MobileRL: Online Agentic Reinforcement Learning for Mobile GUI Agents
Authors: Yifan Xu
Xiao Liu
Xinghan Liu
Jiaqi Fu
Jiayu Huang
Hanchen Zhang
Bohao Jing
Shudan Zhang
Yuting Wang
Zhao wenyi
Yuxiao Dong
Venue: ICLR 2026 Poster (2026)
Keywords: Mobile Agent
Reinforcement Learning
Primary area: applications to robotics, autonomy, planning

Abstract: Building general-purpose graphical user interface (GUI) agents has become increasingly promising with the progress in vision language models. However, developing effective mobile GUI agents with reinforcement learning (RL) remains challenging due to the heavy-tailed distribution of task difficulty and the inefficiency of large-scale environment sampling. We present an online agentic reinforcement learning framework MOBILERL to enhance GUI agents in mobile environments. Its core component is the Difficulty-Adaptive GRPO (ADAGRPO) algorithm. In ADAGRPO, we design difficulty-adaptive positive replay and failure curriculum filtering to adapt the model to different task difficulties. We introduce the shortest-path reward adjustment strategy to reshape rewards concerning the task length in multi-turn agentic tasks. Those strategies jointly stabilize RL training, improve sample efficiency, and generate strong performance across diverse mobile apps and tasks. We apply MOBILERL to two open models (Qwen2.5-VL-7B-Instruct and GLM-4.1V-9B-Base). The resultant MOBILERL-9B model achieves state-of-the-art results in terms of success rates on both AndroidWorld (80.2%) and Android-Lab (53.6%). The MOBILERL is open-sourced at https://github.com/THUDM/MobileRL.

Published as a conference paper at ICLR 2026
MOBILERL: ONLINE AGENTIC REINFORCEMENT LEARNING FOR MOBILE GUI AGENTS
Yifan Xu Xiao Liu 1.2,, Xinghan Liu Jiaqi Fu' Hanchen Zhang" Bohao Jing",
Shudan Zhang", Yuting Wang", ,Wenyi Zhao?, Yuxiao Dong'
Tsinghua University Z.AI
ABSTRACT
Building cemeral-purpose graphical user interface (GUI) agents has become in- creasingly promising with the progress in vision language models. However, developing effective mobile GUI agents with reinforcement learning (RL) mains challenging due to the heavy-tailed distribution of task difficulty and the inefficiency of large-scale environment sampling. We present an online agen- tic reinforcement learning framework MOBILERL to enhance GUI agents in mobile environments. Its core component is the Diffiuult-AAAApii GRPO (ADAGRPO) algorithm. In ADAGRPO, we design difficulty-aapptive positive replay and failure curriculum filtering to adapt the model different task dif- ficulties. We introduce the shortest-path reward adjustment strategy to reshape rewards concerning the task length in multi-turn agentic tasks. Those strategies jointly stabilize RL training, improve sample efficiency, and generate strong per- formance across diverse mobile apps and tasks. We apply MOBILERL to two open models 1.ve2..5-VL-7B Instrruct and GLM-4. 1-9.--99-BaaBe The resultant MOBILERL-9B model achieves state-of-the both AndroidWorld (80.2%) and AndroidLat lab (53.6%). The MOBILERL is open-sourced at https::////. tit.... com/ THUDM/MODOOO beoobileR
results in terms of success rates
AndroidWerld
AndroidLab
AndroidWorld
50
$3.6
Trair
70
80
52.5
35.2
39.3
41.7
60
34.5
30
20
10
20
6L364.3
Oponti CUA
GL444344
2013--3
Figure 1: Left and Center: Task success rates AndroidWorld (Rawles et al. 2024 and Android- Lab (Xu et al., 2024y; hatched areas indicate gains from MOBILERL on top of the SFT model. Right: Trajectory-leve success rate curves AndroidWorld train and test sets during RL training.
INTRODUCTION
GUI agents-powered by vision language models have enabled zero-shot interaction with web
pages and mobile interfaces (Hong et al. 2023 OpenAl] Bai et al. [025 Liu al. 2024). To improve
them, significant efforts have focused on supervised tine-tuning or offline imitation learning over static
expert demonstrations (Rawles et al.) 22033 Xu et al.) 20024] Bai al. 2024 Lu et 2025). However, these methods suffer from limited behavior coverage and poor eтror recovery (Chang et al. 2022). Reinforcement learning (RL) with verifiable rewards presents promising alternative (DeepSeek-A) Al
et al. 2025 Hou et al. 2025). ). Yet, existing datasets with single-step expert labels (Qin et al. 2025 Luo et al. 2025) are insufficient for training or evaluating policies on agentic tasks (i.c., planning and reasoning over multi-step action sequences). Although early progress has been made on online
"YX and XI. contributed equally. Work partially done while these authors interned 7.AT. Corresponding author: YD

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Figure 2: Example mobile tasks finished by our agent. Our agent automatically perform tasks according to human instructions in academic benchmarks and real-world applications.
learning for GUI agents (Bai et al. 2024 Dong et al. 2025 Dai et al. (025). efficiently scaling agentic RL interactive mobile simulators remains largely unexplored.
Specifically, it faces the following technical challenges: (i) Complex instruction following under sparse positive signals: base models usually struggle to reliably produce correct action commands for complex, GUI-specific instructions. Due to the heavy cost and latency of mobile emulation, correctly-execute rollouts are rare, resulting in data-inefficient early exploration. (ii) Large and unstable task difficulty spectrum: some tasks can succeed with multiple rollouts, while others are persistently unsolvable for the model. Naive sampling wastes computational budget and under-utilizee scarce but informative trajectories (Xu et al. 2024). (iii) Sampling bottlenecks in large-scall mobile environments: deploying and managing hundreds of concurrent mobile instances is resource-intensive and hard to reproduce across setups. Low sampling throughput further limits both the scale and efficiency of online agentic RL.
To address these challenges, we present an adaptive online agentic RL framework MOBILERL for advancing mobile GUI agents. MOBILERL consists of three components: reasoning-free supervised fine-tuning (SFT), reasoning SFT, and agentic RL. The two SFT stages provide warm-up for RL. Specifically, reasoning SFT enhances the handling of long and compositional instructions, reduces costly on- on-policy trials in mobile simulators, and enables the broad use of open or human-labeled datasets without relying on proprietary models.
To enable effective online agentic RI., we introduce Difficult-ADAAtiiv Group Relative Policy Optimization (ADAGRPO). Built upon group relative policy optimization (GRPO) (Shao et al.) 2024). its core idea is to adapt optimization to instance difficulty and explicitly reward solution efficiency. ADAGRPO designs three key strategies: (i) Difficulty-Adaptiv Positive Replay (AdaPR) maintains a curated buffer of challenging, high-quality trajectories and balances them with on on-policy samples (Mnih et al.] 2015; Zha et al.) 2019). In sparse-reward mobile environments, difficult successes are rare yet highly informative, emphasizing rare successful rollouts that would otherwise be under-utilized.
