in the source domain. (2) A pseudo-labeling mechanism is designed based on GRPO, where label quality is assessed via uncertainty quantification, enabling effective target adaptation. Extensive experiments on multiple benchmarks demonstrate the superiority of URPA over existing methods.", "questions": "Although the method introduced in this paper is reasonable and has been proven effective through experiments, due to doubts about the motivation and insufficient experiments, my current rating is 4.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The effectiveness of URPA in achieving cross-domain temporal grounding with limited samples is commendable. The judicious application of GRPO to this task provides an economical yet impactful research direction for the field.\n2. The quality assessment design for pseudo-labels is well-justified. Through rigorous theoretical analysis and comprehensive experiments, the authors convincingly demonstrate the validity of their evaluation strategy.\n\nWeaknesses:\n1. Motivation: The necessity of efficiency for UCTG is inadequately justified in lines 44-46. Additionally, the authors lack task-specific analysis - what are the key challenges constraining UCTG development?\n2. Table 1 Results: URPA achieves better performance than full-dataset methods in certain cases (e.g., TACoS→Charades), yet shows significant gaps in most settings. What causes this phenomenon?\n3. Performance at Scale: While URPA's low sample dependency is impressive, how does its performance evolve with increased samples (e.g., 1000-shot, 10000-shot)? A superior method should demonstrate balanced effectiveness and efficiency.\n4. There appears to be an error in the expression/formulation of Equation 3.", "was_revised": true, "final_justification": "This rebuttal addresses my main concerns.", "rebuttal": "We thank the reviewer for the positive evaluation of our work and our detailed feedback to the concerns are as follows.\n\n> W1(1): The necessity of efficiency for UCTG is inadequately justified.\n\nThe efficiency of UCTG is particularly critical not only because collecting dense temporal annotations is costly, but also due to the inherent storage and computational demands of video data. Compared to images, videos require significantly more space and time to annotate and store. A single video often exceeds the size of an entire image dataset, making full-set target adaptation impractical in online or real-time settings. Thus, efficient, data-lite adaptation becomes a necessity to enable scalable deployment of temporal grounding models in real-world applications such as surveillance or instructional content, where labelled data is limited and fast domain adaptation is required.\n\n> W1(2) What are the task-specific key challenges constraining UCTG development?\n\nThe development of UCTG faces three task-specific challenges. First, the lack of ground-truth supervision in the target domain makes it difficult for the model to correct domain-specific errors under domain shift. Second, temporal annotations (i.e., start and end timestamps) are often manually labeled and inherently subjective, introducing annotator bias. Finally, the duration and temporal location of events vary significantly across datasets. Without a deeper understanding of the causal or semantic structure of events, models tend to rely on learning dataset-specific temporal occurrence patterns. As a result, their predictions often fail to transfer across domains with different event distributions, leading to substantial performance drops.\n\nTo address these challenges, first, URPA introduces a confidence-based pseudo-labeling strategy to mitigate the absence of target supervision. Second, It uses a relaxed tIoU reward to reduce sensitivity to annotation bias. Thirdly, by leveraging GRPO-style reinforcement learning, the model learns to reason about event structure and temporal logic, rather than memorizing dataset-specific temporal distributions, enabling better generalization across domains.\n\n>  W2: Explain the performance gaps between URPA and  full-dataset methods methods in most settings, despite URPA outperforming them in a few cases? \n\nThe gap is explained by two factors. First, the richness of source-domain supervision provides different levels of grounding priors, which makes adaptation easier in some transfers such as TACoS→Charades. Second, URPA relies on GRPO-based adaptation with uncertainty filtering, which uses limited target samples more effectively but cannot fully exploit the entire target distribution. As a result, URPA can sometimes surpass full-set methods when source priors are strong, but shows gaps in transfers with larger domain differences.\n\n> W3: How does its performance evolve with increased samples?