MobileIPL: Enhancing Mobile Agents Thinking Process via Iterative Preference Learning
Authors: Kun Huang
Weikai Xu
Yuxuan Liu
Quandong Wang
Pengzhi Gao
Wei Liu
Jian Luan
Bin Wang
Bo An
Venue: ICLR 2026 Poster (2026)
Keywords: Multimodal
Mobile-Agent
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: The Chain of Action-Planning Thoughts (CoaT) paradigm has been shown to improve the reasoning performance of VLM-based mobile agents in GUI tasks. However, the scarcity of diverse CoaT trajectories limits the expressiveness and generalization ability of such agents. While self-training is commonly employed to address data scarcity, existing approaches either overlook the correctness of intermediate reasoning steps or depend on expensive process-level annotations to construct process reward models (PRM). To address the above problems, we propose an Iterative Preference Learning (IPL) that constructs a CoaT-tree through interative sampling, scores leaf nodes using rule-based reward, and backpropagates feedback to derive Thinking-level Direct Preference Optimization (T-DPO) pairs. To prevent overfitting during warm-up supervised fine-tuning, we further introduce a three-stage instruction evolution, which leverages GPT-4o to generate diverse Q&A pairs based on real mobile UI screenshots, enhancing both generality and layout understanding. Experiments on three standard Mobile GUI-agent benchmarks demonstrate that our agent MobileIPL outperforms strong baselines, including continual pretraining models such as OS-ATLAS and UI-TARS. It achieves state-of-the-art performance across three standard Mobile GUI-Agents benchmarks and shows strong generalization to out-of-domain scenarios.

Published as a conference paper at ICLR 2026
MOBILEIPL: ENHANCING MOBILE AGENTS THINKING PROCESS VIA ITERATIVE PREFERENCE LEARNING
Kun Huang',, ", Weikai Xu Yuxuan Liu Quandong Wang', Pengzhi Gao', Wei Liu', Jian Luan', , Bin Wang', Bo An
Xiaomi Inc. "Nayyang Technological University
*Gaoling School of Artificial Intelligence, Renmin University of China huangkun| I @ (exaomii.com xuwk266 @ @gmail.com
ABSTRACT
The Chain of Action-Planning Thoughts (CoaT) paradigm has been shown to improve the reasoning performance of VLM-basee mobile agents in GUI tasks. However, the scarcity of diverse CoaT trajectories limits the expressiveness and generalization ability of such agents. While self self-training commonly employed to address data scarcity, existing approaches either overlook the correctness of intermediate reasoning steps or depend on expensive process-level annotations construct process reward models (PRM). To address the above problems, we propose an Iterative Preference Learning (IPL) that constructs a CoaT-tree through iterative sampling, scores leaf nodes using rule-based reward, and backpropagates feedback to derive Thinking-level Direct Preference Optimization (T-DPÓ) pairs. To prevent overfitting during warm-up supervised fine-tuning, we further introduce a three-stage instruction evolution, which leverages GPT-4o to generate diverse Q&A pairs based on real mobile UI screenshots, enhancing both generality and lay- out understanding Experiments on three standard Mobile GUI-agent benchmarks demonstrate that our agent MobileIPL outperforms strong baselines. including continual pretraining models such as OS-ATI and UI-TARS. It achieves state- of-the-art performance across three standard Mobile GUI-Agents benchmarks and shows strong generalization to out-of-domain scenarios.
INTRODUCTION
VLM-based mobile agents (Wang al. 2023 Ding 2024) have attracted considerable attention due to their ability seamlessly interact with mobile graphical user interfaces (GUIs) and their potential to autonomously perform daily tasks. Since actions are not directly specified in user instructions, mobile agents benefit from generating intermediate thoughts aligned with the current GUI context. Recent work such as AITZ(Zhang et al.) (2024)) has demonstrated that the Chain Action-Plammin Thoughts (CoaT) pattern resembling the slow-thin slow-thinking "System 2" process is particularly effective in GUT domains.
However, directly applying supervised fine-tuning (SFT) CoaT trajectories may cause overfitting, leading the model to be trapped in fixed reasoning patterns. To address this limitation, recent studies in the general domain have explored self-training strategies. These approaches typically utilize the correctness of the final answer in output as a reward signal to train the (oode((Luong et al. 2024). While effective some contexts, relying solely on final answers overlooks the quality of intermediate reasoning steps, which can result in reward hacking and suboptimal reasoning processes. Some search-based approaches, such as ReST-MCTS (Xie et al. 2024), tackle this problem by learning process reward model (PRM) to evaluate individual reasoning steps. However, these approaches often require large-scale manual annotation of intermediate steps (Guo et al. (025a)). This challenge is especially severe in the Mobile GUI Agent domain. Unlike text-based tasks in coding or math, GUI
"Equal contribution.
Work done during the internship at XiaoMi.
"Bo An is the corresponding author.
pretps://huggee ace. ct/ co//attaset///wwk////lllleete

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Published as a conference paper at ICLR 2026
environments rely real devices simulators, making step-level reward annotation significantly more costly and labor-intensio inteniintensive.
To address these limitations, we propose an iterative sampling framework that constructs C-aT-tree based on Monte Carlo Tree Search (MCTS). Instead of relying on a PRM, we score each reasoning step and construct thinking-level DPO (T-DPO) pairs without manual step annotation. Specifically, we perform multi-turn dialogue with a vision-language model (VLM) to incrementally build a CoaT- tree, where each node corresponds to a sampled response at a given reasoning step, conditioned on the dialogue history. This hierarchical structure captures diverse reasoning paths and facilitates fine-grained assessment of intermediate thoughts. We first assign rewards to the leaf node, and then propagate these signals backward through the CoaT-tree to earlier reasoning steps. Based on the resulting values, we construct thinking-level DPO pairs to help agents optimize both final actions and the overall quality of their reasoning.
To mitigate the lack of diversity after warm-up SFT, we adopt an instruction evolution strategy. Specifically, we generate diverse Q&A pairs grounded in real mobile UI screenshots from downstream training datasets. These Q&A pairs serve two purposes: (1) prevent agents from overfitting to static downstream instructions by introducing varied reasoning contexts, and (2) improve agents' understanding of UI layouts through visually grounded question-answering. We evaluate our approach the CoaT dataset AITZ and long-horizon dataset AMEX, where it outperforms state-of-th the- art GUl-agent continual pretraining agents such as OS-ATLAS (Wu et al.) 2024) (+4.04%) and UI-TARS (Qin et al. 2025) (+3.54%) Furthermore, experiments the AndroidControl dataset demonstrate the strong generalization capability of our method to unseen apps and instructions (tasks). Under limited training resources, IPL consistently outperforms naive DPO using only half of the data for one iterative training round (+4.5%), or one-fifth of the data for two iterative training rounds (+0.3%). Analytical experiments show instruction evolution simultaneously improves both the diversity and quality of reasoning.
Overall, our main contributions are summarized as follows:
We propose an iterative framework to construct a CoaT-tree, and utilize rule-based rewards with backward credit assignment form thinking-lever DPO pairs for reasoning optimization.
We introduce an instruction evolution strategy to mitigate overfitting during warm-up SFT, enhanc- ing the model's generalization and UI understanding.
We demonstrate the effectiveness of our method on three GUI-agent benchmarks: AITZ, AMEX, and AndroidControl. Furthermore, our approach even surpasses SoTA continual pretraining models.
2
RELATED WORK
2.1 MOBILE GUI AGENT
LLMs (Achiam et al. 2023 Xu et al.
2025a b Sun al. 2024 Liu al.
2025a
are increasingly
used as autonomous agents for mobile interaction (Li et al.
2024b Wen et al.
2023
Liu et al.) 2025b
Chen et al., 2025). With the rapid development of vision-language language models LMs), researchers build
mobile GUI agents Yang et al. 2023 Zheng et al. 2024 Qin et al. 2025 Team and multi-agent
frameworks (Ding 2024 Li et al.
[024c] Wang al. 2024a] Tan et al. 2025) based on closed-source
VLMs. Meanwhile, some researchers focus on training agents with stronger element grounding
Cheng et al. 2024; Wu et al. 20244,, page navigation (Niu et al. 2024 Lu et al. 2024 Gou et al..
2024 Wang et al. 2025). GUI understanding (You et al. 2024 Baechler et al. 2024) and task
planning capabilited lities (Zhagg et al. 2024c Nong et al. 2024] Xu et al.) 2024] Qinghong Lin et al.
2024 Dorka et al., (224) based on open- source VLMs. Our method organizes trajectory data into
multi-turns of dialogues based on the CoaT thinking pattern, preventing the agent becomes an action
model with limited capabilities.
2.2
REINFORCEMENT LEARNING
The algorithms applied in natural language processing align with human preferences include Direct Preference Optimization (DPO) (Rafallov et al., 2023). Identity Preference Optimization (IPO) (Azar al. 2024), Kahneman--vverky Optimization (KTO) (Ethayarajh et al. 2023). and
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2017). Specifically, ReFT (Luong al. 2024) adopts reinforcement learning as a iinettunig paradigm to improve performance on math problems. ReST-MCTS* (Zhang al. 2024a) focuses the process reward model is important. Xie, et al. (Xie et al. 2024) label the preference via MCTS based on feedback from self-evaluation. For mobile GUT agents, Digirl (Bai al., 2024) and Distrl (Wang et al., (222c) use online trajectory collection to improve the generalization of agents whose process is very slow. Reachagent (Wu al.