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Published as a conference paper at ICLR 2026
(ii) Failure Curriculum Filtering (FCF), as a simplified version of curriculum learning (Matiisen ett al. 2019 Narvekar et al., 2020). down-weights persistently unsolvable tasks using online difficulty statistics, reallocating computational budget toward feasible instances. Given the heavy-tail ttilled difficulty distribution observed in mobile agent benchmarks (Xu et al. 2024, Rawles et al. 2024). pruning hard dead-ends improves sample efficiency while retaining signal from recoverable failures. Different from "casy-to-hard" curricula that assume cheap exploration, FCF is resource-awar and aims avoid repeatedly sampling tasks that remain unsolved under expensive mobile interaction.
(iii) Shortest-Pare Pateestttttuutttet Reward Adjustment (SPA) reshapes the reward function based on completion length, assigning higher returns to shorter solutions. Length-sensitiee rewards counteract bias toward verbose and better align with user preferences in mobile interaction contexts. Unlike token-level length penalties used in text-only RLVR, SPA operates on multi-step GUT action trajectories and shapes only successful rollouts, avoiding incentives for premature termination (see Appendix [F). Unlike token- level length penalties used in text-only RLVR, SPA operates on multi-step GUI action trajectories and shapes only successful rollouts, avoiding incentives for premature termination.
We implement MOBILERL in a Verl-based framework (Sheng et al. 2024), which supports multi-task, multi-tum agentic RL training. Unlike previous Android simulato implementations TToyaaa et al. 2021, Rawles et al. 2024)-hhich which generally do not support true concurrent execution, our framework sustains high throughput that orchestrates hundreds of Dockerized Android virtual devices (AVDs) across multiple machines. This setup enables concurrent interaction with over 1,000 environments while preserving reproducibility. Since most open- benchmarks and simulators built upon the Android operating system (Toyama et al.) 2021 Rawles et al. (2024), this design ensures seamless compatibility and faithful reproduction of environment behaviors.
We train MOBILERL on Qwen2,5-VI--7B- Instrruct (Bai et al. 2025 and GLM-4. 1..M-4.IV-99. V-9B-Base (Tea ct al. 2025), producing MobileRL-7B and MobileRL-9B. respectively. MobilcRL-9B lifts the success rates to 80.2% on ANDROIDWORLDY and 53.6% on ANDROIDLAB, significantly outperforming previous state-of-the-an results (64.2% and 41.2%, respectively). MobileRL-7B outperforms the much larger 72B- ---aaameee models UI-TARS-I 1 .5 (Qin et al. 2025) and UI-GENIE-AGEN (Xiao) jt al., 2025) (e.g.. + +16% on ANDROID OrLd), despite being substantially smaller in scale. Also, extensive ablation studies demonstrate the effectiveness in the design of ADAGRPO.
In summary, our contributions are as follows:
MOBILERL Framework & scalable sampling: We develop MOBILERL with a two-stage warm- up followed by online agentic RL for mobile GUI agents. We further establish distributed sampling implementation that coordinates hundreds of Dockerized Android virtual devices (AVDs), enabling reproducible large-scale training on Android benchmarks.
ADAGRPO Algorithm: We introduce Difficult--Aapptiy Group Relative Policy Optimization (ADAGRPO), which extends GRPO with (i) AdaPR for replaying challenging successful trajecto- ries, (ii) FCF for down-weighting persistently unsolved tasks, and (iii) SPA for length-sensitive reward shaping, thereby accounting for instance difficulty and solution efficiency.
Empirical Results: Training on Owen2.5-VIL-7B- Instrrcct and GL.-4.1 1-4.1V-9B--Bse yields MOBILERL-7B and MOBILERLL-9.. MOBILERL-95 reaches 80.2% on ANDROIDWORLD and 53.6% on ANDROIDLAB, surpassing previous bests (64.2% 41.2%)
2 MOBILERL
We study mobile GUI agents and introduce the MOBILERL framework, as shown in Figure 3 which aims to address three key challenges interactive mobile environments: (i) following complex instructions under sparse and delayed rewards; (ii) handling heavy-tailed and unstable task difficulty distribution; and (iii) overcoming large-scale sampling bottlenecks in mobile emulators.
Given a natural-language instruction (e.g., "open the calendar and add an event for tomorrow at 3 pm""), the agent autonomously performs closed-looy interactions with the mobile device. First, perceives the current screen, grounds UI elements, and executes sequence of actions without human intervention. The feedback is sparse and it can be observed only upon successful task completion, at which point the interaction terminates a predefined horizon is reached. The goal is learn policy
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DifiicuityAAappiiv GRPO (AdaGRPO)
Reasoning Warm-Up
SPA
Adv.
AdaPR Buffer
1.00
1.34
Open-source VLM
Insert Rollouts by
Trace
1.23
Mobile
0.67
0.58
Adv.
Trace
1.00
0.31
Reasoning-Free SFT
0.00
-0.96
Trace
0.20
0.00
-0.96
Madura sampling
Reasoning SFT
Actor
Filtering
Policy Update
Figure 3: Overview of MOBILERI. It consists of 1) reasoning warm-up with both reasoning- SFT and reasoning SFT and 2) online agentic RI. with ADAGRPO. In ADAGRPO, the warmed-up policy interacts with mobile environments to generate rollouts, which are scored by shortest-path reward adjustment (SPA). High-quality positive trajectories are stored in the AdaPR buffer, while low-performing rollouts are pruned via failure curriculum filtering.
that generates strong performance across applications and tasks, minimizes unnecessary interactions, and maximizes task success.
Problem Formulation. We model the mobile GUI agent finite-orrizon Markov Decision Process (MDP) (Littman 2009) M = AS, P.r, no). The state space contains all possible GUI states; concrete state at time t is screen together with the structured UT hierarchy parsed from XMI. metadata (Cf. Appendix C We also include the pooeel-gnnerated interaction history (i.e., previous thi t i on messages) in the state representation, while we do not include the full history of past screenshots/XML due context-length constraints. The action space A finite set of atomic GUI operations; an action at time t is ar E A, including primitives such as Tap, Swipe, Type, Long Press, Launch, Home, Back, Wait, and a terminating action Finish. P(s++1 st, ae) represents the stochastic transition mechanism of the Android OS and installed applications, capturing the uncertainty over possible next states. We consider a finite horizon H N. The initial condition is drawn from joint distribution μl over snttruction-state pairs, i.e., () M4n- Here, c is the natural-language task instruction provided once at the beginning of the episode (t=0). At each timestep é 0, l,....., the agent observes only the current state st and samples action according to a policy Wo. i.e., a₂ ~ To 8t, c). The environment then transitions to 84+1 ~ P(. 8t, ac). An episode yields a trajectory т = ((s0, ao); (s1,,a),.... (ST, (r)) and terminates either when the agent intentionally selects Finish in success state or when the horizon is reached, with < H. The reward is assigned only after task completion, such that R(T) = r(st, ar) with r(s),aT) (0,1) indicating success (1) or failure (0). Consequently, learning maximizes the success probability: 0* = arg maxo E ()) Trumte R(T)].
