Verifier-free Test-Time Sampling for Vision-Language-Action Models
Authors: Suhyeok Jang
Dongyoung Kim
Changyeon Kim
Youngsuk Kim
Jinwoo Shin
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-Language-Action Models
Test-Time Scaling
Robotic Manipulation
Primary area: applications to robotics, autonomy, planning
TL;DR: We propose MG-Select, a masking distribution–guided test-time scaling method that enhances precision of Vision-Language-Action models in robotic manipulation.

Abstract: Vision-Language-Action models (VLAs) have demonstrated remarkable performance in robot control. However, they remain fundamentally limited in tasks that require high precision due to their single-inference paradigm. While test-time scaling approaches using external verifiers have shown promise, they require additional training and fail to generalize to unseen conditions. We propose Masking
Distribution Guided Selection (MG-Select), a novel test-time scaling framework for VLAs that leverages the model's internal properties without requiring additional training or external modules. Our approach utilizes KL divergence from a reference action token distribution as a confidence metric for selecting optimal action from multiple candidates. We introduce a reference distribution generated by the same VLA but with randomly masked states and language conditions as inputs, providing action uncertainty while remaining aligned with the target task distribution. Additionally, we propose a joint training strategy that enables the model to learn both conditional and unconditional distributions by applying dropout to state and language conditions, thereby further improving the quality of the reference distribution. Our experiments demonstrate that MG-Select provides a reliable reference for action selection through task-relevant condition masking
and consistently improves base models across diverse simulation and real-world benchmarks.

Published as a conference paper at ICLR 2026
VERIFIER-FREE TEST-TIME SAMPLING FOR VISION-LANGUAGE-ACTION MODELS
Suhyeok Jang'
Dongyoung Kim
Changyeon Kim SRIWRLD
Youngsuk Kim?
Jinwoo Shin
KAIST "Seoul National University
ABSTRACT
LaannnLannngge--a models (VLAs) have demonstrated remarkable perfor- mance robot control. However, they remain fundamentally limited tasks that require high precision due to their single-inference paradigm. While test-time scaling approaches using external verifiers have shown promise, they require addi- tional training and fail to generalize to unseen conditions. We propose Masking Distribution Guided Selection (MG-Select), a novel test-time scaling framework for VLAs that leverages the model's internal properties without requiring additional training or external modules. Our approach utilizes KL divergence from a reference action token distribution from multiple candidates. We introduce a reference distribution generated by the same VLA but with randomly masked states and language conditions as inputs, providing action uncertainty while remaining aligned with the target task distribu- tion. Additionally, we propose joint training strategy that enables the model learn both conditional and unconditional distributions by applying dropout to state and language conditions, thereby further improving the quality the reference distribution. Our experiments demonstrate that MG-Select provides reliable refer- ence for action selection through task-relevant condition masking and consistently improves base models across diverse simulation and real-world benchmarks.
confidence metric for selecting the optimal action
INTRODUCTION
Vision-Language-Are models (VLAs; Zitkovich et al. 2023; Kim et al. 2024; Black et al. 2025;
Bjorek et al. 2025), trained on large-scale robotic datasets (O'Neill al., 2024; Bu et al., 2025),
have demonstrated remarkable performance in robot control. Among these, autoregressive VLAs represent one of the predominant VLAs (Driess et al., 2023; Kim et al., 2024; Pertsch et al., 2025), leveraging the same autoregressive objective used in training vision and foundation models without requiring architectural modifications, yet achieving comparable performance to more sophisticated architectures. Despite their success, VLAs remain fundamentally limited in tasks that demand high precision; even after extensive pre-training, they often fail on fine-grained manipulation tasks such grasping object placement (Nakamoto et al., 2024; Kwok et al., 2025; Gu al., 2025; Yang et al., 2025). This precision gap is particularly problematic for real-world robotic applications where millimeter-level accuracy can determine task success or failure.
Previous work (Nakamoto et al., 2024; Kwok et al., 2025) shows that while VLAs can achieve high precision with adequate training, their greedy decoding (always choosing the piohest--robability action) becomes bottleneck. To address this limitation, inspired by the substantial gains observed in LLM reasoning with Test Time Scaling (TTS) (Wang et al., 2023; Wan et al., 2025: Kang et al., 2025), they use repeated sampling paired with an external verifier, i.e., value function trained on robotic data. However, these approaches have significant drawbacks: First, they require additional training to obtain verifiers with reinforcement learning objectives before inference, which adds substantial computational overhead and complexity to the deployment pipeline. Second, these extermal verifiers fail to generalize to unseen input conditions (Nakamoto al., 2024), such as novel task prompts or objects, and their reward modeling is tailored to specific datasets, severely limiting their broader applicability (Kwok et al., 2025).
Our approach. To tackle this problem, our research goal is to develop a test-time scaling framework for VLAS that leverages the model's internal properties without requiring additional training or

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Published as a conference paper at ICLR 2026
Predicted Distribution
Sampling
Actions
CondRooeeMasking
CA
Best-af-N
Autoregressive VLA
Condet com-lankinggg
C
"Pick the Ketchup from the Cabinet"
xel- (.,00.Loe. sol: oy U. 0.0.
Condition-Masknng Distribution
Candtisr-Haserg
CN
Robot Staee
Figure I: Overview of MG-Select. (1) Autoregressive VLA Te samples action tokens in parallel from the predicted distribution, while simultaneously computing token-wise KI. divergence from the condition- condition-masking distribution to the predicted distribution. (2) Best-of-N selection is then performed using action confidence score Ca obtained by aggregating these token-wise scores.
external modules. Inspired by verifier- free approaches for TTS (Zheng et al., 2024), we begin with the most straightforward approach: selecting the action with the highest likelihood from multiple sampled actions. We observe that this simple technique alone can improve VLA performance by producing more precise actions in some cases (see Table (a)). However, this approach is not effective in general, as VLAs fine-tuned target tasks for next action token prediction often memorize expert trajectories, causing the probability distribution over action tokens to become overly concentrated, which leads multiple sampling converging to the same result.
These insights motivate us to propose Masking Distribution Guided Selection (MG-Select), a novel TTS framework that leverages the KL. divergence from reference action token distribution confidence metric for selecting the optimal action from multiple candidates. Inspired by recent advances in LLM literature that use self-certainty measures (Kang et al., 2025), we adapt this principle to the VLA setting. Specifically, we introduce a reference distribution generated by the same VLA but with randomly masked states and language conditions inputs. This design ensures the reference distribution provides action uncertainty while remaining aligned with the target task distribution, yielding a more meaningful baseline for confidence measurement. By selecting actions with the highest KL divergence from this uncertainty-awarr reference, MG-Select effectively identifies most confident action sequences while avoiding the limitations of likelihood achieving significant performance improvements in practice. Additionally, propose a joint training strategy that enables the model to learn both conditional and unconditional distributions by applying dropout to state and language conditions, thereby further improving the quality of the reference distribution.
selection,
In experiments, have validated the effectiveness of test-time scaling framework both simulated (Nasiriany et al., 2024; Li et al., 2024; Liu et al., 2023) and real-world benchmarks (Khazatsky al., 2024). Our results show that MG-Select consistently improves state-of-the-ard VLAS (Pertsch et al., 2025) across diverse pick-and-place tasks and various environments. In particular, MG-Select achieves a 28% improvement in real-world in-distribution tasks and 35% in out-of -iff-----ittruttiin tasks, along with 168% relative gain (5.3% + 14.2%) over vanilla greedy decoding on RoboCasa (Nasiriany al., 2024) pick-and-plase tasks trained with 30 demonstrations.
PRELIMINARIES
Problem formulation. We train the policy using the Imitation Learning (IL) framework. Specifically, IL formulates the robot control problem Markov Decision Process (MDP) (Sutton al., 1998) without rewards, M (S. AA P. Po), where S denotes the state space, A the action space, and P(s s. a) E Jo, is the transition probability from state s E s' given action a E A. E [0, 1) represents the discount factor, and Po denotes the initial state distribution. Given a policy
Ty and an expert demonstration dataset D = [ where each trajectory T = {(s,, ax) 4 consists of state-ace aetieacctonn pairs of length [T'), the policy is optimized such that To (s) closely matches the expert action a4 for each demonstration pair.