Proximal Policy Optimization (PPO) (Schulman et al.
the higher-quality step reward, where
2023) uses DPO training to compare the quality of multiple actions. TCPO (Jiao et |l.]|2222)2 (2025) also optimizes thoughts, but does not explicitly enforce thought-action consistency. TreePO (Li et al. 2025), TreeRL (Hou et al., 2025). and SPO (Guo et al.) 2025b) segment long sequences into many short segments, which leads to high computational cost and low data efficiency. In contrast, our method models thoughts with a fixed CoaT-tree and uses T-DPO to optimize the thinking process, while step values are computed directly from rule-based rewards, without unstable PRMs. This design yields more efficient sampling and training, especially in GUI-agent settings.
Stagel: Instruction Evolution
Stage2: CoaT Action-Level Sompling
Stage3: Preference Learning
Trajectories
Multi- Round
Contrastive Pairs Filter
Euctraction
Trammciion
Znrrrcctio EEE
Deoonphheg
Deper plln
Thoight
Thaghht
Theaht
Action
END
Astion Actiss Actios Action Actin
Figure 1: Overview of iterative preference learning framework. The left part presents the process of warm-up fine-tuning a general VLM to a mobile GUI domain agent with basic capabilities. The mid and right parts represent the iterative CoaT thinking-level sampling and T-DPO training process.
METHODOLOGY
In this section, we first introduce the multi-turn thinking process formulation ($ $.11 and explain method. As shown in Figure our method starts with instruction evolution strategy ($} 3.2 to enhance output diversity in warm-up SFT stage. Then, a CoaT-tree through iterative sampling ($ 3.3
is employed for each action. Every leaf node represents complete action and is scored using rule-based reward function. We then backpropagate the rewards along the tree to assign credit to intermediate reasoning steps. This process yields thinking-level contrastive pairs for DPO, which further improves the model's reasoning ability. The detailed process is presented in Algorithm
3.1 MULTI-TURN THINKING PROCESS FORMULATION
Each mobile GUT task contains trajectory T, several pages u, actions à, and an instruction I, which can be represented as:
T= { uo, âo, U1, âi, ... Uny Jn.
(I)
We formulate action à; in the Coa'T reasoning process multi-turn dialogue à; = [81, 82, 83, 84], where 8; represents description, action-thought, action-decision, and grounding, respectively. This thinking paradigm based on the thinking-decision- decision-grooneee grounding triplet, has been widely validated as
effective in previous GUI works (Shen et al. [024 Zhang et al. 2024b Qin et al. 2025 [Cenng et al. 2024). So the reasoning process can be formulated as:
$1 = DeecrrpptionPP..
(2)
82 Thought (P2, u4 I, 1,00," ;ūi-1,81)
(3)
3

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Published as a conference paper at ICLR 2026
P represents each round of dialogue input prompt, is the task instruction, is the current GUL, and à, is the step history action. Agents perform poorly when decoding the entire reasoning process in single step, which is because image modal u dominates the imput tokens, surpassing textual instructions I and action history âg and diverting their attention away from the textual details. During autoregressive training, the agent is unaware that producing a final answer conforming to the required format is indispensable throughout the reasoning process. Multi-turn thinking process effectively mitigates this problem, because additional dialogue steps guarantee a final answer is generated:
83 Action P3. "4, I, 81, 82)
(5) Previous work either performed RL in GUI-Agent directly on the trajectory without CoaT, missing the detailed thinking process of each action, forced the model to bear the heavy burden of outputting the entire reasoning process at once. In our method, when the reasoning process ends, the final 84 is recorded as 0n++1, step i moves one step forward on the trajectory T and its thinking step reward is calculated recursively based on final step 84- Dialogue- textual input helps balance cross-modal token proportions and steers the agent's attention toward the current reasoning step.
84 = Grounding( Pa, uy, I, 81, 82, 83)
3.2 INSTRUCTION EVOLUTION
As discussed in the previous section, the CoaT patterns in the mobile agent domain are typically fixed. As result, agents tend to overfit these static paradigms and struggle to generate diverse reasoning paths after the warm-up SFT training (as detailed in Sec (.4). To address this issue, we enhance the original training trajectories, denoted as T. by appending additional Q&A annotations to UI screenshots through an instruction evolution process, thereby creating new dataset Q with a broader range of instruction formats. Specifi- cally, shown in Figure 2 the evolution process consists of three levels:
Level UP!
Instruction Evolution
Seed ATTZ Annotations
"Esstruction:
"Dratall opp V'buckDuckGo Princcy
Breaderi
"Action: "clck on the \'Iestallt' button locoted et left and upper midsls part the verses.
Level care Bosic Capobilitiss
Q: Where can you find the opp nating the image?
A: "hh opp rating '46" is leonted et coordinntes |box storosstrrttte2210 219))09)2225) (ttp:/eeeesss/oooooeooo beoo__ooodd
Level It: Widget Panctiooality A Reletionshipe
What function 'Iestall the image?
The 'Tretal' button is used download and install DuckDuckS Frivary Browsor opp.
Level GUE Advanced FAQ
Level I: General GUT Q&A tasks. Grounding, Reference (Ref), and Page Descriptions are aimed at enhancing the agent's foundational capabilities. These tasks (Liu et al. [024] Yang et al., (024) are proven to be the core capabilities of GUI agents during the pre- -re-training.
Q: What clements ore included "Website Protection) scrcenshot?
A The 'Website Protection' screanchst inclutes on image of websihe and text indicating blocks hidden trackers.
Q: What is the loyout of the 'Tocls and Browsers' suttione?
Browsers' buttons positianed herizontally this aap'
We process three-stage instruction evolution and knowledge augmentation, enabling the agent produce more diverse outputs for corresponding tasks while effectively mitigating overfitting.
Figure 2:
Level II: Widget caption and relationships. De- scriptions of widget functions and the nested par- tition relationships between widgets. These tasks help agents understand the relationships between widgets, as previous work (Deng et al. (2044) has found that agents tend to click on the textview, even in scenarios where the textview and the button are separate.
Level III: GUI advanced FAQ. Inspired by Shen al. 2024)), we design an advanced FAQ that features more complex Q&A, including descriptions of the page's 's structural framework as well as expectations and predictions about navigation outcomes triggered by control interactions.
Warm-up Supervised Fine-tuning: To develop agents with standard thinking format and expand the reasoning space, we mix T and the instruction evolution data Q, then perform warm-up SFT on [P]
D D- (T = ((o)))(0 and action history) from T or the questions from Q, and e is the reasoning process from T or the answer from @ which is organized into multi-turn dialogues. To ensure output diversity, we select an earlier checkpoint with better potential correct space and diverse output to serve as the seed policy model. More details can be seen in Appendix B
e)0
1=z" where u represents the prior knowledge (instructions, screenshot
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3.3
ITERATIVE PREFERENCE LEARNING
After the warm-up SFT, the agent acquires basic GUI capabilities. We construct Coae--tree by iteratively sampling each reasoning step and then assign score the leaf nodes based on rule-based reward function. Using these scores, we generate thinking-level DPO pairs to optimize the agent's reasoning process.
Iterative Sampling & Rule-based Reward. We iteratively sample each reasoning step along the CoaT paradigm (Zhang et al. 2024b). The K. sampling results ($ step & can be expressed as:
K
8, = (1)
S0,
11-1) k=1
81
Naturally, the final step in CoaT (the leaf node in the sampling tree) expresses a reward compared with the ground truth action a", which is then propagated back other intra-nodes. The formula for the rule-based reward of leaf nodes as follows:
81 type ~ a²)
v(())=
Utype + scorematchy
(7)
0,
others
format + 1. (1 d(ar, v)) (Veype Uformat) d(z, v),
type(ax) CLICK
score
Uformat + (1 "typee 1foommt) F1,
type(ax) INPUT (8)
0,
others
The reward score us;)) ranges from 0 to I, with fully correct prediction receiving score of 1. We use Utpe and Vformar to indicate whether the predicted action type and output format match the ground truth. For click and input actions, we further evaluate their internal structure using smooth rewards based on spatial distance d(x, y) and text match Fi. The final reward is computed from the similarity between the prediction and the ground truth:
Click: distance-based score between the predicted and ground-truth coordinates, normal- ized to [0, 1}; smaller distances yield higher scores.
Input: The F1 score between the predicted and ground-truth strings; greater textual overlap yields higher scores.
The full reward is defined in Equation 7 and discussed further in Section C
Based on the structure of the CoaT-tree, we recursively compute the value each intermediate reasoning step. Specifically, the value of st-1 is computed as the average value of its K. sampled continuations at sy:
0($:-1) = c.