\n\nWe include an experiment in Appendix A.1 (Fig. 4) to evaluate how performance evolves with increasing adaptation samples. URPA consistently outperforms the baseline under all shot settings, confirming the effectiveness of our uncertainty-guided strategy. Interestingly, performance peaks around 300 samples and then declines. This is due to the error accumulation of noisy pseudo labels. Although our method mitigates their effect via uncertainty filtering, fully unsupervised adaptation still suffers when label quality degrades at scale. This highlights a key challenge: more unlabelled data does not necessarily yield better performance unless pseudo-label reliability is also improved. We discuss this in our limitations and plan to address it in future work.\n\n> W4: An error in Equation 3.\n\nThank you for pointing this out. We will correct it to reflect the intended behaviour: a reward of 1 when the output matches the required format, and 0 otherwise."}, {"review_id": "gjy726yoci", "reviewer": "Reviewer_tuX4", "summary": "This paper presents URPA, a method for temporally localizing natural language queries in videos from diverse domains. URPA is first trained on a source domain with temporal annotations, using two losses: one to make the model answer following a specific format, and another to improve temporal grounding quality using extended ground truth intervals. To adapt to a target domain without annotations, URPA selects a small set of target videos, samples timestamps from the model's predictive distribution, and uses the mean as a pseudo-label. These pseudo-labels are weighted by a confidence, which is estimated from the standard deviation of the sampled timestamps. URPA achieves good results on three cross-domain temporal grounding benchmarks, outperforming off-the-shelf and source-only models, and even some UDA methods that use the full target set.", "questions": "1. Have the authors evaluated URPA with other LMMs than Qwen2.5-7B to understand whether the method's effectiveness generalizes across different models?\n2. Do other unsupervised domain adaptation baselines also benefit from training on a limited subset of the target dataset (possibly the same subset used by URPA) compared to using the full target set?\n3. How does URPA perform on the source domain after adapting to the target domain? \n4. According to Table 2, extending the start and end timestamps of the ground truth boundaries leads to a significant performance improvement. How was the value of $\\alpha = 0.1$ selected?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. Despite its simplicity, URPA is a principled approach that boosts temporal grounding performance on the target domain by leveraging pseudo-labels while accounting for their uncertainty.\n2. Unlike other test-time adaptation methods that adapt to each test input, URPA performs a one-time adaptation before inference using only a small subset of videos, which is a more practical scenario.\n3. The paper is generally well-written (with some typos, though), is easy to follow, clearly motivates the problem, and supports its claims with experiments.\n\nWeaknesses\n\n1. Although URPA, implemented on top of Qwen2.5-7B, consistently outperforms its ablated versions on Charades → Activitynet, it is unclear whether these improvements would generalize to other LMMs.\n2. Figure 4 shows that URPA's performance on the target domain starts to decrease after using 300 target samples. It would have been interesting to see how unsupervised domain adaptation baselines (e.g., UDA-TSL) perform when limited to the same randomly sampled target data as URPA.\n3. It would be interesting to evaluate the model on the source dataset after adaptation, to understand whether it retains its ability to perform temporal grounding in the source domain.", "was_revised": true, "final_justification": "The authors addressed my concerns about the generalizability of URPA to other large multimodal models by implementing the method on LLaVA-ST and Qwen2.5-3B, in addition to Qwen2.5-7B, and demonstrating improved performance across models. They also clarified the differences between URPA and the best-performing cross-domain temporal grounding method, UDA-TSL, and explained why UDA-TSL does not show the same performance trend as URPA when using a limited number of target domain samples. I agree that the performance gains on the target domain justify the slight performance drop on the source domain after adaptation. Given these clarifications, I am increasing my score to Accept.", "rebuttal": "We are grateful for the positive response, as well as the valuable suggestions. Following are the responses regarding your concerns.\n\n> W1/Q1: Whether URPA can generalize to other VLM-based temporal grounding models?\n\nTo demonstrate the generality of URPA, we conducted additional experiments using two different temporal grounding backbones: LLaVA-ST and Qwen2.5-3B. As shown in the table below, applying URPA to LLaVA-ST yields substantial performance improvements, indicating that our adaptation strategy is effective across different architectures. Similarly, URPA also improves the performance of Qwen2.5-3B, though the overall results are lower than those of the 7B model due to its more limited reasoning capacity. These results confirm that URPA generalizes well across various vision-language models, regardless of model size or backbone design.