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Autoregressive VLA. Given state 8; S timestep t and an instruction I L, assume language-conding conditioned VLA parameterized by 8, Te S C + ((A), where C denotes the space of possible language instructions and (AA) denotes the set of probability distributions policy is trained on demonstration dataset D (T', j and outputs a distribution Te st, I) over a E A. We further decompose the state into visual observation or and proprioceptive state qe- 8, (ot, (t) with o E O, 9: E Q, where O and denote the observation and proprioceptive state spaces, respectively. Therefore, the policy's action distribution can be expressed as Ta St. I) Ta or. 9:.1). I). In our test test-time scaling framework, we utilize this distribution through repeated sampling to generate multiple candidate actions. In autoregressive VI.A, continuous action chunk Out+ is with time horizon H extracted from trajectory T and tokenized into an action sequence (a1,..., ar) of variable length T.. The probability of an action sequence factorizes
actions. The
Ta 02 98,
II To 0t, It, (cck),
where ack (a,,...,,,, 1x-1) is the prefix to step k - 1. Let V denote the vocabulary of discrete action tokens. At each step k the model produces a logit vector la E RIVI over V. Applying the softmax function yields the next-token distribution no(- os, qt, [.ck) E [0, 1]M1, which is a categorical distribution over [V possible tokens and sums to
METHOD
We present Masking Distribution Guided Selection (MG-Select), novel test-time scaling framework that selects actions based on confidence scores from a reference action token distribution. In Section 3.1, first introduce overall test-time scaling framework. In Section 3.2, introduce the confidence metric and its reference distribution used in our framework. In Section 3.3, we propose joint training strategy for further improving the quality of the reference distribution in parallel with fine-tuning on the target dataset. We provide the overview of MG-Select Figure 1. For additional details, please refer Appendix A.
3.1 TEST-TIME SCALING FRAMEWORK
While VLAs demonstrate strong performance in robot control tasks, the single-inferene paradigm becomes bottleneck: the model always selects the most probable action from its predicted distri- bution (greedy decoding), even when this action may be suboptimal. This limitation is particularly problematic for tasks requiring high precision, such fine-grained manipulation. propose test-time scaling framework that leverages only the model's internal signals, without relying on external verifiers. consists two stages: (1) parallel stochastic sampling to generate N candidates, and (2) Best-of-N selection using specific criterion M.
resolve this,
Sampling candidate actions. timestep t, the autoregressive VLA Ve samples actions a E A from (a aa] Ot, Qr, I). To obtain N diverse candidates in parallel (batch-inference), we sample with temperature T >
afm ~ ~ Ot, qt, I, T) n = 1, N, 1, Tn.
where T, denotes each action candidate's 's sequence length; <p(::)) softmax((/7) controls distribution sharpness and sample diversity (close to greedy as r 0). This yields the candidate set Á {a(m) > with (m) (a (n) a..., (())).
2. Best-of-N selection. Among the N candidate actions, select the final action according to pre-defined criterion M. This criterion is a metric for selecting the best candidate, and the selected action is given by:
arg max Ma
a(m)
3.2 CONDITION-MASKIIN DISTRIBUTIONAL CONFIDENCE FOR TEST-TIME SAMPLING
For test-time scaling, choosing a proper metric for selecting the best candidate is crucial for effective- When multiple candidate actions generated, need reliable way to identify the most
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promising one. Intuitively, using the model's likelihood for action selection would be the simplest choice. However, this approach is not effective in general because VLAs fine-tuned on target tasks often produce overly concentrated probability distributions over action tokens, causing multiple sampling to converge to the same result. Instead, we propose confidence metric based on the KI. divergence between predicted distribution and a reference distribution that represents uncertainty. This approach is motivated by the insight that actions that deviate most from an uncertainty-wware reference are likely be the most confident and precise.
Confidence over action token distributions. We first define the action token distribution over the action vocabulary V probability distribution P(a:) where a; E V represents the i-th action token. While the VLA produces conditional distributions To Ot: Qt. I, aes) given observations, states, and instruction sequences, reference distributions can be constructed independently of such conditioning. These reference distributions can take various forms, such as uniform distributions over the action vocabulary, task-specific priors or other types of policy distributions. For computing the confidence over the action sequence, we first compute token-level distributional confidence at the i-th step token a; by measuring the distance between the predicted distribution P. = TT(- | Ot, qt. L, aci) and reference distribution Q; as C, KL(Q:] P.), where use Kullback-Leible (KL) divergence as our distributional confidence measure. We then aggregate these token-level confidences across the entire action sequence to obtain the final action-level confidence score for ranking candidate actions. Formally, for an action sequence a = (a), 022 ....... 0r) of length T. we compute the action-level confidence as C, Eiez C. >i Eizz ( P:), where I C { ..., T) represents the set of token indices to be aggregated. The choice of I depends on the action tokenizing scheme: for full sequence aggregation, we use I {1, ...... T). while for partial aggregation, we select specific token ranges based on the tokenization structure.
..,
Condition-masse masking distribution. To construct a reference distribution Q, our hypothesis that reference distribution that is uncertain yet not too distant from the target action token distribution will provide meaningful confidence signals. To this end, we mask specific information (Text, State, or both Text&State) from the input modalities given to the VLA Ta, creating condition-maskin distributions that approximate failure modes where essential conditions for task solving are ignored. Formally, we compute the scoring metric follows:
(Text-masking) KLyext
= KL (- (- |
oc q. 0, aci) T= or q. I,acs)):
(1)
(State-masking) KTstaae KT((T(- oc, 0, T, aes)) mo( ot, qr I, T,a<i));
(2)
(Text&Staae-mmakking KLooth (L(TT(-
or 0, 0, a2x) Te(- |o, qt, 1, aci))),
(3)
For each task environment, the optimal confidence variant can vary. For example, in the SIMPLER- WidowX benchmark (Li et al., 2024), which consists solely of pick-and-place tasks, state-masking confidence works best because the model already memorizes how to pick and place objects without task instructions. In contrast, RoboCasa benchmark (Nasiriany et al., 2024), which has multiple task types, text- masking or text&state rext&state-masking are more effective, since the model cannot determine the correct action without instructions.
3.3 JOINT TRAINING STRATEGY
Although our method can be seamlessly integrated with any autoregressive VLA, existing VLAs are not trained under condition-masking settings, and directly masking inputs often leads to un- intended actions. To address this, we propose new fine-tuning strategy that enables the model to generate condition-masee dard fine-tuning the target dataset. Specifically, we train the VLA with both all-condition and condition masiiiiion-msiingggg data, randomly dropping certain conditions during fine-tuning to the target dataset, thereby increasing awareness of condition-maskinn distributions. Given the dataset D, augment it using four different masking variants applied to the proprioceptive state de and the instruction T: (i) all-condition, (ii) text-masking, (iii) state- masking and (iv) both-masking cases. We then train the VLA with the augmented dataset
distributions while maintaining the performance gains from stan-
M {( I), (ge, 0). (0, I), (0, 0)), corresponding to
D augmented where D Paugmented ((T', f,,g( (m)) f(m)) (q.
(m) ((m) E M) follows:
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Soin uI. (0: (((( B. DroD
log (a) or, 21 (mm) f(m)
(al "ol MimijeM
where a, denotes the action sequence of length T generated at timestep 1. As a result, this fine- tuning strategy enables the VLA maintain performance comparable standard fine-tuning while gaining awareness of condition condition-masking distributions. When combined with our proposed confidence measure, this enhanced model (denoted as MG-Select*) demonstrates improved performance over the original Masking Distribution Guided Selection framework.
EXPERIMENTS
4.1 SIMULATED EXPERIMENTS
To validate the effectiveness of MG-Select, we conduct experiments across diverse robotic simulation environments including RoboCasa, SIMPLER X.MPLER-WidowX, and LIBERO. We fine-tune the pretrained To-FAST model for evaluation on all simulation environments, and additionally fine-tune Open VLA for evaluation on LIBERO to demonstrate that our method improves performance regardless of the underlying model architecture.