1 K
v(s
(K)
Here, KC denotes the number of sampled continuations for each reasoning step, and is discount factor. The parameter searching experiment for K. is described in detail in Section 4.4
Contrastive Data Filter. After obtaining the sampling tree and node values, we evaluate the quality of the trees and extract contrastive data. We can divided the sampling trees into three categories R {a, B, 7 based on their output quality, and the classification standards of a, B, 7 are follows:
(S() Vee E 8(0), = 1}
(10)
Σ (1, e)
(1800 Bik, UN 800, uu L, + 11
8
(11)
Σ c)
((00)) Vee E s()) ox + +1}
(12)
Σ e)))
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S()) and by refer to the instruction sampling tree and the k-th leaf nodes value of K sampled output. a is considered a perfect sampling tree, which can stably output correct thoughts and actions with in-domain trajectories, 3 represents potential correct trees that be used to construct contrastive data, and ry denotes sampling trees that require refinement. B + is considered a valid sampling space. In 9, actions with value of and many diverse action types possible extracted as positive samples. In 7, the final ground truth action a" is used as positive sample, but the intermediate steps of CoaT are not provided, and the pairs can be represented
Spairs = ( 1. 7 (k') 41 , 1111);;
(13)
u(3!*)) - u(s(" > 1/K)
Ppaiia
(a" s(())
$1, ......., 82-1)
(14)
Thinking-level Direct Preference Optimization. After CoaT thinking-level Iterative Sampling, several positive and negative example pairs are collected. During this stage, the agent policy undergoes updates through the above data-pairs, SFT loss, and CoaT-DPO loss (Rafailov et al. 2023). Suppose
the agent gets values to pair (+, -) at CoaT step t, which named s; and s, we have the agent performing a comparison for these pairs based on the same thoughts $1.t-1, which can be calculated
as:
To cs( (81:-11
LT-DPO --E - (1 JoT,
log (3 log
Tre (s ($1--1)
(15)
To(s; (1:0-1)
3 log
Fref (s, (1:111)
To further refine the agent's performance post-optimization, we employ the updated agent as the new base agent to continue collecting contrastive CoaT-action level pairs for additional T-DPO training. This iterative process is maintained until the agent reaches the performance bottleneck.
Algorithm 1: Iterative Coa' thinking-level sampling and DPO self-training.
Input: base VLM ær, advanced annotated model RsoTA, step-level trajectory data T, instruction evolution Q&A set Q. number of sampling K, golden action a" value function u, the sampled CoaT data D, number of iteeations .V.
1: for i to No do
Q" +- iestructionevoluttii ROOLA, T) instruction evolution by GPT-4o
3: Q + human_cvaluutionn/ Q²) human filter
4: end for
5: asa < Warm-up_SPP SFF(pp // fine-tune seed model
6: for
No do
1 [7]
8: D: + generate_ sarpttt_samlpin_houuuteeoee
Tr, K.) // CoaT Sampling vileaf + v(D;,a))// // match and calculate leaf values using Eq
9:
10:
Vinire + eacursive_calculttee yleaff / // recursive intra node values using Eq
D.
contrastive_dat data ( V)) // filter positive negative data using
Eq Eq
14p
12:
end for
13:
end DPO D+ D/ DPO self-training reference model
TS..
Output: us, Do. º
EXPERIMENTS
4.1 EXPERIMENTS SETUPS
Dataset. AITZ (Zhang al. 2024b) is high-quality trajectory set filtered and re-annotated from AITW (Rawles et al. 2023), containing four subsets. which also includes five types of actions
AMEX (Chai et al. 2024) uses the same apps and action space AITZ, but its task instructions
more complex and detailed, with an average trajectory length of 15+. AndroidControl (Li al. 2024a) includes OOD datasets, such app unseen and task unseen.

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Published as a conference paper at ICLR 2026
Table 1: Main results of AiTZ dataset. ZS, FT, PF, and IPL are short for zero-shot, fine-tuning, specific domain pre-training, and iterative preference learning, respectively. '-' represents that the agent or evaluation prompt is not open-sourced. Seed means the seed model for sampling and T-DPO training. R2 refers to the number of iterations during training.
Atomic
Model
Mode
CLICK
TYPE
Total
SCROLL
PRESS
STOP
type
match
type
match
type
match
CogAgent (Coal)
ZS
70.22
88.23
66.15
45.80
21.80
45.95
24.60
72.59
53.28
AUTO-GUI (CoeT)
FT
61.40
74.56
32.20
87.20
81.40
57.70
74.40
82.98
47.69
AriaUI-Mait MoB
FT
53.73
85.51
60.20
84.20
80.80
63,70
76.38
78.53
63.56
Seeclick 7B
PF
11.14
69.92
52.96
53.80
53.00
67.88
55.36
62.93
49.11
UGround 7B
PF
58.22
80.94
58.48
82,56
73.85
58.22
68.78
74.54
60.19
OS-Atlas-BB
PF
76.12
75.82
54.83
87,80
81.60
68.67
81.75
77.83
65.11
Ul-Tars-7B
PF
52.50
83.03
64.27
89.97
82.76
61.87
74.35
77.59
65.61
Falcon-U1-11
PF
84.70
69.10
Qwen2 7B (CoaT)
59.72
73.85
74.26
ATTZ-Seed
FT
42.83
82.48
53.16
82.56
75.29
56.65
61.82
73.14
55.40
MobilelPI.
IPL
51.08
91.73
71.45
88.20
83.40
51.69
78.17
81,90
69.15
Table 2: Main results on AMEX. Seed means the seed model for sampling.
Model
Training Data
Gmail
Booking
Music
SHEIN
News
CM
ToDo
Signal
Yelp
Overall
Se
AITW AITW+ +Exteraal
28.2
29.4
18.1
20.0
30.0
53.1
30.7
37.1
27.4
30.44
SphAgcat-7B
ArrW
32.1
45.9
46.1
35.1
48.3
61.1
55.9
43.3
42.9
45.63
SphAgent-7B
77.7
72.0
64.6
79.6
71.3
69.6
70.71
Moo
AMEX
68.5
58.9
54.7
62.5
83.3
66.9
UGround-7B
AMEX
72.7
63.7
67.7
63.7
80.1
67.6
SphAgcnt-7B
ArrW AMEX
62.4
68.1
76.3
71.9
68.6
67.3
77.6
66.0
64.1
69.14
OS-Atlas-7B
AMEX
61.1
73.5
77.9
61.6
75.2
66.4
71.0
75.9
72.0
70.33
AMEX
67.7
70.0
71.8
63.8
71.5
67.7
77.0
86.4
72.8
70.33
AMEX
58.0
70.1
76.6
63.8
79.4
66.8
67.8
80.2
76.6
69.01
Qwen2.Vie VL.-BB
CoaT
75.9
68.1
77.7
66.2
76.8
66.4
77.5
79.6
65.6
70.93
MobileIPL-7B
AMEX (Seed)
57.0
63.1
75.0
50,2
65.6
77.7
62.6
62.19
MobileIPL
77.3
80.0
68.4
85.3
71.3
73.5
82.1
71.8
74.29
Metrics. For evaluation, we use Step.Acc as metries, consistent with Auto-GU Zhang 2023), measures the agent's 's performance and uses Action Type to assess the degree of action type matching. This metric effectively evaluates the model's planning ability.
Baselines. Following prior work Wu et al. 2024) et al. 2025), we use Qwen2-VL- VLBB (Wang
al. 2024b) as the backbone of our model. We select CogAgent (Hong et al. 2024), AUTO-GUI, Shpagent, OS-Atlas, UGround, UI-Tars and FedMobile as baseline agents. GUI continuous pre-training agents can be further divided into two categories: (1) training the model as a GUI grounding agent, such as OS-Atlas-7B. (2) training the model as general GUI agent, such Ul-Tars. More details are provided in Appendix
4.2
MAIN RESULT
AITZ. As shown in Table I MobilelPL achieves SoTA performance on most metrics. The reason for the lower PRESS Acc. discussed in Section 4.4 and Appendix[H] Multiple rounds of T-DPO improve MobilelP1. by more than 10% (55.40% -> 69. 155) compared to the seed model MobilelPI. and Qwen2- VL-77B (60.36% -> 69.15%). Compared to continuous pre-training agents such as Falcon- UI. which is pre-trained on three million GUIs, MobileIPL still surpasses a performance difference of 0.05%. The amount of training data required by our method is substantially smaller than that used by these pre-training approaches.
AMEX. As shown in Table 2 MobileIPL surpasses the previous SOTA model. SphAgent- 7B. by 3.58%. It also outperforms the baseline model (Qwen2-vl+CoaTT by 3.36%. Additionally, MobileIPL surpasses OS-Atlas (+3.69%) and UI-Tars (+3.69%), both of which also use Qwen2-vl as the backbone. With the incorporation of CoaT, the baseline model Qwen2-vl shows an increase of 1. 1.92%.

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Published as a conference paper at ICLR 2026
demonstrating the effectiveness of CoaT patterns. In summary, these results confirm that MobilelPL delivers significant improvements over existing models long trajectory scenarios.
Table 3: High-level instruction experiment re- sults Annroid
Table 4: High-level instruction results on An- droidControl in-domain and OOD subsets.