S, which comprises screenshot of the device
2.1 THE MOBILERL FRAMEWORK
To build a powerful mobile use agent, we present the MOBILERL framework. It comprises three components: reasoning-free supervised fine-tuning (SFT) expert demonstration data, iterative warm-up stage by reasoning SFT, and agentic RL with a diffiuult--adaptive policy optimization strategy we developed in this work.
Reasoning-Free SFT. In agentic RL training, sampling in virtual-devine environments is usually inefficient; thus, starting online RI. directly from base model was found to be excessively time- consuming. Therefore, we perform SFT with expert demonstration data obtained by following the data collection protocol of (Xu et al. 2024), supplemented with the training split the publicly available AndroidControl dataset (Li et al. 2024). Note that this data is reasoning-fies
Reasoning SFT. To further construct a stronger reasoning policy initializer, we perform reasoning SFT via an iterative reasoning refinement strategy over the expert dataset. Manually collected
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expert demonstrations dataset for mobile use often contains only the final action sequence, omitting intermediate reasoning. Training solely on such "black-box" trajectories yields opaque policies. We leverage off-the-shelf instruction model to bootstrap reasoning augmented training set from raw demonstrations, yielding a structured and transparent policy initialization. Concretely, we iteratively build the reasoning instruction-tunn -uning pairs in three stages:
Bootstrap sampling: For each task z with expert answer a", the Instruct model M generates diverse candidate reasoning-action pairs (Ck: 0z). Whenever aş a", we retain (zz, Ca. a²) in DR. Supervised fine-tuning: Train initial reasoning policy my² on DR.
Iterative refinement: At iteration 4, म. proposes candidates; those matching a" scored by correctness. The best explanation c* is added to Dnew, and 7f41 is obtained by fine-tuning.
The resulting reasoning-orientee fine-tuning corpus is trained for two epochs to produce the reasoning warm-up model used for agentic RL training.
During agentic RL (multi-turn) training, we face the challenges of immediate reward assignment and sampling efficiency, which are discussed and addressed by building upon the group relative policy optimization (GRPO) (Shao et al. (0224).
Agentic RL.
Briefly, GRPO advances proximal policy optimization (Schulman et al. 2017) by replacing the learned value baseline with on-the-fly, group-relative baseline computed from set of trajectories for the same task. Given a shared initial condition (8o,c) ~ Ho. we sample group of G trajectories ........ . TG} by rolling out mmmii Let T; denote the number of steps for trajectory Tú, Si,t; di be the state and action at step and the crajectory-lever reward for T, be R(T:) {0, 1}. We define the group-relative trajectory-Iever advantage for any step (si,r, a.,z) on trajectory ти as A., R(a)- us where nnn and on are the mean and standard deviation of (R(T))) The GRPO loss can be written in empirical form as
GRRPO (0) = 1 Σ
G
Th min( (p.) A,,, (1) Art))
(1)
T
where (0) Tommpti (8.,)) is the token-wise importance sampling (IS) ratio. We add the KL
Fulay,lssaa)
loss B DKL(TI (To Te... to prevent the model from deviating too much from the prior distribution.
2.2 DITFCUULT--AAPIIVG GRPO
We develop Difficulty-Aaapite GRPO (ADAGRPO) with three strategies ssortestt-peeeeee path reward adjustment (SPA),
strteeies-shorresst positive replay (AdaPR), and failure curriculum filtering (FCF) to address the challenges faced in mobile agentic RL.
First, in multi-turn mobile agentic tasks where immediate rewards are absent within a single round, unlike single-turn settings (L.u et al., 2025 Team et al. (025) the reward allocation strategy must be redesigned. Beyond assigning a uniform terminal reward, we introduce Shortest- Pathh Reward Adjustment (SPA), which reshapes rewards with respect to task length. The goal of the adjustment to provide more informative learning signals, guiding the model toward accurate and efficient completion paths and facilitating the computation of trajectory-level advantages.
Second, a further challenge arises from the uniform sampling strategy employed in standard GRPO. In mobile
each sample carries significant computational cost-this approach results in poor sample efficiency, particularly due to the repeated inclusion of inherently unsolvable tasks. To mitigate this, adapt data collection and training based on instance difficulty through two mechanisms: Difficult--Aappiee (FCF), At the same time, restrict redundant successful trajectories to avoid unnecessary updates and promote training efficiency.
Positive Replay (AdaPR) and Failure Curriculum Filtering
2.2.1 SHORTEST-PE PATH REWARD ADJUSTMENT (SPA)
In mobile tasks, the environment returns a binary terminal reward E 1) (Xu et al. 2024 Rawles et al. 2024) indicating task success. Previous approaches typically broadcast this reward to every
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O...., that the per-step signal remains aligned with the sparse objective. However, assigning identical rewards to all successful rollouts biases training toward longer trajectories, since they contribute more gradient terms. To counteract this, we introduce SPA, which re-scales the reward for each trajectory T; as
timestep, i.e. R(s), a+) =1
RSPA (st, (s), r((r) (1 (1-
T; T Tunn
Tiin
min ITyl. a E (0,1). Tye TeTee
(2)
T
where 7; |Ti| is the length of trajectory Tr, and Tauce [Tg r(T;) 1} denotes the set of successfid trajectories for the current problem instance. Here Tmin the length of the shortest successful trajectory in Tauce. and a (0, 1] controls the penalty strength. In this formulation, shorter sequences are not automatically considered better; unsuccessful early terminations still receive a reward of 0. This adjustment encourages the policy to prefer shorter, successful paths without sacrificing the success rate. As in prior work, we broadcast RSPA (Se, 0e) to every timestep of the trajectory and use it to compute group-relative advantages.