4.1.1 SETUP
RoboCasa (Nasiriany et al., 2024). RoboCasa provides 24 atomic tasks set in household kitchen environments. We focus on pick-and-plans tasks, which are particularly challenging since they require high-precision actions (i.e., grasping objects) and are well-suited for evaluating improvements in precision. Following Bjorck al. (2025), train the base model with 30, 100, and 300 demonstrations for each task. For comparison, we also report results for GROOT NI (Bjorck et al., 2025), taken from the original paper.
SIMPLER- Widdow (Li et al., 2024). This benchmark evaluates whether our method improves precision in a real-to-sim setting. Because does not provide simulated training data, train the base model on BridgeData V2 (Walke et al., 2023) and evaluate it on 4 pick-and-place tasks. For comparison, we also report results for RT-1-X (O'Neill et al., 2024), Octo (Team al., 2024), RoboVLM (Liu et al., 2025), and SpatialVLA (Qu et al., 2025), as reported in the SIMPLER paper (Li et al., 2024) and the respective original papers.
LIBERO (Liu et al., 2023). This benchmark evaluates multiple axes of generalization, including variations in layout, objects, and goals, as well long-horizon tasks (LIBERO-LLong) that require sustained high-precision actions.
4.1.2 EXPERIMENT RESULTTS
RoboCasa (Nasiriany et al., 2024). Table I presents the performance of MG-Select with -o-FAST (Pertsch al., 2025) RoboCasa. MG-Select consistently improves the base model across all tasks, including the pick-and-place tasks, and under all demonstration scales. Notably, improvements appear even without joint training, showing that our test-time scaling alone can reliably select higher- precision actions. When combined with joint training, the gains are further amplified, since learning the condition-masking distribution during training provides a more reliable confidence signal for test- time scaling. We also observe particularly strong improvements in the low-data regime. For instance, with only demonstrations, MG-Selee with our joint training achieves 168% relative improvement on pick-and-place tasks over the base model, highlighting that our method effectively compensates for limited performance under scarce data. We provide the detailed results in Appendix B.
SIMPLER-WiGG Widoow (Li et al., 2024). Table 2 shows the performance of MG-Select with xy- FAST (Pertsch et al., 2025) on SIMPLER WidowX. XIMPLER-Wdoww. MG-Select clearly improves the base model across all tasks, demonstrating the robustness of our approach in enhancing action precision. We note that the base model performs relatively poorly on the "put eggplant in basket" task, since its background differs substantially from the other three tasks, making it sensitive to model-specific training configurations. For instance, SpatialVLA (Qu et al., 2025) achieves 100% success on the eggplant task but performs poorly the remaining tasks. Despite this challenge, MG-Select still
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Table 1: Performance comparison on RoboCasa (Nasiriany et al., 2024). We report the average success rate (%) over 50 trials on 24 tasks, including pick-and-place tasks, trained with varying numbers of demonstrations. Results for our methods are averaged over 3 random seeds, while baseline results are taken as reported in the original paper. indicates reproduced performance, and indicates results with additional joint training before applying our test-time scaling framework.
30
100 Demos
300 Demos
Model
Pick and Place
Pick and Place
Pick and Place
GROOT NI
17.4
22.6
To-FAST
5.3
30.9
17.0
40.2
43.2
61.2
MG-Select (Ours)
32.0
22.6
43.7
46.5
MG-Select* (Ours)
14.2
34.6
31.0
48.1
46.9
62.9
Table 2: Performance comparison on SIMPLER-WER WidowX (Li et al., 2024). We report the average success rate (%) over 24 trials on pick-and-plase tasks. Results for our methods are averaged over 3 random seeds, while baseline results are taken as reported in SIMPLER paper (Li et al., 2024) and the respective original papers. indicates reproduced performance, and indicates results with additional joint training before applying our test-time scaling framework.
Model
Spoon on Towel
Carrot on Plate
Stack Cubes
Eggplant in Basket
Average
RT-1-X
0.0
4.2
0.0
0.0
1.1
Octo
12.5
8.3
0.0
43.1
16.0
RoboVLM
29.2
25.0
12.5
58.3
31.3
SpatialVI.A
16.7
25.0
29.2
100.0
42.7
-ASFT
66.7
70.8
41.7
8.3
46.9
MG-Select" (Ours)
69.4
75.0
43.1
13.9
50.3
provides consistent improvements on the eggplant task, indicating that our approach remains effective when the base model struggles. For detailed results, please refer to Appendix B.
LIBERO (Liu al., 2023). Table 6 presents the performance of MG-Select with To FOSTAST (Pertsch al., 2025) LIBERO. In this benchmark, we further extend our evaluation by applying MG-Select to Open VLA (Kim et al., 2024), showing that our approach compatible with different architectures. The results show that MG-Select achieves superior average performance over both base models, demonstrating its general effectiveness. Notably, LIBERO-Objec and LIBERO-Lome are the most challenging task suites (lowest base model performance), and the gains observed on these benchmarks highlight the effectiveness of our test-time scaling framework in improving precision. We provide further details about Open implementation in Appendix A.
4.2 REAL-WORLD WORLD EXPERIMENTS
To further validate our method's generalization beyond simulation environments, we conduct real- world robot experiments on 7-DoF Franka Research robot arm. We use the publicly released To-FAST-DROID as the base model for evaluation, which fine-tunes the pre-trained -0-FAST on the DROID dataset (Khazatsky al., 2024).
4.2. SETUP
In-distribution tasks. We design in-distribution (ID) tasks to evaluate the effectiveness of our method in enhancing base model performance under limited data, as task-specific real-world data is costly to collect. The ID) tasks are pick-and-place tasks defined by a start and goal location, focusing on whether our method can generate tigh-precision actions for objects with different geometries, e.g., teddy bear, cube, rigid cup, and sponge. For these tasks, fine-tune the x -aSST--DROID model on 60 demonstrations per task, consisting of demonstrations for each of the four objects.
Out-of-distribune tasks. We design out-of-distribution (OOD) tasks to evaluate whether our method improves the zero-shot generalization of the policy. We construct 2 OOD tasks involving unseen objects, e.g., .. lighter cup and roll of tape. These OOD tasks pick-and-place tasks similar to the ID tasks, but the policy must generalize to unseen real-world scenes and objects. The gains on these tasks reflect the effectiveness of our method in improving policy robustness.

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No-FAST-DROOT
mg-FAST-DROID MG--Select
(a) Grasping a sponge from the box
To DO-FAST-DROD
TTo Fo-FAST-DROID MG-Select
(b) Releasing sponge the howl
Figure 2: Qualitative results of MG-Select in real-world pick-and-place tasks. We visualize one of our real-world experiments in the "Box to Bowl" task: (a) grasping an object from the box and (b) releasing it into the bowl. The rollout shows that MG-Sclect can generate sih-pprcisioo actions at critical moments for task success, whereas the base policy (To (oo-FAST-DROID) often struggles at these steps.
Table Real-world performance on in-distribution tasks with Franka Research We evaluate our method on seen tasks after multi-task training with 60 demonstrations per task. Each task is defined by a start and goal location with 4 different target objects. We report the average success rate (%) over 24 trials (4 objects x 6 trials) for each task. indicates results with additional joint training before applying our test-time scaling framework.
Pick and Place
Model
Average
Box to Bowl
Box to Plate
Basket to Bowl
Plate to Basket
Th FAST-DDRODD
41.7
45.8 50.0
25.0
37.5
MG-Select* (Ours)
58.3
54.2
29.2
47.9
4.2.2 EXPERIMENTAL RESULTS
In-distribution tasks. Table 4 presents the performance of MG-Select on mo-
Table 3: Real-world performance out-of-distribution tasks with Franka Research 3. We report the average success rate (%) over 16 trials for each task.