Mode
Model
Grounding
Step.Ace
Mode
Model
IDD
app-UN
task-UN
Aria-UI-7B
43.2
10.2
PaLM 2S:tull)
65.5
58.7
59.7
FT
IntermVL-2-4u
72.7
66.7
FT
PaLM 2S(LoRA)
70.8
58.5
59.6
Qwen2-VT VL--B (SFT)
68.5
69.1
Qwen2-V1. (I.-7R(FFT)
69.1
61.4
64.1
OS-Atlas-7B
78.5
71.2
FedMobillegget
52.3
51.2
PF
Falcon-UI-7B
72.7
PF
SphAgent-7BB
69.4
57.1
62.9
UJ-Tars-7B5 7B
80.5
72.5
OS-Atlas-7B
71.2
60.7
66.2
RL
Qwen2-VL-7B(GRRO))
70.7
69.8
RL
Qwen2-VL-7B(GRPO)
70.2
68.1
69.7
Ours
MobilelPL
77.0
72.7
IPL
MobileIPL-7B
73.6
70.0
72.2
Annrold shown in Table MobileIPI. achieves SOTA performance Step-Acc (72.7%) reaching the SOTA model Falcon-Ul with fewer data. MobilelPL also outperforms continual pre- training agents in the GUI domain, such as OS-Atlas (+1.5%) and UI-Tars (+0.2%). Compared to VL(SFT), the baseline model Qwen2-VL(SFTT) MobileIPL not only improves Mobile Agent performance but
also enhances grounding by 8.5%. As shown in Table 4 MobileIPL. continues to achieve SOTA performance in unseen OOD settings, demonstrating strong generalization. In contrast, compared to performance in the IDD domain, the pre-trained model OS-Atlas shows a significant drop. MobileIPI. exhibits less performance degradation in out-of-domain settings. We also GRPO with DW. under the same computational resources, and found OOD performance similar to MobileIPL, because both are self-training. However, MobilcIPL still outperforms GRPO in all subsets.
4.3 ABLATION STUDY
To test the effectiveness of IPL and instruction evolution, conducted ablation experiments. First, removing IPL and using only SFT caused performance to drop from 35.4% to 60.4%, compared to the first round of MobileIPL, highlighting the crucial role that IPL plays. Next, removing instruction evolution led to a 2.5% drop in IPL performance in the first round. This occurs because, without evolution, the model generates fewer training samples (156,418- -> 113,239). And shown in Figure (a), without instruction evolution, the diversity of model outputs decreased, causing a drop in IPL performance. This further confirms that instruction evolution is crucial for improving IPL.
Table 5: Ablation study results on AITZ.
Model
Scroll
Click
Type
Press
Total
MobileIPL-RI
45.8
71.1
81.2
23.5
65.4
IPI.
46.9
59.4
78.6
55.4
60.4 (-5.0)
Evo (R1)
44.8
67.7
78.8
24.0
62.9 (-2.5)
IPL Negative (R1)
46.9
61.1
74.2
56.6
61.4 (-4.0)
IPL + Naive DPO (RI)
47.5
59.7
73.8
58.2
60.3 (-5.1)
1/2 training data (R1)
42.9
68.3
79.0
43.8
64.8 (-0.6)
4/5 training data (R2)
30.8
67.1
77.6
33.2
60.6 (-4.8)
Ablation Study. Additionally, we remove negative samples from IPI.-RI, training the model using only fully correct samples. This results in 4.0% performance drop, suggesting that negative samples help the model learn how to reason rather than merely memorize (SFT). Furthermore, training on the entire trajectory with naive DPO reduces performance from 65,4% 60.3%. Compared with SFT trained on CoaT tree positive data (-IPL Negative), naive DPO still 1.1 % lower, confirming the effectiveness of Coae-tree sampling and the thinking maakknng-proesss process optimization.
Low Resource. We also perform low-resource on AITZ, sampling 1/2 and 1/5 of the training data. As shown in table using only half of the data, the first round of IPI. training already outperformed
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Published as a conference paper at ICLR 2026
the best results achieved by the original CoAT-SFT (-IPL) and naive DPO training. Furthermore. when using only one- of the data, the second round of IPL training surpassed the performance of CoAT-SFT (-IPL), demonstrating the effectiveness of our method even in Low-resoures scenarios.
4.4 DISCUSSION AND ANALYSIS
Rollout Efficiency and Performance Trade-off. We compare MobilelPL with GRPO and the MCTS- style baseline SPO-Chain(Guus al. 20256). Although MobileIPL requires more rollout sampling than GRPO, it achieves better accuracy. Compared with SPO-Chain, MobileIPL uses only about half as many rollouts per sentence (~277 vs. ~54) while still obtaining a +1.12 accuracy gain. We also observe that SPO-Chain is sensitive to hyperparameters (e.g., temperature and cut-point probabilities). Its best performance is achieved with a higher temperature (1.4) enlarge the exploration space, but this also produces longer sentences and slows down sampling. Overall, MobilelPL offers better trade-off and is more practical for GUI settings.
Table 6: Comparison of RL methods in terms of accuracy and rollouts per sentence.
Model
Accuracy
Rollouts per sentence
MobileIPL (Ours)
69.15
27
SPO-Chain
68.03
55
GRPO
66.29
Reasoning Space Sampling, To evaluate the instruction evolution, we analyze the diversity of the sampling space for Random 1000 steps. the standard deviation of encoded embeddings, the dimensionallty-rrduedd distribution, and the distribution of S(() mentioned in Section 3.3 As shown in Figure 3 (a), the thoughts after instruction evolution exhibit a broader space than direct SFT. Additionally, the embedding standard deviation within each tree increases significantly compared to the original data (+ 0. 158)... The diversified outputs do not negatively impact the agent' 's reasoning process, while the proportion of action sampling that includes the correct answer improves from 72.7% to 77.9%. The bottom-right subplot reflects the distribution of output accuracy. Consistently Correct indicates that all samples for current step match the golden answer, while Consistently Error is the opposite. Both represents cases where some samples correct while others incorrect, which serves as an ideal source for constructing T-DPO pairs. Compared to 47% on the evolved data, the agent achieves 68.7% convergence the original data but exhibits strong polarization(4%) (4%). Three-stage instruction evolution significantly expands the sampling space (from 4% to 31 1)), proving that it simultancously improves both the diversity and quality of reasoning. More details are in Appendix F
Parameters Searching. We conducted ablation study on the impact of the sampling number (KC) per stage and iterative round number (R). As shown in the table 7 increasing the number of samples generally leads better model performance. However, since our framework adopts tree structure, increasing the sampling number from 3 to 4 causes the minimum number of tree nodes to grow significantly from 3 improvement is limited (less than 1%). Therefore, we adopt sampling number of 3 for the final
64. Despite this sharp increase, the performance
Table 7: Parameters Searching on AITZ for the first round. I. is the sampling number and R is the round of T-DPO learning.
Parameter
TYPE
CLICK
SCROLL
PRESS
STOP
Total
K 2
79.2
68.9
35.1
38.3
76.1
64.0
K 3
81.2
45.8
23.4
65.3 (+1.3)
K. 1
81.2
70.4
51.2
35.5
66.8
65.9 (+0.6)
R = 0
75.7
53.3
43.7
58.2
63.2
57.5
R = 1
77.5
71.1
43.3
23.5
67.0
61.2 (+3.7)
R = 2
80.5
71.1
47.0
31.1
67.6
64.1 (+2,9)
R
82.0
71.5
47.2
47.8
79.1
68.4 (+4.3)
74
82.6
71.5
51.1
51.7
78.2
69.2 (+0.8)

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Published as a conference paper at ICLR 2026
Set 1: Action Thogge without Exelntion
Set 2: Action Thoughr with Evaiution
Thought Enteddinns
0.851
Alae 835
Conpurica The Stadard Dollstiee and Aceuma
Tru Sumppling Biverdty Barriaaiis
Thsught Paskaion Though
Thought
IPL Scaling
Funition Thought
6.22
Presporien
Value
0.275
0:218
0.215
Jec
0.177
(s) Reasoning Space Sampling
IPL Pre-traiming
Figure 3: (a) Reasoning diversity before and after instruction evolution (left top) and the distribution of standard deviation and accuracy (left bottom). (b) Step.Ace changes for action types in AITZ across IPL. iterations (right top). (c) The performance of UI-Tars-BB and Owen2-VL-7BB on AITZ as the seed model with 4-round IPL training (right bottom).
experiments. Regarding the number of rounds, we observe that both IPL performance and the size of the self-training dataset converge after several iterations. We therefore select the convergence round as our default setting. Additional details computational cost are provided in Appendix G
IPL Scaling. Although overall Step.Acc increases across IPL. iterations, not all action types follow this trend. As shown in Figure (b), from the seed model to the first IPL round, PRESS accuracy drops sharply (58.22% + 23.49%) )),, whereas CLICK rises (53.26% - 12%). In the second round, however, PRESS accuracy rebounds. This stems from the severe underrepresentation of PRESS actions early on: the proportion of PRESS samples in the preference data grows from 1.6% (1 round) 10.9% (2 round) training progresses. With greater reasoning diversity and more PRESS-related examples, the model gradually learns PRESS behaviors and recovers accuracy in later rounds.