\n| Model               | Charades→ActivityNet |      ActivityNet→TACoS |       \n|---------------------|----------------------|-------|\n|                     | R@0.5                 | R@0.5              |\n| Qwen2.5-7B          | 15.2                |  7.7               |\n| URPA+Qwen2.5-7B                | 42.6                |  22.0              |\n| Qwen2.5-3B          |      9.6                |    4.5                | \n| URPA+Qwen2.5-3B     | 33.9               | 14.3              |\n| LLaVA-ST-7B         | 13.6                 | 6.5               |\n| URPA+LLaVA-ST-7B    | 36.2                |  16.9              |\n\n> W2/Q2: Do other unsupervised domain adaptation baselines also benefit from training on a limited subset of the target dataset?\n\nTo address the reviewer’s concern, we compare URPA with UDA-TSL, the current SOTA method for cross-domain temporal grounding, using the same limited number of unlabelled target samples. We evaluate three settings:\n\n- UDA-TSL (NoUDA): trained only on the source domain;\n- UDA-TSL (200-shot): adapted using 200 unlabelled target videos;\n- UDA-TSL (2000-shot): adapted using 2000 target videos;\n\nAs the original code is unavailable, we reimplemented UDA-TSL following the paper. Results show that UDA-TSL achieves notable improvements only with ≥2000 samples. In contrast, its 200-shot variant barely outperforms the source-only baseline due to insufficient target domain coverage.\n\nURPA, designed for data-efficient adaptation, yields substantial gains with just 200 samples and outperforms UDA-TSL in both transfer directions. This highlights URPA’s robustness and practicality for real-world low-resource scenarios where collecting large-scale unlabelled target data is infeasible. These results confirm that URPA is more suitable for few-shot unsupervised cross-domain adaptation, offering better performance without relying on extensive target data.\n\n| Method             | Charades→ActivityNet(R@0.5 / R@0.7) | TACoS→Charades(R@0.5 / R@0.7) |\n|--------------------|------------------------------|--------------------------|\n| UDA-TSL (NoUDA)    | 35.83 / 19.52                 | 27.25 / 16.42            |\n| UDA-TSL (200-shot) | 36.02 / 19.68                 | 27.44 / 16.71            |\n| UDA-TSL (2000-shot)| 39.15 / 21.83                 | 30.76 / 18.87            |\n| URPA (source-only) | 36.46 / 18.78                 | 54.40 / 29.81            |\n| URPA (200-shot)    | 42.57 / 21.25             | 55.54 / 32.04        |\n\n> W3/Q3: How does URPA perform on the source domain after adapting to the target domain?\n\nWe use mIoU to evaluate source-domain performance before and after adaptation with 200-shot target data. As shown in the table below, URPA achieves better alignment with the target domain after adaptation, while source-domain performance decreases slightly as source-specific knowledge is partially replaced by target-domain features. This trade-off is typical in domain adaptation, yet URPA continues to maintain strong temporal grounding ability on the source domain.\n| Model\\Task                        | Charades→ActivityNet (mIoU) | ActivityNet→Charades (mIoU)|\n|-----------------------------|----------------------|-----------------------|\n| URPA (before adaptation) on source    | 56.3                 | 38.6                  |\n| URPA (after adaptation)  on source   | 54.5                 | 37.2                  |\n\n> Q4: How was the value of α  selected?\n\nWe determine α through cross-validation. With small values, the model retains accurate temporal grounding while modestly correcting annotator subjectivity. Larger values further reduce annotation bias but simultaneously lower label precision. We observe that α = 0.1 provides the best balance between mitigating annotation bias and preserving temporal grounding accuracy."}, {"review_id": "kYh7CRaHkB", "reviewer": "Reviewer_Wxx8", "summary": "Uncertainty-quantified Rollout Policy Adaptation for Unlabelled Cross-domain Temporal Grounding introduces URPA, a data-efficient and annotation-free approach for adapting video temporal grounding models across domains. The method begins with training on a labeled source domain using GRPO, a reinforcement learning framework, and then adapts the model to an unlabeled target domain using only 100–200 videos. By averaging multiple rollout predictions, URPA generates pseudo labels and estimates their uncertainty through variance. These uncertainty scores are used to weight the adaptation reward, enabling the model to focus on more reliable supervision. Experiments across six cross-domain benchmarks demonstrate that URPA enhances generalization and supports efficient, real-time adaptation without requiring ground-truth annotations.", "questions": "How can models handle ambiguous or vague language descriptions?", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. URPA achieves high data efficiency, requiring only 100–200 unlabelled target-domain videos for adaptation, making it practical for real-world scenarios where annotated data is limited or costly to obtain.\n2. Theoretical soundness is well-established, with a rigorous analysis demonstrating that rollout variance approximates Bayesian predictive variance, thereby validating the use of uncertainty for reward weighting during adaptation.