FAST-DROID (Pertsch et al., 2025) in in-distribution tasks. MG-Select outper- forms the base model across all tasks, achieving a 28% relative gain. This demonstrates that test-time scaling framework remains effective beyond simulation, enabling gigh-precision actions to complete pick- and-place tasks with diverse objects. We also provide qualitative results about real-world experiments in Figure 2, which show that MG-Select improves the precision of policy at critical moments of leck-and-place tasks, i.e., grasping and releasing, where the base model often fails.
Model
Pick up Tape
Take Cup Bowl
Average
TO-FAAS-DROID
56.3
50.0
53.1
MG-Selecc (Ours)
68.8
75.0
71.9
Out-of-distributin tasks. Table presents the performance of MG-Select To -O-FAA--DROID in out-of-distribution tasks. The results demonstrate that MG-Select can be directly applied generalizable policy, enhancing its robustness and precision novel objects, with 35% improve- ment. Notably, MG-Select shows clear gains on objects that are more difficult to grasp and lift than in-distribution ones, e.g., a roll of tape.
4.3 ABLATION STUDIES AND ANALYSES
We investigate the effectiveness of the proposed components on RoboCasa and conduct the inference latency analysis LIBERO-Object. For component-win wise analysis, RoboCasa with 100 demonstrations, whereas for the latency analysis, we use models trained on LIBERO.
models trained
Inference strategy. Table (a) shows that low-temperature sampling (e.g., T = 0.5) already improves over greedy decoding the jointly trained model. Even simple Best-of-N strategies, such selecting
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M
N
PnP
All
N
PnP
All
Text
State
PnP
All
Greedy
1
28.5
42.7
1
27.6
43.8
31.0
48.1
Sampling
27.6
43.8
2
30.0
46.2
30.1
46.7
Uniform KL
4
30.0
46.5
4
31.0
48.1
29.7
46.3
Likelihood
4
30.5
46.8
8
30.0
46.9
MG-Select
4
31.0
48.1
16
30.7
46.1
32
31.0
46.6
64
33.3
48.4
(a) Inference strategy
(b) Number candidates
(c) Condition-masking variants
Joint-lL MG-Select
PnP
All
7
PnP
All
I
PnP
All
X
17.0
40.2
0.5
27.5
43.9
Sum
26.1
44.5
/
22.6
43.7
1.0
28.8
44.3
Avg
24.7
44.7
x
28.5
42.7
2.0
25.4
43.8
First 1
25.5
44.1
/
31.0
48.1
4.0
31.0
48.1
First 3
27.1
45.5
8.0
30.0
45.5
First 5
31.0
48.1
First 7
29.2
46.3
First 10
26.6
45.1
(d) Effect of joint training
(e) Regularization temperature
(I) Aggregation strategy
Table MG-Select ablation experiments. We present component analysis of our proposed test-time scaling framework on RoboCasa (Nasiriany et al., 2024), trained with 100 demonstrations. We report the average success rate (%) over 50 trials and 3 random seeds. Temperature (r) for stochastic sampling is fixed to 0.5 across all experiments. PnP denotes the 8 pick-and-place tasks, and All denotes the full set of 24 tasks. Gray rows indicate the main results reported in Table 1.
actions by likelihood or KL divergence against uniform reference distribution (Kang et al., 2025), yield further gains. Building on this, MG-Select achieves the strongest improvements, confirming that condition -aatiinnn-mmiiig distributional confidence provides more effective uncertainty signal.
Number of candidates. Table (b) shows that performance increases up to N 64, but the improvement beyond N 4 is marginal. Considering computational efficiency, we adopt N = 4 as the practical point that yields meaningful precision gains.
Condition-maskii masking variants. Table (c) presents the results of different masking variants after joint training. Text-masking achieves the best performance, while other variants remain competitive and outperform the uniform baseline (Kang et al., 2025).
Effect of joint training. Table 5 (d) shows the effect of combining our joint training strategy with MG-Select. Joint training alone already outperforms vanilla imitation learning, likely because mandttinn-masking prevents the model from overfitting. Coupling with MG-Select yields further gains over using MG-Select alone, confirming the effectiveness of the proposed strategy.
Regularization temperature. We empirically find that naively using the condition-maskee distribu- tion (r = 1.0) reference does not work well, as shown in Table 5 (c), compared to uniform- based KL divergence (Kang et al., 2025). It is possibly because "peaked" around certain action tokens, which undermines the purpose of distributional confidence by failing to consider the entire probability distribution. To address this issue, we apply an appro- priate high temperature (e.g., 7 4.0) the condition-masking distribution, which regularizes its concentration and results in superior performance.
coaiittionmmskingg distribution may be
Aggregation strategy. Table (f) shows that the aggregation strategy for action-level confidence is crucial for selecting high-precision actions. Intriguingly, the naive summation of token-level confidence performs the worst, while truncating to the first 5 tokens works best. We hypothesize that the results may be correlated with the nature of the FAST tokenizer (Pertsch et al., 2025), i.e., each action sequence is composed of variable number of action tokens, which are aligned from low- to high-freque frequerr We provide an additional analysis on the truncated FAST tokens and Robocasa performance Appendix H.
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Table 6: Performance comparison on LIBERO (Liu et al., 2023). We report the average success rate (%) over task suites, each consisting of 10 tasks with 50 trials per task. Results for our methods are averaged over 3 random seeds. indicates reproduced performance, and indicates results with additional joint training before applying our test-time scaling framework.
Model
LIBERO-Spuie Spartall
LIBERO-Object
LIBERO-GaD LIBERO-Long
Average
OLtnVLA
85.0
63.4
75.6
52.2
69.1
MG-Select (Ours)
84.8
72.3
74.9
54.7
71.7
Mg-FAST
97.4
95.4
95.6
79.6
92.0
MMG-Select (Ours)
97.2
98.0
94.5
82.7
93.1
Effect of single prefill deployment. Since MG-Select generates multiple candidate actions in parallel, it inevitably introduces additional latency, as the prefill step must be repeated N times. This issue is particularly critical for VLAs, which require prefilling every step when generating action sequences conditioned on the current observation. To address this, we design a single-prefill deployment strategy that shares one prefill across all N candidates before decoding. This significantly reduces the computational overhead, as shown in Figure 3: with N 4, our deployment achieves a 45% reduction in latency compared to vanilla MG-Select. As result, the inference time of MG-Select remains comparable to that of single-action inference across different candidate sizes. We provide the detailed results in Appendix C.
5 RELATED WORK
Vision-L Lasion-Language-Acee models. Developing gen-
900
eralist robot policies has long been central objec-
Epioada Inforeon Laterny (4
450
MG
tive in robotics. Recently, Vision-Languuge-Attinn models (VLAs) have emerged as a prominent ap-
MG Select-Singe Prefil
proach, showing strong performance across diverse downstream tasks through large-scale pre-training on robotic datasets (Driess et al., 2023; Zitkovich al., 2023; Black al., 2025; Pertsch al., 2025; Bjorek et al., 2025). Two common design paradigms have been explored: augmenting a -ainunl model (VLM) with diffussio-based action expert (Black et al., 2025; Bjorck et al., 2025), or converting the VLM into VLA in autoregressive manner (Kim et al., 2024; Pertsch et al., 2025). However, despite these advances, they fundamentally rely single-inference paradigm to generate actions, which increases the risk of eиrors high-precision tasks.
200
150
100
50
22 20
32
64
Candidates
Figure 3: Inference latency comparison on LIBERO-Object. We compare vanilla MG- Select with its efficient deployment variant us- ing single prefill, based on F--FAST (Pertsch et al., 2025).
Test-time computing. Applying additional computation at test time is widely recognized as an effective approach to generate more accurate outputs for challenging tasks across domains. In large language models (LLMs), numerous methods have demonstrated its effectiveness in improving reasoning capabilities, e.g., mathematics, coding, and problem-solving (Chen et al., 2024; Brown et al., 2024; Ehrlich et al., 2025; Song et al., 2024). In robotics, test-time scaling has recently emerged as promising paradigm, which involves repeated sampling combined with external value functions. For example, Nakamoto et al. (2024) ranks candidate actions using value function trained via offline reinforcement learning on diverse robotic datasets, while Kwok et al. (2025) introduces VLM-based action verifiers obtained through reward modeling with synthetic preference datasets. Unlike these approaches, MG-Select requires no external modules. It performs Best-of-N sampling using only the model's intrinsic signals, i.e., condition-maskin distributional confidence. MG-Select consistently improves performance across diverse pick-and-place tasks. Moreover, it offers an efficient framework by eliminating the need for external model loading or interaction, and by introducing optimized parallel sampling that reduces inference latency.