Iterative Preference Learning On GUI Continuous Pre-training Agent. As discussed in the previous experimental analysis, continuous pre-training in the GUI domain provides the agents with stronger base model. However, we still need explore the compatibility between post-training IPL, instruction evolution, and pre-training, As shown in Figure UI-Tars outperforms Qwen2- VL-7B in all training stages, demonstrating better performance during the instruction evolution phase (62.7% (62.7% > 55.4%). After four rounds of IPL, UI-Tars Step.Ace improves by 1.4% compared to MobilelPL (69.2%- -> 70.6%). More importantly, UI-Tars nearly converges after the first round of IPI,, significantly reducing the number of sampling and preference learning iterations, thereby keeping the computational cost of post-training within an acceptable range.
5 CONCLUSION
In this paper, we propose Mobile Iterative Preference Learning (MobileIPI.), a self-training GUI agent framework that incorporates instruction evolution, iterative sampling in the CoaT-ttree, and rule-based reward. We extensively evaluate MobileIPL on the AITZ, AMEX, and AndroidControl benchmarks, demonstrating its effectiveness. Furthermore, MobilelPL exhibits strong generalization capabilities on the OOD subsets of AndroidControl. Experiments show that instruction evolution increases output diversity, generates training data in IPL, and thereby improves IPL performance. Finally, Continuous Pre-training experiments confirm the mutual reinforcement between MobileIPL and pre-training, leading enhanced performance.
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6 ETHICS STATEMENT
We have rigorously refined our dataset to remove any elements that could compromise personal privacy, thereby guaranteeing the highest level of protection for individual data. Instruction evolution was completed by AI SoTA close-sourced VLM, to whom we paid the necessary compensation to ensure that the training data was not leaked. The human evaluation of our work was carried out through meticulously randomized selection of IT professionals. This process ensured gender- balanced and educationally diverse panel, reflecting a wide spectrum of perspectives and expertise.
7 REPRODUCIBILITY STATEMENT
All models and datasets used in this paper are open-source. The full experimental setup is detailed in Unless noted, all experiments use the same settings. We describe compute resources in Overall, these practices make our results reproducible.
Appendix D
Appendix G
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Weikai Xu, Zhizheng Jiang, Yuxuan Liu, Pengzhi Gao, Wei Liu, Jian Luan, Yuanchun Li, Yunxin Liu, Bin Wang, and Bo An. Mobile-bench--v2. A more realistic and comprehensive benchmark for vlm-based mobile agents. arXiv preprins a7X1v:2505.11811 2025b.
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A COAT THINKING PROCESS
The table 8 summarizes the CoT paradigms (inputs and outputs) used in prior related works. CoaT paradigm for fine-tuning agents: The effectiveness of the approach in AITZ stems from the inclusion of extra screen descriptions part of the input, along with joint output of Screen Context, Action Think, Action Target, and Action Result. In contrast, our experiments show that a stage-wise multi-turn dialogue output leads to better performance. In this setup. the model focuses on a single sub-task at each stage, which not only improves clarity but also encourages a simplified and deliberate reasoning process. This insight aligns with UI-TARS, which only requires the model to generate a brief thought during inference. Small-scale agent framing: Even models with relatively small parameter sizes can benefit from task-decomposed downstream training. For instance, OS-ATLAS and Falcon-UI adopt a similar architecture using GPT-4o for textual description and OS-ATLAS A-ba as the grounding model. They fine-tune models separately on different downstream tasks, resulting in collection of OS-ATLAS pro models, each specialized for specific sub-task. Large- scale prompting-basee based frameworks: Larger models typically adopt a multi-agent framework to support Cty-style reasoning process. For example, AppAgent v2 and Mobile-Agent-v2 both utilize
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plan coti--action-io cestion-reflection architecture complete tasks. In our work, adopt stage-wise CoaT multi-turn dialogue format, where the model focuses on one sub-task at a time. This design enables us move away from the dependence extra screen description inputs, leveraging the description grounding structure proposed in OS-ATLAS to form the final structure of the MobilelPI. Coa'T paradigm.
in AITZ, while
Table 8: Comparison of methods and their corresponding CoT paradigms.
Method
Backbone
Input
Output (Col Paradigm)
Android in the Zoo
One fine tuned agent
Instraction image
Screen Context Action Think Action Target
screen description
-
Action Resalt
UI-TARS OS-ATLAS
Onc finc-tuned agent One fine-tuned agent large agent Multi-agent + prompe engimeering
Instraction image
Thought Action
Instraction + image
Action Description + Action Model
Mohile- Mehile-Agent-v2
Instraction image
Stage I: Plan - Stage 2: Action - Stage 3:
Rellection
MohilelPT.
One tueet agent
Instraction image
Stage Description Stuge Plan Stage 3:
Action - Stage 4: Grounding
B SELECTION OF SEED POLICY MODEL
In our preliminary experimental exploration, we discovered that for the seed policy model, better performance in the SFT phase does not necessarily translate to a higher upper bound in the subsequent IPL phase. This is because as training progresses, the model's output space becomes increasingly aligned with the training data, reducing its diversity in sampling. Consequently, for incorrect instances, the model tends to generate erroneous outputs regardless the sampling attempts. To address this, we propose a sampling-orient oriented selection method for the seed policy model, incorporating the following two evaluation metrics:
Sampling Accuracy(Aces), which requires the model to hit more correct actions in the sampled output space S.
Σl
(e) a ΣM
Aces
(16)
1801
Sampling Diversity(Divr) which requires the model to have more diverse and extensive sampling space. Standard deviation calculation of single sampled tree Deugo)
Deugt +
Σ StdDev (E(A(*) (1) k== 1.......
(17)
Among them, E($ (o) represents the representation the keth sample output of the řth step after the encoder. Calculation of the standard deviation of the set Diug:
N
1
Diva =
Deugtn
(18)
N
1=1
where N is the number of sampled trees in the set R.
C RULE-BASED REWARD DESIGN
Derivation Of The Value Function. Our value function incorporates hyperparameters inspired by ReFT and is also influenced by the sampling number used during IPL. We explain the rationale behind key parameter choices in our method, especially those in Eq. 13.. and Eq. 14):
Strong Reward: We follow the ReFT (Luong et al. 2024) score to define strong reward signals, assigning values of and 0, corresponding to fully correct and completely incorrect reasoning paths, respectively. In ReFT, a supervision signal of 0.1 encourages the model to produce a final answer following the predefined format. In approach, this signal is repurposed to reward action type
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Table 9: Text Fl vs. text-embedding similarity: ablation on reward design. Best results are in bold.
Reward Type
TYPE (type)
TYPE (acc)
TOTAL (type)
TOTAL (acc)
BERT F1
84.17
76.70
77.81
62.49
87.78
81.23
78.74
65.37
matching. Mcanwhile, an additional Uformat reward is introduced to encourage proper formatting of actions.
Weak Reward: For input action, the value linearly increases from format + Dtype up to the strong reward level, with Fformat acting as a threshold distinguish weak from type-correct reward. For grounding actions, values range between fformtt + Utype and 1, too. A value of fformat indicates minimal correctness (e-g., extractable coordinates), while indicates a closest match with the golden action, suitable for DPO pairing. Except 8 formet and Vtype serving as discrete supervision signals, all other value signals are maintained as continuous. 1/ in Eq. (13) arises naturally from our hierarchical training structure. For example, if one child is incorrect (e.g., value drops from to O), the average value for the parent node decreases by 0.33 when the sampling number 3. Thus, 1/ serves meaningful threshold distinguish positive negative examples in the CoAT tree.
Text F1 vs. Enneddind Similarity: We replaced the Fl-based reward with a BERT-based semantic reward and evaluated both variants. As shown in Table ? the F1 reward outperforms the BERT embedding reward across all metries, with the largest gain on TYPE ACTION (acc) (+4.53%) This aligns with the importance of exact keyword matching GUI input, indicating that F1 is better suited than semantic similarity for reward design in mobile UI input scenarios.
Thought Embeddings Density (2D Histogram!
Thought Embeddiings Density (2D Histogram)
150
2500
1200
100
10
2000
50
1000
400
550
-100
-20
50
100
-150
-100
-30
50
100
120
Figure 4: The heatmap at the left represents the sampling before instruction evolution, while the one the right represents the sampling after instruction evolution.
D EXPERIMENT SETUP
Models. Unlike AITZ, we do not compare the CoaT result with the expected page and decide whether to roll back because most actions in real-device scenarios cannot be rolled back without cost. Previous work conducted continual pretraining on Qwen2-VL--BB using GUT domain data, resulting in stronger base model. In our ablation study, we discuss the impact of continuous pretraining IPL. O
Setup. We conduct hyperparameter searches on AITZ to reproduce the baseline results and find that the optimal learning rate ranges from 3e-5 to 3e-6. Therefore, all baseline fine-tuning experiments adopt this setting. Before IPL, during the instruction evolution stage, apply LoRA fine-tuning with a LoRA rank 128. For IPL Stage 1, we use a learning rate between 1e-6 and 1c-7. In subsequent stages, we apply constant learning rate of 1e-7. The batch size is consistently 128. During
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fine-tuning (including baseline fine-tuning), enable ViT training, whereas in the IPL phase, experiment with freezing VIT. For AITZ training, we followed the Falcons' approach, utiliziig a maximum 1540x1540 resolution. For other experiments, optimize computational efficiency. The maximum context length is set to 32K for all experiments. The fine-tuning experiments are conducted for 2 epochs, while IPI. training is performed for 1 epoch. Since the large volume of Android control data, we sample 1/5 of the dataset for each IPL training iteration.
reduce the resolution to 1280x720 to
CoaTT Multi-turn Dialogue Prompts.