2.2.2 DIFHCULTY- POSITIVE REPLAY (ADAPR)
In sparse-reward mobile environments, successful yet challenging rollouts are rare but highly informa- tive; effectively leveraging them enhances the learning signal and stabilizes policy updates. Therefore, inspired by the paradigm of experience replay (Mnih al.] 2013 Zha et al., 2019), we introduce difficulty-aaaptive positive replay (AdaPR) strategically to retain and reuse challenging. high-value trajectories while blending them with fresh on-policy samples. We introduce key components of AdaPR: buffer construction for high-quality trajectory selection and mixture sampling to balance replay and exploration.
Buffer Construction. At iteration the rollout set is T, = (-(1) 3.... (N)), collected under the current policy Te., We compute the trajectory- advantage and insert the top trajectories into the replay buffer B.
Mixture Sampling, Each policy update is performed on mini-batch of M trajectories obtained from the mixture distribution g(T) PT) + (1 7) Pon(T) where Pon the on-policy distribution Te, and ps is the empirical distribution over B. To keep the replay contribution under control, at most yM trajectories with the highest current advantage are drawn from B, preserving on-policy diversity. We provide quantitative analysis of the replay buffer effectiveness and the token-level PPO-style clip ratio in Appendix
2.2.3 FAILURE CURRICULUM FILTERING (FCF)
To avoid repeatedly sampling tasks that yield zero reward which wastes computation and hinders the collection of positive advantage data, we propose failure curriculum filtering. In FCF, any task producing all-zero rewards for two consecutive epochs enters a three-epoch cooldown, during which its sampling probability is reduced according to Wusk = exp(-f), where is the number of consecutive failure epochs; after this cooldown period, the task is permanently removed. This method can be regarded To avoid excessive hyperparameter tuning, we simplify it to target only the exclusion of the most difficult subset of data. Tasks with consistently high failure counts permanently removed from the sampling pool. For stability, failure histories from previous training are retained. Notably, FCF is applied only to the training sampling distribution; all evaluations are conducted on the full test set without removing any tasks. Notably, FCF is applied only to the training sampling distribution; all evaluations are conducted on the full test set without removing any tasks.
variant of curriculum sampling (Matisen et al. 2019 Narvekar al. 2020).
Summary. In summary, MOBILERL consists of reasoning- free SFT, reasoning SFT, and difficulty- adaptive RL for training mobile GUI agents. Reasoning- foundation from expert demonstrations, while reasoning SFT adds intermediate reasoning to improve instruction following and policy transparency. On top of this initialization, agentic RL with ADA- GRPO addresses the challenges of sparse terminal rewards, heavy-tailed task difficulty, and expensive sampling. Specifically, SPA reshapes terminal rewards for denser feedback, AdaPR strategically reuses challenging successful trajectories, and FCF filters out unnooovall tasks.
SFT helps build a strong action

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Published as a conference paper at ICLR 2026
Table Success rates (%) of proprictary and open-source models on AndroidWorld and AndroidLab for mobile GUI interaction tasks.
Models
#Params
AndroidWorld
AndroidL.ab
Proprietary Models
CPT-4o-2024-11-22 OpenAA
2023
34.5
31,2
Sauue-Sonnne--42000 thining (Anthropic
thinking
2023
41.0
40.6
UI-Tars- QQin et al.
2025)
64.2
38.3
AUTOGI.M-2024-10 Liu 2024
36.2
Open Models
Qwen2.5-
(Bai et al.
2025
7B
27.6
10.1
L.-M-4.VV9B-Thinn
(Tem et al. 2025
9B
41.7
24.6
UI-Tars-7B (Qin et al. 2025)
7B
33.0
32.6
V-Droid (Dai et al. 2025)
8B
59.5
38.3
Ul-Genie-Agent (Xiao et al.
2025
72B
41.2
MOBILERL (Ours)
w/ Qwwen..5-VL-77
7B
72.0
42.5
w/ V-LM-4.1V-9--BBB
9B
80.2
53.6
EXPERIMENTS
3.1 EXPERIMENTS SETTINGS
Datasets and Benchmarks. In the two-stage SFT process, we utilize data from human annotations and AndroidControl (Li et al. 2024), constructing 97. 97.9k and 23.6k training steps, respectively. During the RL stage, for the final MobileRL-9B setting. we employ an interactive training from AndroidWorld (Rawles et al. (024). consisting of 2,000 tasks with verifiable rewards. We evaluate on two interactive Mobile benchmarks: AndroidWorld, which includes 116 tasks across 20 apps, and AndroidLab, which comprises 138 tasks across 9 apps. Both benchmarks provide interactive environments. We further assess performance on the static dataset AndroidControl (Li et al. 2024)), which contains 8,444 test samples. Due space limitations, additional details the training and test datasets are provided in Appendix
Baselines. Our baselines encompass both closed- and open- source agents and models. The closed-source LLMs include GPT-40-2024-11122 (OpenAl 2023) and Slaude-Sonnet-422222
thinking (Anthropic) (023). and closed-source agents TarTars- 1.5 (Qin et al., 2025) and Auto- GLM (Luu al.] 2024). The open-source VLMs. including Qwen2.5- V VL-VLBBBBBB I-Intruct (Bai et al. 2025). GLM-4. V-9B- Thinking (Team et al. 2025), UI-Tars-7B (Qin et al. 2025). V-Droid (Dai
al. 2025 and UI-Genie-Agent (Xiao et al. 2025). are used (see Appendix E.3 for input modality and evaluation protocol details).
3.2 MAIN RESULTS
We evaluate MOBILERL by using Qwen2.5-VL-7B and GLM-4.IV-9B-BBr as backbones on online interactive benchmarks, AndroidWorld and AndroidLab. As shown in Table MOBILERL significantly outperforms previous results. With 0wen2.5-VV-777 as the backbone, MOBILERL achieves 72.0% AndroidWorld and 42.5% AndroidLab. outperforming previous state-of- the-art methods. By using GLM-4. V-9B as the backbone, the performance of MOBILERL further improves 80.2% AndroidWorld and 53.6% AndroidLab achieving the best results among the compared baselines under our evaluation protocol.
3.3 ABLATION STUDY
To evaluate the contributions of the MOBILERL framework and the components of the ADAGRPO algorithm, start with two base models and progressively apply Reasoning-Free SFT, Reasoning
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Models
AndroidWorld
AndroidLab
Owen2.5-VLL-7B-Insse (Bai et al.
2025
27.6
10.1
+ Reasoning- Free SFT
50.2+22.6
36,9+26.8
+ Reasoning SFT
56.8
38.7
+ ADAGRPO (MOBILERL-7B)
72.0+15.2
42.5+3.8
GLM-4.IV9BBBaS. (Team et al. 2025)
7.7
10.1
Reasoning- Free SFT
48. +,0.4
42.7+32.6
+ Reasoning SFT
66. 66.2+18.1
45.0+2.3
+ ADAGRPO (MOBILERI--9B)
80.2+14.0
53.6+8.6
(a) Improvements of task success rates by incrementally applying Reasoning-Free SFT, Reason- ing SFT, and ADAGRPO to the base models, respectively.