6 CONCLUSION
In this work, we propose MG-Select, a novel test-time scaling framework for Laiiuu-L Action models (VLAss). Our approach leverages condition-masking distributional confidence as

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Published as a conference paper at ICLR 2026
self-generated signal for Best-of-N sampling, enabling precise action selection without external verifiers. This framework mitigates the precision issues inherent in single-inference paradigms and consistently improves policy performance across wide range of simulation and real-world benchmarks. In addition, we introduce joint training strategy and optimized implementation to further enhance both effectiveness and efficiency. We believe MG-Select opens up verifier- test-time scaling paradigm in VLAs, improving robustness and precision solely through the model itself.
REPRODUCIBILITY STATEMENT
We provide implementation details about training and deployment in Appendix A.
ACKNOWLEDGMENTS
This work supported by Institute of Information & communications Technology Planning & Evaluation (UTP) grant funded by the Korea government (MSIT) 19--2099-11190075 Artificial Intelligence Graduate School Program (KAIST); RSS-022-005002733 Global AI Frontier Lab; RS- 2025-02653113, 13, High-Performance Research AI Computing Infrastructure Support at the 2 PFLOPS Scale). We are grateful RLWRLD Inc. for generously providing compute resources and support for the real-world experiments conducted in this work.
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A IMPLEMENTATION DETAILS
A.1 TRAINING ON SIMULATION DATA
Imitation learning. We
representative autoregressive VLA policies base models:
To-FAST (Pertsch et al., 2025): It uses Paligemma-38 VI.M (Beyer et al., 2024) as the backbone and is trained on 2 NVIDIA A A100 GPUs with full fine-tuning from the pre-trained checkpoint. Common training configurations are fixed with the AdamW optimizer and a cosine decay schedule, with warmup_st
1,000, peak_1r= 2.5e-5, decay 2.5e-6, and decay_ steps = 30,000. Training steps, global batch size, and action chunk horizon vary by dataset as shown in Table 7.
Table 7: Training setups of T-ST for different simulation benchmarks.
RoboCasa
Configuration
SIMPLER-WidowX
LIBERO
demos
100 demos
300 demos
Training steps Global batch size Action chunk horizon
3k
5k
20k
10k
10k
64
64
64
64
32
16
16
16
5
10
Open VLA (Kim al., 2024): It uses Prismatic-715 VLM (Karamcheti et al., 2024) as the backbone
and is trained on NVIDIA A100 GPUs with LoRA fine-tuning (r = 32) from the pre-trained checkpoint. We use a global batch size of 32 for LIBERO (Liu al., 2023), while other training configurations follow the official VLA VLA implementation. Note that, consistent with the OpenVLA configuration, we train the model separately on each LIBERO benchmark rather than performing multi-task fine-tuning.
Joint imitation learning. Joint imitation learning strictly follows the training configuration of the aforementioned imitation learning, differing only in the data configuration, as it incorporates condition-dropout data. We consider 3 variants of dropout data, (1) text-masking, (2) state-masking, and (3) both text&state-masking text&state) in RoboCasa and LIBERO, and only dropout 10% of state data in SIMPLER -EE-WddoXX VLA, VLA,
For T-SFAST
randomly dropout 10% (text/statc/both
For Open input.
apply 10% dropout only text condition since Open VLA does not receive state
A.2 TRAINING ON REAL WORLD DATA
Imitation learning. We use the official mo --SS--DDOOD (Pertsch et al., 2025) checkpoint as the base model for real-world experiments. We fine-tune on our manually collected dataset using 2 NVIDIA A100 GPUs with full fine-tuning for 10k steps. Training performed with a global batch size of 64 and an action chunk horizon of 10. Other configurations are fixed with the Adam W optimizer and a cosine decay schedule, with warmup_stt steps 300, peak 1e-5, decay 1r 1e-6, and decay_steps = 30,000.
Joint imitation learning. We follow the same training configuration as the above imitation learning setup, while additionally applying random dropout of 10% 00%/1%% (text/state/both text&state).
A.3 DEPLOYMENT
MG-Select's main hyperparameters are: (1) the sampling temperature т, (2) the number of candidate actions N, (3) the variant of condition-masking condition-masking distribution. We search for the optimal configuration on each dataset within the following ranges: {0.1, 0.3, 0.5, 0.7, 1.0), N e {4, 8), variants E [text, state, text&state), and regularization temperature E (4.0, 6.0, 8.0, 10.0, 12.0, , 14.0, 16.0). and report the best result for each policy.
and (4) the regularization temperature for the
For aggregating token-level confidence scores, we use the summation the first 5 tokens by default in FAST.. In contrast, for OpenVLA, since its output sequence length is fixed to the action dimension of the training data. Additionally, only the text-masking variant is applied in VLe as it does not take state input.
use the average score across the entire token sequence,
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B DETAIL RESULTS ON SIMULATION EXPERIMENTS
Table 8: Detailed performance comparison on RoboCasa (Nasiriany et al., 2024). We report the average success rate (%) over 50 trials, trained with varying numbers of demonstrations. For clarity, the 24 tasks are grouped into three categories: pick-and-place, open-and-close and others. Results for methods averaged over 3 random seeds, while baseline results are taken from the original paper (Bjorck et al., 2025), y indicates reproduced performance, and * indicates results with additional joint training before applying our test-time scaling framework.
Demos
Demos
300 Demos
Model
Open
Open
Open
GROUT N1
0.4
26.0
26.0
17.4
2.2
52.8
43.3
32.1
22.6
68.3
60.0
49.5
FAST!
53
51.3
39.2
30.9
17.0
60.7
46.5
40.2
13.2
74.7
67.4
61.2
MG-Seleet" (Ours)
50.1
Table Detailed performance comparison on SIMPLER- W (Li al., 2024). We report both the task success and the grasp success rate (%) over 24 trials on 4 pick-and-place tasks. Results for our methods are averaged over 3 random seeds, while baseline results are taken from the SIMPLER paper (Li et al., 2024) and the respective original papers (Liu al., 2025; Qu al., 2025). i indicates reproduced performance, and indicates results with additional joint training before applying our test-time scaling framework.
Spoon on Towel
Carrot Plate
Stack Cubes
Eggplant Basket
Average
Model
Grasp
Success
Grasp
Success
Grasp
Success
Grasp
Success
Grasp
Success
RT-1-X
16.7
0.0
20.8
4.2
8.3
0.0
0.0
0.0
11.5
1.1
Octo
8.3
46.5
16.0
RoboVLM
54.2
29.2
25.0
25.0
45.8
12.5
58.3
58.3
45.8
31.3
SpatialVI.A
20.8
16.7
29.2
25.0
62.5
29.2
100.0
100.0
53.1
42.7
FA-T
83.3
66.7
83.3
70.8
91.7
41.7
8.3
8.3
66.7
46.9
MG-Select* (Ours)
87.5
69.4
83.3
75.0
79.2
43.1
26.4
13.9
69.1
50.3
C DETAIL RESULTS ON EFFICIENT DEPLOYMENT STRATEGY
Table 10: Detailed inference latency comparison on LIBERO-Object. This table presents the detailed results corresponding to Figure 3, comparing vanilla MG-Select and MG-Select with the single prefill strategy. We report the average inference latency over 10 episodes for each of 5 random seeds, across different numbers of N candidates. Latency is measured NVIDIA A100 GPU. indicates lower values are better.