1. Page Description, Based on the mobile screenshot: Image URL, identify and describe the key elements visible on the screen, including any text, buttons, icons, input fields. or other interactive components.
2. CoaT Action Thought. Given the task: instruction, and considering the contextual details from the image alongside the full history of previous actions: action history, determine the most logical and effective next step. Focus on providing a clear, actionable, and goal- oriented response to advance the task.
3. CoaT Action Description. Task:
Determine the Most Appropriate Next Step. Based on the previous analysis and the objective, determine the most appropriate next step to achieve the goal. Choose from the following options: **click** **; Select button or specific Ul element by specifying it clearly (e.g. 'click xxx', where 'xxx' is the button name or identifier). **scroll**; Perform a scrolling action if the required element is not visible, specifying the direction (e.g., 'scroll up', 'scroll down'). =**yype*;; Inpul specific text into a sield or search bar. specifying the text clearly (e.g.. type "content"). **press**; **; Interact with device-level buttons such as Home, Back, or Enter, specifying the button (e.g.. "press Back") ". **stop**; Conclude the task, indicating that the objective has been achieved. Provide the chosen action in the specified format and ensure it aligns with the analysis and the visible UI elements.
4. Click Action Grounding. As discussed earlier, your task now to identify the precise screen region coordinates to tap for the action coat action. The coordinates must be integers and strictly within the range of 0 to 1000 for both axes. Please provide your response in the required format: <lbox_ startl>(topp (top_x, top_y).(boliin)
bottom_y)<lbxx___ <>lbox__eed>>>> Ensure your outpul adheres to these constraints and remains concise.
Instruction evolution Prompts.
1. Page Description Annotation. I will provide you with mobile page. Please describe the current page. Your description should include the content of the page and its general functionality. Please note that the descriptions you generate should be of moderate length. Your page description should match the actual image.
2. Action Thought Annotation. **QUERY*;; task, **ACTION HISTORY**; To proceed with the query, your past actions include: action history, **XEXT ACTION**; This is the next action you need to take; coat action, **TASK**; Given the screen and the above information, you have three tasks do. First, you have analyze what you have done. Second, you should analyze the screen for relevant details that might pertain to the given query. This includes checking for specific applications, icons, or buttons that are visible and any information or results that are currently displayed on the screen. Tip: If the screen does not have the information you need, you can scroll left or scroll up to try to get the information. Don't it answer this logic question by saying that because the provided **NEXT ACTION** is.... therefore, the next action is.. You need to think carefully on your own. You must answer the question with suitable lengths and the following format: 'Think: I have done..., Current screen is.... I need to... So the next action is Your final action should be the same the NEXT ACTION above.
3. Q&& Annotation. Your goal is to draw inspiration from the given images and image description information to create multiple new questions and answers. This new creation is closely related to the given image and information, but the answers involved should be directly derived from the given information, because UI positions and UI text are one-to-one correspondence. Specifically, you should construct the following three types of questions
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and answers, a total of 15: 1. the function of some elements in the image. 2. Grounding questions and answers (the coordinates and approximate location of the target in the image). 3. Partial detailed information questions and answers (the structural relationship between multiple elements, type, style, etc.). Please try to keep your questions and answers diverse and informative, and ignore the message in the device status bar. Here is the information related to the image: UI positions: fui positions], UI text: {ui text), coat screen desc: { (coat screen desc), Please provide the following information in JSON format with the key questions and answers, and Don't I add annotation parsing:
uu APP drower
Home Page of Mobile Phone
Left-slide APP drower
Preference of Armotated Actions When Searching for Apps
1400
1398
Scnl Up
Sced let
Scroll Left
Scrolll
800
600
548
400
M
ScolUp
Scall
Figure 5: The left figure shows example of unstable annotation preferences in AMEX, while the right figure presents the proportion this type of annotation.
E HUMAN ANNOTATION
We use GPT-4o for annotation, which is priced at 4 USD per 1M input tokens and 16 USD per IM output tokens. As shown in Appendix B (Instruction Evolution Prompts), each image has resolution of 1080 x 2440. The input prompt is approximately 2K tokens, and the output is around 0.5K tokens, resulting in a per-page annotation cost of 0.008 USD (input) + 0.004 USD (output)= 0.012 USD. Given that the Android in the Zoo dataset [1] contains around 18,000 pages, the total annotation cost is approximately 200 USD.
So Model Cost:
Human verification is used to identify cases where the model produces incorrect thoughts due to being forced to align with the golden action. As shown in following table, some initial annotations contain template-like phrases (e.g.. "Since", "annotation") and mention multiple possible actions. These mistakes often happen when the model tries to justify a given action even if it doesn't match the actual screen.
Human Verification:
The bolded text in following Table highlights common errors. To address this, we regenerated the prompts and applied automatic filtering based on specific tokens, which helps reduce the need for manual work.
Before Human Verification: (coat_action thinkk: "Reason for the Action: To find and compare prices, the user needs to locate similar products. -Swiping up scrolling down is logical step to explore further information or discover additional options, as apps like Etsy often display "similar items" or "recommended products" below the primary product listing. This action aligns with typical app design patterns where related items or comparison options are placed further down the page. 4. Logical Derivation of Action: Since the task specifies comparing prices of similar items, and the current screen does not explicitly list such items, the user must navigate downward to uncover more options. The instruction to "swipe up" (or scroll down) matches the action type ("action type": "scroll". "direction" "" "ddwn"", indicating an attempt to reveal additional content or sections. 5. Since the next action swiping down matches the annotation, the next action is swiping down.
After Human Verification: Think: have previously navigated "Back," " which indicates that may have returned to the previous screen listing, likely part of exploring options related the query
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Table 10: Data size and training time across iterative rounds.
Standard SFT
Round
Round
Round
Round 4
Round 5
Data Samples
63,158 2.8
156,418
34,010
28,491
17,027
17.780
Time (hours)
7.9
2.0
1.7
1.0
1.0
for comparing prices of men's 's athletic shoes. Current screen is displaying detailed information about product on the Etsy app. This includes item details such "Handmade item." "Made order." " and shipping information. There are also options to "Follow shop." "Message seller," and "Add to cart," but does not appear to directly show other comparable items prices for similar men's 's athletic shoes.I need to scroll further down on this screen to potentially uncover additional details, such as related products, a comparison section, or other useful information for the query. So the Next action Scroll down."
F ITERATIVE SAMPLING IN THE COAT-TREE
As shown in Figure 4 before instruction evolution, the distribution is highly concentrated, with only 8 points exceeding 1000 (including points above 1200). After instruction evolution, the distribution becomes more balanced, with 20 points exceeding 1000 (including 2 points above 1500).
Ins: Search for "weather like in Seoul" in google chrome, I pay special attention to today's 's weather
Ins: Open Chrome. Search Music event in New York. Select the first one. Record its location and time in Google Keep Notes. Add to Favorites.
Current Page Description:
Current Page Description:
mobile search interface where the user has entered "weather like Seoul" the search bar. Below the search bat. list suggested displayed, including vaniations like 'weather like in Korea' and "does it snow Scoul".
a Google search results page for "What's is on the menu at Chipotle?", It disploys the Chipotle Maxicon Grill menu, categorized into "Entrees * "Lifestyle Bowls," and clong prices, navigation options like "Overview, "L "Locatione, and 'Meru," cs well as "Full menu button of the bottom. The includes Google search ber, and system
GoLLLE Annotated Bids
Emall
Armotated
ppotee Mexican Grill
Current Action Thought:
current page,
icons
into the search box. The next
entered "weather like in Seoul'
Current Action Thought:
step is search for this content and check today's weather.
The task requires opening search for Music event New York, but the page need to neturn of the opp first.
nelated
So the next Action is:
main interface
Click "weather like sugpestion box in the upper middle part of the screen
So the next Action is:
aol-MMes Tide
Click on home button the upper left part of the screen
PRESS ENTTER
PRESS Back
An example from AITZ demonstrates that when the task and image are the same, multiple actions may navigate to the same page.
Figure 6:
Potential correct space ratio. The proportion of |a| + | represents the potential correct space on the training data, and the change of this metric can clearly express the agent's 's ability to repair and reason out the correct process based on the correct answer.
G COMPUTATIONAL COST OF IPL ACROSS ITERATIONS
As shown in Table 10 while the first IPL iteration incurs higher cost due to the larger volume of preference data, subsequent iterations are significantly lighter. The training time per round decreases rapidly, the model generates fewer low-quality samples and requires fewer updates. In fact, by the third iteration, the training time becomes comparable to or even lower than the initial supervised fine-tuning (SFT) stage.