Training Trajectony-eeee Reward
0.90
Mobieell tell, Adalt
NasileRL AdaPR
mill w/b
Mob (dR) w/o SPA
Models
AndroidWorld
0.80
MOBILERL
71.1
0.75
w/o AdaPR
63.6
w/o SPA
69.1
0.65
W/o AdaPR&SPA
58.5
w/o FCF
64.8
0.55
w/o ADAGRPO
56.8
0.50
20
40
60
80
100
Training Steps
(b) Training trajectory-level rewards from the Android-
(c) Test performance the Android- World test set under different variants.
World environment with respect to training steps.
Figure 4: Ablation studies of the MOBILERL framework and its ADAGRPO algorithm. We use the Reasoning SFT model with Sive2.5-VVL77B-Inse backbone for the ablation of the ADAGRPO algorithm. All test set results are averaged over three runs to mitigate the impact of randomness.
SFT, and ADAGRPO. Then, using the Qwen2.5-VL-77 ----nn.--------B--B InIstruct model trained with Reasoning SFT as the initialization point and on the AndroidWorld training set, we conduct an analysis of the impact of each component of ADAGRPO-Adaa
SPA, and FCF
MOBILERL Ablation. We summarize stage-wise gains in success rate (SR) in Table 4a For Qwen2.5-VL-7B. the combined improvements of MOBILERL are +44.4% on AndroidWorld and +32.4% on AndroidLab: For GLM-4.IV-9.. the overall gains are +72.5% and +43.5%. Overall, Reasoning-Free Free SFT delivers a strong initial lift, and Reasoning SFT offers additional improvements. Building upon the strong foundation established by the preceding stages, the ADAGRPO stage further augments the final performance, achieving improvement exceeding 10% on the AndroidWorld dataset and over 5% on the AndroidLab test set.
ADAGRPO Ablation. The design of ADAGRPO covers SPA, AdaPR, and FCF. The ablations performed four settings: (i) MOBILERL w/o AdaPR (no replay), (ii) MOBILERL w/o SPA (no reward shaping), (iii) MOBILERL w/o AdaPR & SPA (neither), and (iv) MOBILERI. w/o FCF (uniform sampling: this variant keeps both SPA and AdaPR enabled).
We report on-policy trajectory reward curves during training (excluding replayed trajectories) in Figure 4b and the success rates on the AndroidWorld in Table 4c The curve reports the binary verifiable success reward (before applying the SPA length penalty), so it reflects whether each on-policy rollout succeeds on the sampled training task. The curve reports the binary verifiable success reward (before applying the SPA length penalty), so it reflects whether each on-policy rollout succeeds on the sampled training task. To avoid bias from the Androidl lab reward model, we use only AndroidWorld in this study. Each component of ADAGR PO contributes to improving the performance of MOBILERL. Specifically, we have the following observations:
FCF under constraints. With a 100-step budget (> 40 hours), FCF plays a key role in filtering. Removing it biases early sampling toward overly hard tasks, yielding many negatives and lower
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Table 2: Results on the AndroidControl (Li et al. 2024). Note that data from AndroidControl was not included during the RL stage.
0.90
Rewarrd
Motilent
0.85
MotileRi Sessoning Free SFT
0.50
0.75
AndrvidContral
AndroidCContrll-Igg
Model
0.70
Type
Grounding
SR
Type
Grounding
SR
0.05
Ul-Tars-7B
98.0
80.5
72.5
UI-Tars-728
98.1
90.8
91.3
85.2
81.5
74.7
0060
Ul-Genie-7B
94.3
83.5
82.9
74.5
035
UI Ul-Genie-722
98.3
95.4
94.8
84.9
86.3
77.0
0.50
MORI MOBILERI. -R
Training Stops
RL
96.7
93.7
91.9
87.0
73.6
72.8
w/ RI.
96.9
92.4
91.4
86.3
71.5
71.3
MOBILERL.99
Table 3: Effect of reasoning-free SFT evalu- ated on AndroidWorld.
wio RI.
98.6
95.8
95.9
89.8
78.1
77.5
w/ RL
97.9
94,6
94.3
87.0
74.5
73.9
reward ceiling. The w/o FCF curve keeps rising, suggesting stronger results with more efficient pipelines or simulators.
FCF only (w/o AdaPR & SPA). Training is initially stable but collapses after about 30 steps, indicating that AdaPR and SPA are necessary for stabilizing the training.
Effect of AdaPR. After about seven steps (once the replay buffer is populated), the gap between w/o AdaPR and the full MOBILERL method grows, showing the benefit of replay.
Effect of SPA. Noticeable gains SPA appear after roughly 60 steps, likely because the lack of step-wise control leads to overly long trajectories.
Is Reasoning-Free SFT Still Necessary? We apply supervised fine-tuning on the expert dataset without reasoning traces, which we term Reasoning- Free SFT. This raises key question: Is fine- tuning with expert data that lacks reasoning still beneficial?
We compare MOBILERL (Reasoning- -rree SFT Reasoning SFT + ADAGRPO) with MOBILERL without Reasoning-Free SFT in Figure 3 Interestingly, our experiments show that incorporating Reasoning-Free Free SFT consistently improves performance AndroidWorld. It suggests that without explicit reasoning traces, (Reasoning-Frre Free)) SFT contributes to enhancing final results for MOBILERL.
Performance Offline Dataset. As shown in Appendix D. Reasoning- Free and Reasoning SFT stages include training data from AndroidControl, which has been converted into MobileRL format. In Table 2 we present the results of MobileRL the AndroidControl test set. The SR score of MobileRL-99 w/o RL version surpasses all previous models, achieving the state of the art, while the RL version largely maintains the score.
Success Rates by Task Complexity. We divide the AndroidWorld test set by rounded-up Complex- ity (Rawles al. 2024): complexity level (510 steps), level 2 (11-20), level (21-30), and level 4+ (>30). We run eight test trials at temperature 1.0 and report pass @ 1/2/4/8 in Figure MOBILERL yields consistent gains all complexity levels. Notably, post-RL pass@1 I exceeds Rre-RL @ indicating substantial effectiveness. Consistent with AdaPR's 's design for heavy-tailed difficulty, larger gains occur on high-complexity tasks. For more fine-grained, beyond step-based complexity, see Appendix B.2
difficulty analysis
Impact of SPA on Step Efficiency. Although SPA has the smallest impact on overall accuracy in the ablation, its effect step efficiency is clear. As shown in Figure 6 partitioning tasks by complexity reveals that MOBILERL with SPA consistently completes tasks in fewer steps across all difficulty levels. Moreover, when compare cases where both models are correct (BC), both wrong (BW), and those where only one is correct, MOBILERL with SPA more frequently yields shorter trajectories in every group.