Latency (s, 4)
N
MG-Select
MG-Select Single Prefill
1
20.2
20.2
2
30.4
22.7
4
43.4
23.7
8
62.0
24.3
16
76.0
30.4
32
201.0
34.1
64
430.0
43.1
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D ADDITIONAL EXPERIMENTS
D.1 EFFECT OF DROPOUT RATIO
Table 11: Ablation experiment dropout ratios. We present dropout ratio analysis for joint imitation learning on RoboCasa (Nasiriany et al., 2024), trained with 100 demonstrations. We report the average success rate (%) over 50 trials and random seeds. From left to right, the dropout ratio corresponds to text, state, and both text&state conditions. PnP denotes the pick-and-plaas tasks, and All denotes the full set of 24 tasks. Blue rows indicate the main results reported in Table
Dropout Ratio (%)
PnP
All
5/ 5/ 5
24.2
45.3
10/10/10 20/20/20
31.0
48.1
27.9
46.1
Dropout ratios on RoboCasa. We investigate the effect of the dropout ratio in joint imitation learning for MG-Select. Table shows that ratio 10%/10%//10% achieves the best performance. We hypothesize that small ratios (e.g., 5%) are insufficient for learning meaningful masking distribution, while large ratios (e.g., 20%) make the masking distribution too similar to the all-condition distribution, leading our confidence metric to select suboptimal action.
D.2 EFFECT OF AGGREGATION STRATEGY
Table 12: Ablation experiment on aggregation strategies. We report the average success rate (%) 24 trials 4 pick-and-place tasks in SIMPLER-Widov methods are averaged over 3 random seeds. Blue rows indicate the main results reported in Table indicates reproduced performance, and indicates results with additional joint training before applying our test-time scaling framework.
(Li al., 2024). Results for our
Model
Spuon on Towel
Carrol on Plate
Stack Cubes
Eggplant in Basket
Average
FAST!
66.7
70.8
41.7
8.3
46.9
MG-Select* (First 1)
68.1
70.8
20.8
52.1
MG-Select* (First 5)
69.4
75.0
43.1
13.9
50.3
MG-Select* (First 10)
66.7
76.4
38.9
22.2
$1.0
MG-Select* (Sum)
62,5
73.6
43.1
16.7
49.0
MG-Select* (Avg)
66.7
68.1
40.3
16.7
47.9
Aggregation strategies HIMPLER-Widow! We investigate whether the aggregation strategy for action-level confidence remains important in different domain. Table 12 shows that the truncation strategy consistently outperforms naive summation and averaging, and intriguingly, truncating first token yields the best performance. This suggests that Nomainn-speciff tuning of the token-span span size can further improve MG-Select. Nevertheless, we adopt the first tokens truncation default, shows robust and superior performance across action domains.
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D.3 EFFECT OF MODEL SCALE
Table 13:
Additional performance comparison on LIBERO (Liu et al., 2023). We report the average success rate (%) over 4 task suites, each consisting of 10 tasks with 50 trials per task. Results for our methods are averaged over random seeds. f indicates reproduced performance, and indicates results with additional joint training before applying our test-time scaling framework.
Model
LIBERO-SHN -paatal
LIBERO-Object
LIBERO-Goal
LIBERO-Long
Average
MiniVLAt
79.4
38.8
68.0
30.2
54.1
MGG-Sllet** (Ours)
76.8
60.9
72.1
32.3
60.5
Implementation Details. MiniVLA (Belkhale Sadigh, 2024) is 7x smaller variant of Open containing only 1B parameters. It uses Qwen 2.5 0.5B backbone while retaining the same VIT used in Ope We fine-tune MiniVLA with vanilla imitation learning on the full set of LIBERO training data for 30k iterations with global batch size of 128. Joint imitation learning includes 10% text-only dropout, as MiniVLA does not take state inputs. We adopt the average aggregation strategy, because the output token length is fixed to the action dimension.
Experimental Results. Table presents the performance of MG-Select with MiniVLA (Belkhale Sadigh, 2024) on LIBERO. The results show MG-Select significantly outperforms the base model on average, indicating that our test-time scaling framework generalizes across different VLAs and model scales.
D.4 COMPARISON WITH ADDITIONAL BASELINES
Table 14: Additional performance comparison on HMMPERR-Wddw' (Li et al., 2024). We report the average success rate (%) over 24 trials 4 pick-and-place tasks. Results for RoboMonkey (Kwok et al., 2025) and our methods are averaged over 3 random seeds. Blue rows indicate the main results reported in Table indicates reproduced performance, and indicates models trained with joint imitation learning. For a fair comparison, we match the number of candidates in RoboMonkey that used by MG-Select. Latency is measured on an NVIDIA RTX A6000 GPU.
Model
N External Verifier Latency (ms, }) Spoce on Towel
Carret on Plate
Stack Cubes
Eggplant Basket
Average
FA-FAST
616.2 (1.00x)
66.7
70.8
41.7
8.3
46.9
-RoboMonkey"
1194,9
-GG-Select (Ours)
880.3 (1,43x)
69.4
75.0
43.1
50.3
Implementation Details. RoboMonkey (Kwok et al., 2025) is recent test-time scaling method that uses a LLaVA-7B VLM-based verifier select the optimal action. We aim to investigate whether our confidence metric can perform as well as an external verifier. To explicitly compare the two approaches, we fix Gaussian perturbation and majority voting process in RoboMonkey, using only the verifier to perform Best-of-N selection from sampled action chunks.
and the = 4 for parallel stochastic sampling, and remove the
Experimental Results. Table 14 shows that MG-Select achieves competitive performance compared to RoboMonkey without requiring an external verifier. MG-Select substantially more efficient than RoboMonkey in terms of latency, demonstrating that our method is not only effective but also practical test-tine time-time scaling approach.
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E EFFECT OF TEMPERATURE IN CONDITION-MASKING DISTRIBUTION
In our proposed method, we utilize a condition maniitin-mmsking distribution to serve as the reference distribution for the confidence metric. Here, define the general condition-masking distribution as maskedd which corresponds to the first argument the KL divergence terms (see Eq. (I), (2), and (3)). In this section, provide theoretical derivation how temperature affects the entropy of the reference distribution maakked
E. THEOREM
Theorem. Let Tiikee 7) be the temperature condition mudition-masking reference distribution.
Then its entropy (mmsked) is monotonically increasing in temperature 7 >> 0:
HH (masked)
0.
Ör
Thus, using higher temperature (e.g.. = 4.0) explicitly increases the entropy of the reference distribution, preventing confidence metric from being biased by the over-confidence (low-entropy) of the masked distribution itself.
E.2 PROOF
Let =(a) denote the logit (unnormalized log-probability) of action A from Tmasked- The probability of under temperature is defined as:
masked (a; T) exp(2(a)/T)
where Z(T) = Σ exp(z(a')/T).
(5)
Z(T)
The entropy of this distribution is defined as:
H (Tmasked)
Σ
Tmasked (a; log Taakked (a; T).
(6)
First, we expand the entropy term using the definition of the softmax distribution:
Tmasked T) 2((a)
H (Tmasked) =
log (T)
log (TT)
Σ maskeed (a; ()
Taakked (a; ())(a)
log Z(T)
E Tendee [2(A)].
Differentiating H (Tmasked with respect to т. We first note the derivative fog-partition function log (T))
0 log 7(T)
2(a))
(-2(()
z(a))
Σ
exp
Z(T)
72
Σ masked (a; T)=(a)
72
Euuneed [x(A)).
(8)
Differentiating Eq. (7) with respect to T:
0H
i log Z(T)
(-1 Frmmae [=(A)] 1 OE 2E a [=(A]]
(9)
Or åT
Substituting Eq. (8) into the first term, the expected value terms cancel out:
0H 1 UE
&.E.man [=(A))
(10)
0T
T
ØT
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Published as a conference paper at ICLR 2026
Next, to evaluate the derivative of the expectation Errasta (=(4), the chain rule for Orraaded (a:r)
macked (a;
log T (;; T)
T masked (a; +)
2(0) 72
Olog [(T)
= maakkod (a;
TTuskedd (a; 7)
(Eransse
[=(A]] -(a))-
(11)
Using this, the derivative of the expectation becomes:
B [x(A)]
maskedd (a;
2(a)
a
masked (a; ())()((a) (Braasted [=[A)] z(a))
72 ( Em [=(A))) Brrmarra (=
Var Var [x(A)]
(12)
Finally, substituting this result back into Eq. (10):
HH (Tmasked)
1
1
Var [=(A)]
Var a [=(A)].