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Instruction Install opp \'Spotify\"
Page Description (0. 325)
Page Description (0.7)
Page Description (1)
i acreenshot smortphone
screerdhet smartphone and sinple layout. At the top, the stotus bar shows the time 5-51 and verious
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home diep
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shows indicating network signel Wi-Fi connectionn, and bettier snatus Below the status bor, there o search her for quick web searches. The main part of
au0llondd vamous icons
icons inckcating in network strungth. balt any life, and more. Balowr the
and bettery stictal Becos the statual ber: there's a search bar for Google.
status bor there's search bar for main part
quick wes searches
screen featiuees dock with app icons Play Store, Youluube the dock, there one thires settings options: VHome ings, ( ("Widgers, ( and V Whollboperss 1 The novigetion bar ot the bettom includes baok button,
main part
the scneen shows c grid of app icons, including the Google, Gimal, Gengle Photos, Youruube olong of
the screen ows grid apo icons.
fimsl
and YouTube wugpetgg focus communication, mntmmmmetttt There dise three quick settings ophioss available:
congoiinng the Phone opp,
and
Messiages. Google
Google
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with ophions for Vi-lome wethings. 1t
VHoom dettinge, y 1 "Widgete. V and ("Wallppera papers, 1 which (ikely prov
("Widgets 1f and Walllpppprrs"" augpesting qucck access to these settings.
home button, and a recent appa butter for multitoking
quick accnes to customizah on opt ons
for the home screen
Ground Truth: on the Play Store opp locateed left and lower middle part of the screen
Action Thought (0. (.55
Action Thought (0.1)
Action Thought (1)
Action Thought (1)
The Pla Store opp alroody open, indicating that the next step is search for and install the Vidipet fy'll aps. Possible
The Play Store app already)
The Play Store ican is visible on
The Play Store ican visible
ozen, and the next log step
the cock, which is relevent to the given query of inttllling Spot-fy. Peosible octions
the dock, which is nelevent for the glven query to install Spotify. Pessible
To seanch for and install the
V Vipptifyll cnn. |P /temible actions
tap the Play Shore izon to spen and Play Store
on
actions one to top with the
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botton of scneen type
"Spotify'" proceeed
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to find and
installation processi""
Spotify.
install the Spotify
Con'T Action (1) ConT Action (0. !YoaT Action (0.1) CooT Action (0. 1) Coo' Action (5)
ConTT Action (1)
Con'T Action (1) CooT Action (t)
Click the Play Shore opp. loceted ot the left and lewer m port of the screen.
Click the
Click en the Play
Click the
Click the
Click the Play
Play Store icsn located ot the middie and lowen middle) port
app lower right.
SSSptiiyt
located the lower niddle left part of the sonean.
Stone spe
Play Shore
looated the lower left.
lecateed the lower left port of the scnsen.
Hay Share
lewer middle part
Stone opp lscoted
the left and
the screen,
Groundring (1)
Groundring (1) Groundning (0.1) Groundning (0.1) Groundning (1)
Groundring (1) Groundning (1)
Groundning (1)
(174,004)(194 624) (158 (43) (386 664)(406 584) (410, (40,666)(450000)) 20..38000881.0000157777722222 (165 615,6008,,85588 (15) 6237(1756433
Figure 7: A sampling tree from AITZ demonstrates how the value is calculated.
Therefore, the overall compute overhead of IPL remains moderate and manageable, especially considering its performance gains. Compared to SFT, IPL introduces only a modest increase in compute, but brings substantial improvements in reasoning and generalization.
H
CASE STUDY
Unstable annotation preferences. As shown in Figure 5| the left section illustrates two different annotation preferences when searching for an app from the Home Page: SCROLL UP and SCROLL LEFT, leading different destination pages. The right part shows the overall preference distribution when annotators need to find an app. In rare cases, the annotation involves clicking on Google Play Store perform a search. This phenomenon is quite common because, fundamentally, the task completion paths for a UI Agent are diverse. This is also the key difference between online evaluation and offline data evaluation. From this, we observe that RL training on data with unstable preferences performs worse than SFT (e.g., AITZ SCROLL). This is because the DPO pair training method inherently attempts to correct errors in sampled preferences. As a result, the agent oscillates between two decisions when encountering the same GUI and instruction, failing achieve consistent alignment.
Action Equivalence. Unlike Unstable Annotation Preferences, where different actions lead to different but equivalent pages, the issue here arises from annotators' random labeling habits in the training data, preventing the model from learning a consistent preference. Action Equivalence refers to the phenomenon where multiple actions on the same page can lead to the target page. However, since only one action is annotated correct, other valid actions are mistakenly treated as incorrect. As
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shown in Figure after entering search query, clicking suggested item in the recommendation bar, and pressing the Enter key on the keyboard produce the same effect. Similarly, when navigating back, clicking the on-screen back button and pressing the hardware back button yield the same outcome.
Thinking-Level Sampling. As shown in Figure unlike mathematical reasoning. the Coa' process may not exhibit clear logical or computational errors. For given action, a sampling CoaT data may produce hallucinations (Page Description) due insufficient detail in the page description or fabricated elements; generate repetitive thoughts (Action Thought) due to neglecting action history; describe the wrong relative position of the correct element (CoaT Action): or misgrounding an element (Grounding), which is then classified as negative sample. At the same time, outputs with more detailed and accurate descriptions, diversified thoughts, and different ways of describing the same widget are classified positive samples. Negative examples may be disadvantageous compared positive examples, for example, because the description of the page is not detailed enough or the positioning of the elements is not accurate enough. At the same time, the wrong process may also give the correct result, but this is very rare case. In this example, negative samples are generated due the following three reasons: (1) Rough page description: The page contains eight app icons, but the agent's 's description includes only four apps: Play Store, Gmail, Phone, and YouTube; (2) Hallucinated Thought: The agent is unclear about its current page location. In reality, it is on the Home page, but it mistakenly believes is in the Play Store (e.g., "The Play Store app is already "pen"). (3) Fabricated Position and Elements: The agent generates the action "Click on the "Spotify" app", even though there is no Spotify icon on the current page. This hallucination may stem from the instruction. Additionally, the Play Store icon should be located the lower left part of the screen, but the agent incorrectly describes it as being in the middle and lower middle part.
I USAGE OF LLM STATEMENT
This paper utilized an LLM to improve the clarity and fluency of the text.
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Reviews:
[{"review_id": "0sDVWIoe6l", "reviewer": "Reviewer_YpsF", "summary": "This work introduces MobileIPL, a self-training framework for mobile GUI agents that enhances the intermediate “thinking” or reasoning steps of vision-language models interacting with mobile interfaces. The key innovations are (1) instruction evolution — using a model like GPT‑4o to generate diverse Q&A pairs from real mobile UI screenshots to enrich training data and avoid reasoning collapse; (2) construction of a CoaT-tree (Chain of Action-Planning Thoughts) via iterative sampling of reasoning trajectories at the action level, scoring leaf nodes through rule-based rewards, and back-propagating values to generate thinking-level preference pairs (T-DPO) for training; and (3) iterative rounds of preference learning to improve the agent’s reasoning diversity and correctness without requiring heavy process-level annotations. Empirical results on GUI benchmarks (AITZ, AMEX, AndroidControl) show that MobileIPL outperforms strong baselines (like continual-pretraining agents) and generalizes better to out-of-domain mobile UI tasks.", "questions": "My primary concern, as noted in the weaknesses section, relates to the level of technical innovation in the proposed approach. I would welcome further clarification from the authors regarding the novel methodological contributions and how they advance beyond existing agent-based and RL-training frameworks.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The GUI agents are becoming more and more popular recently, which is a very interesting and highly practical direction.\n\nThe overall presentation and paper writing is very clear. The pipeline components, i.e., SFT data collection and preference training annotation and filtering, are all illustrated well, which are helpful in advancing open-source GUI agents development.\n\nThe results are comprehensive and convincing, especially demonstrating OOD performance.", "weaknesses": "From a novelty standpoint, the primary contributions of this work lie in building a comprehensive end-to-end pipeline, whereas most of the technical components appear to be adaptations of existing methods or relatively straightforward extensions, especially in light of the recent surge of research on agentic system design and RL-based training. I sincerely appreciate the considerable engineering effort invested in large-scale SFT data collection and RL system implementation—this is clearly valuable for the open-source community and not trivial to execute. Nevertheless, the limited methodological innovation remains the main reason I am unable to assign a higher score.", "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": "wGWLPpM4qe", "reviewer": "Reviewer_5WvC", "summary": "The paper proposes MobileIPL, a self-training framework for vision-language mobile GUI agents that enhances reasoning via Iterative Preference Learning. It introduces a CoaT-tree built through Monte Carlo sampling, uses rule-based rewards with backward credit assignment to form thinking-level DPO pairs, and mitigates overfitting via a three-stage instruction evolution strategy. Experiments on AITZ, AMEX, and AndroidControl show strong performance and out-of-domain generalization.", "questions": "Please see the weaknesses for the major concerns.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The CoaT-tree construction via iterative sampling enables fine-grained reasoning optimization without manual step annotations and seems novel.