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Published as a conference paper at ICLR 2026
MobileRL AdaGRPO
MobileRL Complexily
MobileRL Win
Tie
MobileRL we SPA Win
Complesity
1.0
+3.3%
+7.%
+5.2%
1.0
n=116
nm:49
n=41
n=11
n=15
n=65
n=32
n=19
+3.5%
+5.9%
29.2%
0.6
0.6
41.4%
41.5%
45.5%
53.3%
52.6%
0.4
0.2
0.2
@11
82
44
@8
82
24
18.2%
1.0
Complexity
1.0
Complexity 2nd
43.19
55.1%
64.6%
9.4%
26.3%
0.6
0.6
-16.8
15.5%
14.6%
36.4%
26.7%
31.2%
21.1%
0.4
10.0% 185%
0.4
11.8
All
8.21%
C1
C2
C3
C4
6.2%%
BC
BWV
Others
91
22
04
@8
@1
@2
44
Figure 6: Win rate of MOBILERL vs. MOBILERL w/o SPA, where win means completing a task with fewer steps. n denotes the number of task templates per cate- gory, Categories: All (all templates); C1-C4 (complex- ity levels 1-4); BC/BW (both methods correct/wrong); Others (exactly one method correct).
Figure 5: P on AndroidWorld by task complexity (Rawles al. (2024) lev- els. Pass @ is the fraction of tasks solved within the top-k attempts.
RELATED WORK
Mobile GUI Agents. Recent work leverages powerful language models build agents that operate
real PCs and phones (Agashe et al.] 2025 Qin et al.) 2025 Lai et al.] 2025b), including Android
agents that perceive GUIs and act taps, swipes, and text (Toyama al. 202 1 Xu al.. 2024). To improve action prediction and learning, frameworks explore multimodal exploration (Yang et al.
2025a), verifier-driven control (Dai al. 2025 and small- code-based execution (Wen et al. 2025). Yet many systems still rely on offline RL or single-turn data: DigiRL. uses offline demonstrations (Bai al. 2024); U1-RI trains on single-step episodes (Lu et al. 2025); and UI-Tars applies DPO in an offline regime (Qin et al. 2025), leaving online, multi-turn RL for adaptive mobile agents unexplored. Studies emphasize realistic applications: AppAgent (Yang al. (202)) evaluates closed-soures source models real-wor wol-world apps, while A3 (Chai et al. (225)) offers realistic app suite with an autonomous evaluation protocol that reduces human effort.
2023). modular reasoning (Lai
Benchmarks for Mobile Agents. Benchmarking generally follows tracks. Static replay- based
(Li et al. 2024), Android the Wild (Rawles et al., 2023), Mo- bileAgentBench (Wang al., (2024a), and Mobile-Bench (Deng al.) (024) offer plenty of tasks and trajectories. These benchmarks fall short for real-world evaluation, as fixed trajectories and screens limit agents' ability handle uncertainty and exploration. Interactive emulator environ- ments AaattssAnndiiWooltl (Rawles et al. 2024), AndroidLab (Xu et al. (2024), and B-MOCA (Lee et al. 2024) span diverse apps and realistic tasks, yet remain challenging for current agents. Current mobile GUI benchmarks are limited: they lack asynchronous, parallel VM interaction for scalable training. To the best of our knowledge, all public mobile GUT benchmarks to date target the Android operating system.
5 CONCLUSION
In this work, we present MOBILERL, an agentic RL framework that advances mobile GUI agents. It achieves this by combining staged initialization with an adaptive reinforcement learning algorithm (ADAGRPO). Training begins with reasoning-free SFT on large-scale expert demonstrations, fol- lowed by a reasoning SFT stage that adds intermediate rationales and reduces cold-start exploration costs. Building on this, we introduce Difficult-ADA ptive GRPO (ADAGRPO), which enhances GRPO with shortest-pai path reward adjustment, adaptive positive replay, and failure curriculum filtering. These three strategies improve sample efficiency and guide policies toward more accurate and efficient task completion. Experiments on Android World and AndroidLab demonstrate that MOBILERL with open models significantly outperforms both open-source and proprietary baselines.
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ACKNOWLEDGMENT
This work was supported by NSFC 62495063 and 62276148, Z251100008425007, BNR2026RC01009.
STATEMENT
Ethics Statement This work involves human annotation for collecting human interaction data. Details of the data collection process and privacy protection measures are discussed in Appendix D.2 We ensure that no other components of work pose potential risks or harms.
Reproducibility Statement We provide detailed discussions of all training and evaluation datasets in Appendix D including their sources, preprocessing steps, deduplication methods, and evaluation protocols. Training algorithms, code frameworks, parameter settings, and implementation details are described in Appendix B
To facilitate reproducibility of the sampling framework and experimental results, we release an anony- mous repository at https:: /tttps://nnonymmus 4open. sewencee:/Mooil1
1ese--icrr--5555 The repository includes instructions for configuring the sampling framework, with most complex dependencies (e.g., sampling framework workers and AVDs) packaged into Docker images, ensuring that users can launch rollouts on Linux machines. The reproduction procedures for experimental results are also documented in the repository.
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Published as a conference paper at ICLR 2026
Han Xiao, Guozhi Wang. Yuxiang Chai, Zimu Lu, Weifeng Lin, Hao He, Lue Fan, Liuyang Bian, Rui Hu, Liang Liu, Shuai Ren, Yafei Wen, Xiaoxin Chen, Aojun Zhou, and Hongsheng Li. Ui-genie: A self self-improving approach for iteratively boosting mllm-based mobile gui agents, 2025. URL
https: /taps:///rra www.rwwww....r.eeoe..o.. abs/ .29966..196
Yifan Xu, Xiao Liu, Xueqiao Sun, Siyi Cheng. Hao Yu, Hanyu Lai, Shudan Zhang. Dan Zhang, Jie Tang, and Yuxiao Dong. Androidlab: Training and systematic benchmarking of android autonomous agents, 2024.
org/ /bsg/bbs////eee 2410.
Zhao Yang, Jiaxuan Liu, Yucheng Han, Xin Chen, Zebiao Huang, Bin Fu, and Gang Yu. Appagent: Multimodal agents as smartphone users. arXiv preprint urXiv:23/2.1377/ 2023.
Daochen Zha, Kwei-Heng Lai, Kaixiong Zhou, and Xia Hu. Experience replay optimization. arXiv preprint 1rX.v:1906000887,, 2019.