(13)
7
73
Since Var ansite (=(A)) 2 and
O, we conclude:
OH (Tmasked)
0.
(14)
F RESULTS ON CALVIN BENCHMARK
Table 15: Performance comparison on CALVIN (Mees et al., 2022). We report the success rate for each instruction chain and the average number of consecutive successes over 5 instruction chains. The model trained on environments A, B and and zero-shot evaluation performed on novel environment D. Results for methods are averaged over 3 random seeds. f indicates reproduced performance, and indicates results with additional joint training before applying our test-time scaling framework.
Tasks Completed in Row (%)
Method
Task
Avg. Len (t)
1
2
3
4
5
To FASTT
ABC + ABC -
96.0
85.8
74.4
62.4
50.6
3.69
MG-Select* (Ours)
96.9
88.0
77.8
67.6
55.8
3.86
To demonstrate that our method is also effective for long-horizon multi-step planning tasks, we evaluate MG-Select on CALVIN benchmark in the zero-shot setting.
Setup. CALVIN (Mees al., 2022) consists of 34 distinct tasks and uses Franka Panda Arm for manipulation. We evaluate on the ABC - D setting, measuring whether the model can execute long-horizon danguage-conditione tasks in zero-shot manner. We fine-tune FAST with vanilla imitation Icarning environments A.B,C for 5k iterations with global batch size of 32. Then, we evaluate the model on environment using 1000 instructions. Joint imitation learning includes 10%//10%/105 dropout, and MG-Select searches for optimal deployment hyperparameters within the ranges defined in Appendix A.
Experimental Results. Table 15 presents that MG-Select consistently outperforms the base model across all consecutive tasks, demonstrating our proposed method's generalizability to long horizon and multi-step planning.
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Published as a conference paper at ICLR 2026
G VISUALIZATIONS OF MG-SELECT
(a) Task Turn on stove. Top OpenVLA, Bottom OpenVLA + MG-Select (Ours).
(b) Task: Put the bowl on top of the cabinet. Top: Open Bottom Vpen MG-Select (Ours).
Figure 4: Visualization of failure cases of MG-Select LIBERO-Goal. We show representative failures corresponding to minor performance drop in LIBERO-Goa In simple and atomic tasks, MG-Select may introduce unnecessary stochasticity, leading to slight misalignment between the gripper and object, whereas the base model already produces near-optimal actions.
Rveroge per Towen
25
68
85-
80
75-
C6
Action Chunk Index
Figure 5: Visualization of token-level KL divergence of MG-Select on SIMPLER WidowX. We visualize the token-level state-masking confidence (see Eq, (2)) for successful pick-and-place episode in SIMPLER WidowX. Average KL per token denotes the mean KL over first action tokens, and the box box-plots show KL statistics across action candidates (N 4). Each frame corresponds to the state observed after executing the respective action chunk. We observe that KI. rises sharply during the alignment phase (C1-C2), where state information is crucial for action prediction. At the same time, KI. values among candidates also vary, and the highest-Ke KI. candidate produces the correct alignment. Similar patterns obsersved in grasping (C10) and placement (C13).
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Published as a conference paper at ICLR 2026
H DETAIL ANALYSIS OF TRUNCATED FAST TOKENS
Aggregation Strategy
First 1
First 3
First 5
RoboCasa Performance (%)
47
First 7
45
44
0.7
0.8
0.9
1.2
1.3
Normalized Reconstruction Error (o)
Figure 6: Correlation between action reconstruction error and RoboCasa performance. We compare the performance of truncated aggregation strategies (see Table 5 (f)) against the action reconstruction error using FAST (Pertsch et al., 2025) tokens on RoboCasa (Nasiriany al., 2024) with 100 demonstrations. Reconstruction error is measured by tokemizing continuous actions and detokenizing them using only the first K tokens. The error is reported as the Mean Absolute Error (MAE) normalized by the standard deviation of the original actions, averaged across all action dimensions.
Action reconstruction from FAST tokens. We analyze the behavior of the FAST (Pertsch et al., 2025) tokenizer, which produces variable-length token sequences ordered from low-frequency to high-frequency components. To understand how these tokens relate to action information, conduct a simple action reconstruction experiment on the RoboCasa dataset using 100 demonstrations per task: (1) tokenize continuous actions into discrete FAST token sequences, (2) truncate the first K tokens, and (3) detokenize the truncated sequence back into continuous actions. We evaluate E (1, 5, 7}, corresponding RoboCasa's ' 7-dimensional action space. As a reconstruction metric, we measure the mean absolute deviation between the original and detokenized actions with the first K tokens, normalized by original action's standard deviation. Interestingly, we find that reconstruction error negatively correlated with RoboCasa performance. In particular, using only the first 5 tokens provides reasonable reconstruction, suggesting more stable and length-invariant invariant confidence measure than naively aggregating all FAST tokens, as shown in Table (f).
20

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Published as a conference paper at ICLR 2026
1 LLM USAGE DISCLOSURE
We acknowledge the use of large language models (LLMs) in preparing this manuscript. LLMs were employed solely refine writing quality, including grammar correction, vocabulary suggestions, and typographical checks. All substantive ideas, analyses, and conclusions in this paper are entirely the work of the authors.
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Reviews:
[{"review_id": "cDVBbiWMNu", "reviewer": "Reviewer_jiih", "summary": "This paper proposes MG-Select, a verifier-free test-time scaling framework for Vision-Language-Action (VLA) models in robotics. Instead of relying on external verifiers or further model training, MG-Select leverages a KL-based confidence metric between a conventional model distribution and a reference distribution generated by masking certain language or state inputs. The method incorporates a novel joint training strategy to improve representations for both conditional and unconditional inputs, and is validated through comprehensive experiments on simulation and real-world benchmarks, achieving substantial improvements in both in-distribution and out-of-distribution robotic tasks.", "questions": "1. Can the authors provide a direct, quantitative comparison or ablation with recent external-verifier methods (e.g., RoVer, RoboMonkey) on any of the main tasks or with open-sourced baselines, so as to substantiate claims of equal or better performance (and efficiency) of MG-Select?\n\n2. Table 5 shows that “Text-masking” performs best for most tasks—can the authors formalize or empirically analyze why this is the case, and whether this result holds if tokenization schemes or action domains are changed? How robust is this preference for “text masking” versus other possible masking strategies?\n\n3. The confidence metric $C_{\\mathbf{a}}$ is calculated over the first 5 tokens (Table 5f). Is this choice robust? Does aggregation over different token span sizes appreciably affect the selection quality, or is this a dataset-specific artifact?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- Verifier-free paradigm: The work addresses a clear practical limitation of prior VLA test-time scaling methods by eschewing external verifiers and any additional model components, simplifying deployment.\n\n- Elegant use of KL divergence: The use of KL divergence as a confidence measure between the “full information” and “condition-masked” distributions is both theoretically motivated and practically appealing, connecting well with recent LLM self-consistency work but tailored for the robotics domain.", "weaknesses": "1. While the masking variants (text, state, both) and truncation (first 5 tokens in Table 5f) offer empirical benefit, the rationale for these choices is left at the level of intuition/hypotheses. Given that “naive summation” (Table 5f) performs quite poorly, a more careful analysis—perhaps formalizing when/why these aggregation choices yield confident actions—should be pursued.\n\n2.  The paper defines the action-level confidence as $C_{\\mathbf{a}}=\\sum_{i \\in \\mathcal{I}} \\mathrm{KL}(P_i | Q_i)$ (Section 3.2), but is not explicit about (i) whether KL is computed at each timestep over the full action token vocabulary or just next-token distributions, (ii) how $\\mathcal{I}$ is selected when token chunking is variable, and (iii) whether the reference $Q_i$ is static or recalculated for each sampled action sequence. The choices in Table 5f (Sum, Avg, First 5) are empirical, but the paper lacks a principled definition for $\\mathcal{I}$ in general VLA architectures.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rw9chBt86u", "reviewer": "Reviewer_wMxK", "summary": "The paper proposes Masking Distribution Guided Selection (MG‑Select) to do test‑time scaling method for VLAs. \n\nThe idea is to generate N action candidates with stochastic decoding and then pick the one with the highest distributional confidence, defined as a KL divergence between the model’s conditional next‑action distribution and a reference distribution produced by the same model with certain inputs masked (text, state, or both). Experiments on RoboCasa, SIMPLER‑WidowX, LIBERO, and a Franka arm show consistent gains, especially in low‑data regimes.", "questions": "1. Why choose main baseline as pi-0-fast? Which is not the best performing model by itself. What if applied on other model such as OpenVLA-OFT\n2. How does it compare to some test time training baseline such as simple entropy minimization methods?