\n\n- Experimental results on AITZ, AMEX, and AndroidControl indicates that MobileIPL outperforms previous strong baselines with less data usage.", "weaknesses": "- A few recent related works are not well-discussed. For example, TCPO [1] proposes thought-centric preference optimization, which is similar to the Thinking-level DPO proposed in this work. TreePO [2], TreeRL [3], SPO [4] all introduces tree-structure rollout and value backpropagation, which is similar to the iterative sampling process of MobileIPL.\n\n-  The clarity is not clear. For example, (1) Section 3.3 is not a complete part. It introduces Iterative Preference Learning. However, this section ends with the construction of $\\\\beta _ \\\\mathrm{pairs}$ and $\\\\gamma _ \\\\mathrm{pairs}$, without the preference optimization process. Although the DPO process is mentioned in Algorithm 1, it is not clear enough for clarity. Also, it is not clearly demonstrated what $\\\\alpha$, $\\\\beta$, $\\\\gamma$ mean, are they sampling trees (Line 260) or numbers (Line 248) or qualities (Line 247)? (2) what is $d(x,y)$ in Line 223? It is not explained before or after. **Make sure all the notations or concepts are clearly explained before using them.**\n\n- The motivation behind Iterative Preference Learning is unclear. Specifically, if an outcome-based reward is accessible and the values of intermediate reasoning steps can be obtained via MCTS, why not directly apply GRPO or PPO with step-level or segment-level advantage estimation—as in SPO [4]? Such an approach would provide finer-grained reward signals than DPO and is also simpler. Could the authors please provide further justification or experimental results to clarify this design choice?\n\n[1] TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making. EMNLP 2025\n\n[2] TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling\n\n[3] TreeRL: LLM Reinforcement Learning with On-Policy Tree Search. ACL 2025\n\n[4] Segment Policy Optimization: Effective Segment-Level Credit Assignment in RL for Large Language Models. Neurips 2025", "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": "buXzcNEwXd", "reviewer": "Reviewer_6kud", "summary": "#### Main Content Summary\n\n* **Problem Statement**: VLM-based mobile agents rely on the **Chain of Action-Planning Thoughts (CoaT)** paradigm, but the **scarcity of diverse CoaT trajectories** severely restricts their generalization ability. Existing self-training solutions either overlook the correctness of **intermediate reasoning steps** or require **expensive process-level annotations** to construct Process Reward Models (PRMs).\n* **Proposed Solution (MobileIPL)**: The paper introduces **MobileIPL (Iterative Preference Learning)**. This method is designed to enhance the agent's thinking process by **iteratively learning preferences** to distinguish between high-quality and low-quality reasoning paths. This mechanism effectively optimizes the correctness of intermediate thoughts **without the high cost of manual process-level labeling**.\n* **Observed Failure Modes**: Analysis of negative samples reveals consistent reasoning errors, including **Rough page description**, **Hallucinated Thought** (e.g., believing the agent is on the Play Store when it is on the Home page), and **Fabricated Position and Elements** (e.g., generating an action for a non-existent element).", "questions": "### Open Research Questions for MobileIPL\n\n* **1. Generalization Beyond GUI and Abstract Action Spaces**\n    * **Question**: How can the Iterative Preference Learning (IPL) framework be successfully extended and validated in **more complex, less-structured embodied environments**, such such as physical robotics or 3D navigation, where actions are continuous and visual states are highly dynamic and less deterministic than in a GUI?\n\n* **2. Mitigating the Root Cause of Hallucination in the Thought Process**\n    * **Question**: Can MobileIPL's approach be integrated with **explicit visual grounding mechanisms** or **internal state tracking models** to not just penalize (via preference learning) but fundamentally **prevent** the generation of Hallucinated Thoughts and Fabricated Elements?\n\n* **3. Optimizing the Preference Learning Signal for True Causality**\n    * **Question**: How can the preference signal be refined to better distinguish between a **causally correct** but less detailed thought sequence and a **highly detailed** but ultimately incorrect or inefficient one, especially in cases where a flawed process yields a correct final result (the rare case mentioned in the paper)?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "#### Strengths (1-2 points)\n\n* **1. Cost-Effective and Scalable Process Improvement**\n    * **Detail**: MobileIPL successfully addresses the bottleneck of requiring **expensive process-level annotations** for building Process Reward Models (PRMs). By utilizing an Iterative Preference Learning approach, the method provides a scalable and cost-efficient mechanism to automatically guide and improve the quality of the agent's intermediate reasoning steps.\n\n* **2. Direct Focus on Intermediate Reasoning Quality**\n    * **Detail**: Unlike many self-training strategies that only optimize for the final task success rate, MobileIPL's core mechanism explicitly targets the **correctness, detail, and diversity of the \"thoughts\" (intermediate reasoning steps)**. This helps the agent build a more robust and trustworthy foundation for planning and execution.", "weaknesses": "#### Weaknesses (3-4 points)\n\n* **1. Failure to Address Root Causes of Hallucination**\n    * **Detail**: The paper highlights severe errors like **Hallucinated Thought** and **Fabricated Elements**. While MobileIPL attempts to suppress these errors via preference learning, it **does not fundamentally solve** the VLM's underlying limitations in **visual grounding** and accurate internal state tracking, leaving the model susceptible to imagining non-existent states or elements.\n\n* **2. High Domain Specificity and Limited Generality**\n    * **Detail**: The proposed method is highly specialized for **VLM-based mobile agents** operating within the structured environment of **GUI tasks**. The direct applicability of MobileIPL's preference learning strategy may be restricted outside of this specific domain, such as in continuous control, physical robotics, or less-structured web navigation.\n\n* **3. Risk of Error Accumulation in Iterative Training**\n    * **Detail**: As an iterative self-training technique, the approach carries an inherent risk of **compounding errors** or training instability. If the preference model in earlier iterations inaccurately favors flawed reasoning patterns, these errors could be amplified throughout the process, leading the final agent to converge on a suboptimal or unreliable policy.\n\n* **4. Constraint by the CoaT Architectural Paradigm**\n    * **Detail**: The entire framework is built upon enhancing the existing **Chain of Action-Planning Thoughts (CoaT)** structure. This reliance prevents the model from exploring or learning potentially more efficient **non-sequential, parallel, or highly flexible** reasoning processes that might be required for solving complex, multi-faceted tasks more optimally than a strict linear chain allows.", "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": "vp6ZtO7iYV", "reviewer": "Reviewer_Lxqd", "summary": "This paper introduces MobileIPL, a novel framework to enhance the reasoning abilities of VLM-based mobile agents for tasks involving mobile GUIs. Addressing limitations in existing approaches, such as overfitting from supervised fine-tuning and reliance on costly manual step-level reward annotation, the authors propose Iterative Preference Learning, which constructs diverse reasoning trajectories through Monte Carlo Tree Search and uses rule-based scoring for intermediate thoughts instead of process reward models. Feedback from scored leaf nodes is backpropagated to earlier reasoning steps, forming thinking-level preference pairs for Direct Preference Optimization to improve both final action selection and the reasoning process itself. To boost generalization and prevent overfitting, a three-stage instruction evolution strategy generates varied Q&A pairs grounded in real UI screenshots, enhancing agents’ contextual understanding.", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper introduces a creative method (MobileIPL) for improving reasoning in mobile GUI agents, leveraging an iterative sampling and learning process that avoids expensive and hard-to-scale process-level manual annotation. By combining Monte Carlo Tree Search (MCTS) with rule-based rewards and backward value propagation, the paper sidesteps unstable process reward models and manual step annotation, streamlining the learning process.\n- Unlike many prior works that optimize only for final task reward/accuracy, this method explicitly scores and optimizes at the level of intermediate \"thinking\" steps, fostering better structured and generalizable agent reasoning.\n- The approach is thoroughly evaluated on three widely recognized benchmarks, using both in-domain and out-of-domain tasks, and consistently outperforms several strong recent baselines and continual pretraining models. The analysis demonstrates the benefits of each introduced component (e.g., iterative rounds, instruction evolution), strengthening the validity of the methodological choices.", "weaknesses": "- The method entirely relies on rules (format, type, spatial distance, and F1 for input) to quantify step-level reward. These may not capture nuanced or partially correct reasoning paths, edge-case actions, or subtle mistakes that a learned process reward model or human judgements might identify. Is the rule-based approach robust to complex/ambiguous UI layouts or fuzzy matching of instructions? Even with more granular step-wise signals, rule-based reward systems are susceptible to reward hacking (the model may exploit rules to maximize reward without genuinely improving reasoning). \n- The iterative sampling and tree construction (MCTS-like CoaT-tree) can be computationally expensive, scaling poorly with both the number of sampled trajectories (K) and the depth of reasoning. Can the authors report the runtime and resource requirements, especially compared to simple SFT or even RL-based pipelines?\n- The process involves choices of sampling number K, discount factor c, DPO data filtering thresholds, etc. There is insufficient discussion of robustness to these parameters, or general guidance about their selection for new domains.\n- Although the instruction evolution is partly filtered by humans, it remains heavily reliant on LLM-generated QA pairs. Are these synthetic instructions always realistic and helpful? Does the method generalize if downstream tasks differ greatly from the synthetic questions generated?", "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)