A
CASE STUDY
We present a case study from AndroidWorld evaluating three agents: the Reasoning- Free SFT agent, Reasoning SFT agent, and MOBILERL agent. We choose ingeentt,, which requires creating a recurring event ti- Iled "Review session for Budget Planning," starting on 2023-10-15 at 14:00, lasting 60 minutes, repeating weekly without end, and including the description: "We will understand business objectives. Remember confirm attendance."
All agents successfully configured the title and basic settings in the initial steps. Their performance diverged in subsequent steps. The Reasoning-Free SFT agent made timing errors (setting 16:00 instead of 15:00) and executed redundant checks, revealing weak task understanding (Figure(7). The Reasoning SFT agent skipped an adjustment step, yielding an incorrect event duration (Figure By contrast, the full MOBILERL agent completed the task accurately and efficiently, satisfying all requirements without redundant operations (Figure
B TRAINING DETAILS
B.1 HYPERPARAMETERS
In both the Reasoning Free SFT and Reasoning SFT stages, we conducted fine-tuning for two epochs. employing a cosine-decayed learning rate scheduled from 1 x 10-5 to x 10-6, The training was implemented using 1.1 SaaaaaFactooo with packing mode enabled accelerate training efficiency. All images were used at their full resolution without compression.
In the RL stage, we extend the GRPO framework from Verl with our customized functionalities. The main hyperparameters are summarized in Table
B.2 ACTION SPACE.
We design a series of actions based on AndroidLab (Xu et al. 2024), which supports concise set of actions for GUI interaction, as described Table
B.3 IMPLEMENT DETAILS
B.3.1 PRUNING NEGATIVE TRAJECTORIES
To stabilize training, we prune trajectories with the lowest advantages, reducing noisy samples in the replay buffer. High-advantage trajectories, if stored, expected to be sampled only once average. Thus, we maintain a maximum lowest-advantage trajectories.
trajectory ratio of 1:2 by randomly discarding the
We further study a pruning strategy that discards overly frequent erroneous trajectories from the reinforcement learning buffer before each update. As depicted in Figure 10 this filtering keeps the
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Published as a conference paper at ICLR 2026
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Step
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The Reasoning-Free SFT agent, where the expert data does not include the reasoning part, in the SimpleCall Lmmplealeendaraan eed.aaddeeeddettttaaaa... inggninntt task in AndroidWorid, Because of space constraints, we only present the most critical and notable steps for comparison.
Figure 7:
policy entropy consistently higher during training. By pruning trajectories whose advantages remain persistently negative, the agent avoids being driven by detrimental gradients; probability mass is instead spread over a broader action space, fostering exploration and delaying premature convergence. This pruning strategy ultimately leads to robust policy learning.
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Published as a conference paper at ICLR 2026
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Figure 8: Reasoning SFT agent in the Simp eCa MCCa endd RAddRepeat i Engntt task An- droldWorld. Because of space constraints, we only present the most critical and notable steps for comparison.
B.3.2 INFLUENCE OF IMAGE RESOLUTION
In our preliminary experiments, we applied uniform compression images (maximum pixels per image = 500,000) across all three stages in order to accelerate training, yielding the initial results. The standard practice, however, is to use uncompressed images. For experiments based on Wwen2.5-VL-Instrus
observed that this difference did not significantly affect performance while greatly accelerating training. In contrast, for experiments based on GLM-4.1V-9B. V-9B-Base, we found notable accuracy differences between compressed and uncompressed settings, as shown in Table 6 Therefore, we updated the reported results to those obtained with full-resolution images.
This change in accuracy is reasonable in the SFT stage, since the AndroidLab tasks contain more question-answering oriented tasks, where higher resolution images often make it easier to read screen content. In the RL stage, clearer images allow the agent explore more thoroughly, leading higher scores.
B.3.3 EFFECTIVENESS OF ADAPR BUFFER
AdaGRPO employs a mixture sampling strategy that incorporates recently successful trajectories stored in replay buffer. Since these trajectories originate from earlier policy snapshots, their
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Published as a conference paper at ICLR 2026
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ingEvent task in Android World. Because of space constraints, we only present the most critical and notable steps for comparison.
distribution may deviate from the current policy, raising the possibility that they fall outside the PPO-style clipping range used in the GRPO objective and therefore contribute marginally to the gradient. To ensure that replayed trajectories remain sufficiently close to the on-policy distribution, enforce strict entry and exit criteria for the AdaPR buffer and conduct targeted empirical analyses. These analyses demonstrate that stored trajectories stay near the current policy, maintain a low clip ratio, and provide effective gradient signal throughout training.
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Table Summary Main Hyperparameters
Component
Hyperparameter
Value
Data
Max Prompt Length
16384
Data
Max Response Length
4096
Data
Train Batch Size
256
Data
Validation Batch Size
256
Actor / Policy
Strategy (Parallelism)
FSDP
Actor / Policy
PPO Micro Batch Size/GPU
4
Actor / Policy
Learning Rate (LR)
le-6
Actor / Policy
Gradient Clipping
1.0
Actor / Policy
Clip Ratio
0.2
Actor / Policy
PPO Epochs
1
Rollout & Sampling
Sampling Temperature
1.0
Rollout & Sampling
Max New Tokens
4096
Rollout & Sampling
Number of Samples (n)
16
Rollout & Sampling
Max Turns
50
Rollout & Sampling
Max Pixels
5000000
Rollout & Sampling
Min Pixels
65536
Algorithm
KI. Loss Coefficient/ B
0.001
Algorithm
SPA/
1.0
Algorithm
ADAPR/ Replay Buffer Size
256
Algorithm
ADAPR/
1.0
Algorithm
ADAPR/
0.25
Table 5: Action Space for Mobile GUI Interaction. We utilize merging action space from Android-
Lab (Xu et al. 2024) and AndroidControl et al. 2024), which represents screen positions with bounding boxes aligned to XMI. data.
Action
Parameters
Description
Tap
element= (xmeent=(x1,ye [xl, y1, y2]
Tap at the rectangle defined by top-left (xl.yl) and bottom-right (x2.y2).
Type
text=
Enter the given string into the fo- cused input field.
Swipe
direetiionn//u///ow/////////tttd
Swipe in the given direction the specified distance. Optionally constrain the rectangle element. Press and hold on the given rectan- gle area.
dist=r(shott/mmddnn//onngg
eeement= (optional)
Long Press
e eeement=
Launch
app- (AppName)
Launch the named application. Press the system Back button.
Back
none
Home
none
Press the system Home button. Wait for three seconds. End the session with an optional message.