\n3. Can you provide more details about the masking strategy and aggregation design.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Simple and practical: Uses only the base VLA; no external verifier or extra training objective at inference time. Easy to bolt on to existing autoregressive policies\n\nGood performance gain especially in low-data and OOD settings.", "weaknesses": "This method adds some latency due to increased compute", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EC0tKTsNDw", "reviewer": "Reviewer_6QZo", "summary": "The paper proposes MG-Select, a self-verifying test-time selection mechanism for Vision-Language-Action (VLA) models. Instead of relying on an external verifier or value function, MG-Select computes KL divergence between masked and unmasked modality predictions as a measure of epistemic uncertainty. The approach enables verifier-free scaling at inference and is evaluated on multiple robot manipulation datasets (RoboCasa, LIBERO, DROID). The authors claim performance improvements on OOD (out-of-distribution) tasks while reducing verifier overhead.", "questions": "1.\tHave you considered the effect of model scale—does MG-Select generalize across small and large VLAs?\n\n2.\tHow sensitive is performance to the number of candidates (N)? Does uncertainty estimation saturate after a threshold?\n\n3.\tCould masked divergence be integrated into learning-time regularization to improve robustness, rather than only test-time selection?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe paper identifies a key bottleneck in large multimodal agent systems: dependency on external verifiers during test-time scaling. To solve this, this paper attempts to replace it with a lightweight, self-contained mechanism. The idea of using internal divergence between masked/unmasked representations as a proxy for self-consistency is an intellectually interesting alternative to ensemble- or verifier-based uncertainty estimation. This motivation aligns well with emerging trends in verifier-free inference for embodied models like OpenVLA or RT-2.\n\n2.\tMG-Select’s implementation is refreshingly simple: sample multiple candidate actions, compute divergences under masked inputs, and select the candidate with the smallest divergence. The method avoids retraining or architectural modification, making it plug-and-play for deployed VLAs. This simplicity could enable fast adoption in practical robotic systems.\n\n3.\tThe experiments span multiple datasets and include an ablation study on the number of samples and masking strategies. The consistency of improvement across different settings suggests the metric is robust to domain variation. Particularly, the evaluation on DROID with real-robot rollouts adds credibility to the claim of practical deployability.", "weaknesses": "1.\tThe KL divergence between masked and unmasked predictions is treated as an uncertainty signal, but the paper does not connect it to a formal epistemic uncertainty framework. For example, there’s no derivation relating it to Bayesian variance, entropy-based uncertainty, or self-consistency under dropout. Without such justification, it remains an empirical heuristic.\n\n2.\tWhile MG-Select eliminates external verifiers, it performs multiple forward passes (one per candidate), which could offset computational gains. The authors mention this but provide no latency or throughput benchmarks. In real-time control, inference latency often dominates, and without such metrics, claims of “efficiency” are weak.\n\n3.\tAll evaluated tasks involve short-horizon manipulation with static visual backgrounds. The method’s efficacy under long-horizon planning, sequential reasoning, or language ambiguity (e.g., temporal grounding) remains unexplored.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "A8rn7pGQbC", "reviewer": "Reviewer_CKBD", "summary": "The paper proposes Masking Distribution Guided Selection (MG-Select), a verifier-free test-time scaling framework for Vision-Language-Action (VLA) models that enhances robotic action precision without additional training or external verifiers. MG-Select computes a confidence score based on the KL divergence between the model’s predicted action-token distribution and a reference distribution generated by masking input conditions (language or state), allowing the model to identify the most reliable action among multiple sampled candidates. A joint imitation learning strategy further improves this reference distribution by randomly dropping conditions during fine-tuning. Experiments on RoboCasa, SIMPLER-WidowX, LIBERO, and real-world Franka tasks show consistent gains—up to 168% relative improvement in low-data regimes—demonstrating that MG-Select effectively boosts precision and robustness while remaining computationally efficient and generalizable.", "questions": "1. Include a theoretical justification of why the KL divergence from a masked reference distribution correlates with action optimality. Formal statements or lemmas demonstrating its reliability would strengthen the novelty claim.\n\n2. Add more mathematical insight or justification in the appendix—for instance, derivations of how temperature regularization affects the entropy of the reference distribution, or an ablation on different masking ratios to validate robustness.\n\n3. Include representative failure cases or visualizations to illustrate limitations (e.g., when MG-Select slightly underperforms in LIBERO-Goal). Consider adding evaluations on more complex, compositional manipulation tasks to assess generalization breadth.\n\n4. Clearly specify which baselines are reproduced and which are cited from prior work. Provide full reproduction details (training steps, batch sizes, learning rates) in the appendix to ensure fair comparison.\n\n5. Add a visualization of token-level KL divergence over time or provide statistics on token-wise confidence contributions to improve interpretability and transparency.\n\n6. Tighten the abstract by focusing on conceptual insights rather than numeric claims, and clarify how relative improvements are computed. The conclusion could also better articulate MG-Select’s potential to shape future verifier-free test-time scaling paradigms in robotics.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper proposes Masking Distribution Guided Selection (MG-Select), a verifier-free test-time scaling approach for Vision-Language-Action (VLA) models. The method leverages internal condition-masking distributions to construct a self-generated confidence metric, which is a timely and meaningful contribution to the emerging direction of verifier-free test-time optimization.\n\n2. The methodology section (Section 3) is well structured and logically divided into sampling, confidence estimation, and joint training components.\n\n3. The experiments are comprehensive, spanning multiple simulation benchmarks (RoboCasa, SIMPLER-WidowX, LIBERO) and real-world setups (Franka Research 3). The results demonstrate consistent improvements, indicating good generality.\n\n4. The comparisons include strong baselines such as RT-1-X, Octo, RoboVLM, and SpatialVLA, which enhances credibility.\n\n5. Table 5 provides a detailed ablation over candidate number, masking variant, and aggregation strategy.\n\n6. The writing is clear, the structure follows ICLR standards, and Figure 1 effectively conveys the pipeline.", "weaknesses": "1. The boundary of innovation is somewhat vague. The proposed confidence estimation is closely related to recent “self-certainty” or “TrustScore” approaches in LLMs (e.g., Zheng et al., 2024). The paper does not clearly articulate how MG-Select differs conceptually or theoretically—whether it introduces a new mathematical formulation, or provides theoretical guarantees for its confidence metric.\n\n2. Equation (2) for joint imitation learning is intuitively reasonable but lacks explanation regarding the fixed 10%/10%/10% masking ratio. No sensitivity analysis or theoretical rationale is provided for this choice. Similarly, the effect of the regularization temperature in the KL divergence is discussed empirically but not theoretically.\n\n3. Despite extensive experiments, there is a lack of qualitative or failure-case analysis. The method is only evaluated on pick-and-place tasks, while scenarios requiring language reasoning or multi-step planning are absent.\n\n4. Some baseline results are quoted from original papers, while MG-Select models are fine-tuned or re-trained under new settings, potentially introducing data or optimization biases.\n\n5. The paper gives only empirical explanations for why aggregating the first five tokens yields the best performance, without analyzing how this relates to the FAST tokenizer structure or action semantics.\n\n6. There are repeated phrases (e.g., “achieves significant performance improvements”), and the abstract’s statement of “168% relative gain” may be misleading without specifying the baseline reference.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)