\n3. The method enables fully unsupervised adaptation, eliminating the need for ground-truth labels in the target domain by leveraging pseudo labels combined with uncertainty estimation for reliable self-supervision.\n4. The framework supports lightweight, test-time adaptation without the need for full retraining, making it highly suitable for real-time and on-device applications in resource-constrained environments.\nWeaknesses:\n1. The adaptation performance does not consistently improve with more unlabelled target samples. As shown in the paper, excessive adaptation can lead to overfitting on noisy pseudo labels, which limits the method’s scalability in large-scale unsupervised settings.\n2. The method focuses on temporal grounding optimization but does not explicitly address semantic misalignment between language queries and video content, which may limit its effectiveness in scenarios with ambiguous language or complex video semantics.", "was_revised": true, "final_justification": "Thank you to the authors for addressing my concerns in the rebuttal. The clarifications on the adaptation performance with unlabelled target samples and the use of uncertainty-aware pseudo-labeling are satisfactory, especially the insight that URPA excels in data-efficient scenarios. The explanation on how temporal IoU indirectly aligns language queries with video content, as well as the strategies to handle ambiguous queries through GRPO rollouts and uncertainty-based weighting, are well-justified. Based on these responses, I am satisfied with the revisions and will maintain my original score.", "rebuttal": "We thank the reviewer for valuable feedback and for appreciating the idea of our method. Following are the responses regarding your concerns.\n\n> W1: The adaptation performance does not consistently improve with more unlabelled target samples.\n\nAs demonstrated in Figure 4 in the manuscript, our method employs uncertainty-aware pseudo-labels to effectively reduce the influence of noisy supervision in low-resource scenarios, outperforming naive self-learning baselines. When adapting with 100–200 unlabelled target videos, URPA achieves strong performance by filtering unreliable predictions and focusing on confident supervision. However, as the number of target samples increases, the absolute number of noisy pseudo-labels also grows, their accumulation can lead to overfitting on incorrect guidance, thereby degrading performance. This highlights a key insight: URPA is best suited for data-efficient adaptation, where the pseudo-label quality can be more reliably controlled via uncertainty estimation. Designing more robust noise-aware mechanisms for scaling to larger datasets remains an important direction for future research.\n\n> W2: How to address semantic misalignment between language queries and video content?\n\nWhile our method does not introduce an explicit semantic alignment loss, the use of temporal IoU as the task reward indirectly encourages the model to align the query semantics with relevant video segments. Accurate grounding requires the model to match language cues with visual content, and the reward function provides learning signals that reinforce such alignment during adaptation.\n\n> Q1: How can models handle ambiguous or vague language descriptions?\n\nAmbiguous or vague queries pose a major challenge for temporal grounding, as they lack explicit visual cues. Our method addresses this in two ways. First, by leveraging multiple GRPO rollouts, the model is encouraged to explore diverse temporal hypotheses rather than committing to a single point estimate. This helps uncover plausible alignments even under semantic uncertainty. Second, our use of a variance-based uncertainty metric allows the model to down-weight pseudo-labels generated from ambiguous queries, reducing their influence during adaptation. Together, these strategies help the model remain robust in the presence of vague or underspecified language."}]

Author rebuttal:

Meta-review: The paper addresses the task of Cross-domain Temporal Grounding (ie localising natural language queries in videos), where key challenges are domain shifts, and also a lack of annotations in the unlabelled target domain. Concretely, the method is first trained on a source domain with temporal annotation. Second, to adapt to a unlabelled target domain, the authors select a small set of target videos, sample videos from the model's output and use the mean as the pseudo-label, using GRPO rollouts.

The reviewers commented positively about the strong results achieved by the method, that the method is both principled and simple, and that unlike other test-time adaptation methods which must be run for each test-input, this only needs to be done once on a small subset of videos. The method is therefore more suited for practical applications.

The concerns that reviewers raised were addressed well during the rebuttal phase. As a result, the final decision is acceptance. Please update the camera ready according to the reviewers' comments and the rebuttal.
Decision: Accept (poster)