Wait
none
Finish
message=(string]
(optional)
Buffer entry and exit conditions During training, only the top-x trajectories (sorted by estimated advantage), with 23% in all experiments, eligible enter the AdaPR buffer. Since the success rate increases throughout training, this truncation ensures that nearly all stored trajectories correspond valid task completions (i.e., correct think-ac act sequences), preventing drift toward outdated or incorrect behavior.
Trajectories leave the buffer following strictly enforced rules:
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0.30
Mobileii
pruning
Entropy Loss
0.20
0.15
0.10
0.05
0.00
20
40
60
80
100
Figure 10: Effect of the Training Steps
strategy evaluated on Android
Table 6: Comparison of success rate between compressed and original images across different training stages and datasets.
Compressed Images
Original Images
Model
AndroidWorld
AndroidLab
AndroidWorke
AndroidLab
Reasoning-Fite Free SFT Reasoning SFT ADAGRPO (MobileRL-9B)
48.9
39.8
48.1
42.7
66.
40.3
66.2
45.0
75.8
46.8
80.2
53.6
1. Age-based eviction: settings) is immediately removed.
Any trajectory older than N = 1/k rounds (i.e., N = with our
2. Capacity- eviction: If the buffer exceeds the maximum capacity, the least advanta- geous trajectories are removed first (descending advantage order).
These constraints guarantee that all replayed trajectories originate from recent policy snapshots whose divergence from the current policy remains limited.
Empirical analysis of clipping behavior To quantify the degree to which replay-induced off- policy samples fall into the clipping range, we report the fraction of tokens for which the importance ratio exceeds the clipping threshold. Fi Before enabling AdaPR, the clip ratio fluctuates between 0.5%-1%. After enabling AdaPR, the ratio momentarily rises to approximately 6% early training as the replay buffer populates, and stabilizes at 3%%4% afterward. Considering that the batch mixture is approximately 1:1 between on-policy and replayed samples, this implies that over 90% of tokens originating from replay remain within the unclipped region and thus contribute fully to the gradient.
the token-level clip ratio throughout training.
On-policy vs. off-policy task coverage In Figure 12 we show the fraction of unique tasks covered by on-policy rollouts and off- off-policy (replayed) trajectories. After the initial steps, the ratio remains stably around 1:1 throughout training, demonstrating that the buffer effectively preserves task diversity and does not collapse to a narrow subset of tasks.
Effect trajectory-level accuracy Finally, Figure 13 directly compares the trajectory- success rate between training with vs. without replay. Across the entire training horizon, including replay, improves trajectory-level correctness by more than 5% absolute, further validating that replayed samples provide meaningful and beneficial gradient signal.
Summary Together, these findings show that replayed successful trajectories are only mildly off-policy due to strict buffer management, remain largely within clipping bounds, and contribute substantially to learning. This demonstrates that AdaGRPO maintains high sample efficiency while effectively exploiting historical successful rollouts.
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0.10
MobiicAL MoblicR, AD3FR
Clip
0.00
0.00
20
40
60
80
100
Training Steps
Token-level clip ratio during training. Enabling AdaPR temporarily increases the fraction of clipped tokens as the buffer warms up, but the ratio stabilizes at 3%-4%, indicating that over 90% of buffer-originating tokens remain inside the clipping region and contribute fully to the GRPO objective.
Figure 11:
On-Policy Off Policy
60
20
40
60
100
Training Step
Figure 12:
On-policy vs. off-policy task coverage. After warm-up, the replay buffer and on on-policy rollouts cover comparable number of unique tasks, preserving task diversity throughout training.
C XML PREPROCESSING FOR UI REPRESENTATION
The original XML from the Android accessibility service defines the layout and elements of the user interface, including all components on page. As result, it contains many nodes used solely for structural layout purposes, which do not provide useful semantic information. Moreover, scrollable pages often contain more nodes than are visible on the screen, leading to the inclusion of many off-screen nodes.
C. REMOVAL OFF-SCREEN NODES
We first determine whether retain off-screen nodes via the input parameter remain__ndes
remain nodes=True: remain_nodes-Trrue Off-screen nodes are preserved, e.g., when summarizing the full page content without requiring scrolling.
remain_nmoceee www.nodddssaseeeeee Off-screen nodes are removed to avoid interference during action simulation (e.g.. tapping, scrolling).
In the original XML., a node considered on-screen if its bounds property lies entirely within the screen dimensions [0. 0] to [Window H eight, W Window, Width] and is contained by its parent node. We check this condition recursively to identify on-screen nodes.
C.2 REMOVAL OF REDUNDANT NODES
Nodes that do not convey functional or semantic information are removed. node is considered functional if it satisfies at least one of the following:
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Training Tralectoryies Reverd
0.90
On poicy policy rewerd
0.85
polcy
0.80
0.75
0.65
0.60
0.55
0.50
20
40
60
80
100
Training Steps
Figure 13: Trajectory-level success rate with without replay. Incorporating replay yields more than a 5% absolute improvement in trajectory-Ievel accuracy across training, demonstrating that replayed trajectories provide meaningful and effective gradient signal.
Any of the boolean attributes is True: checkable, checked, clickable, f focusable, scrollable, long-clickable password, selected.
The text content-dest attribute is non-empty.
All nodes failing these criteria are considered redundant and are deleted.
C.3 ATTRIBUTE SIMPLIFICATION
Attribute descriptions in the original XML are verbose and token- expennexeeesive. We simplify them as follows:
Keep only True values for the boolean functional attributes listed above (omit False values).
Remove index, resource- id, and package (not useful for semantic understanding).
For class, retain only the last component (e.g., android. widget Fryout Framelayout).
Merge text and content-des attributes and display them separately.
Retain bounds in full, as it indicates the node's position on the page.
C.4
EXAMPLE TRANSFORMATION
Original node:
<node index="0" text="Audio Recorder"
resource-dd=ccmmddnneeee dimwwwerr.mmmm......mmmmm..
content-Gesc==* caackkaleaaacc checked="false"
clickable==falsee"
enabled="true" focusaalellassbelallalee focused="false
scrollable-"falsee"
long<clickable==faalee password="false" selected=" bounds="
[221, bounds=
(221, 1222) />
Simplified node:
TextView; ; Audio
Recorder; [221, 1095] [858,1222] 1222]
D DATA COLLECTION
In this section, we present the composition and construction methodology of the training and testing data used each stage, well their respective proportions.
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D.1 DATA DISTRIBUTION
We present the source distribution of the first two stages of SFT data in Table 7 In the RL stage, for the MobileRL-9B setting, we utilized 2,000 tasks from AndroidWorld, and MobileRL-7B setting, we utilized tasks form AndroidWorld and Androidl ab. Details of our data construction, deduplication, and reward acquisition methods are provided in a later section.
Table Data distribution and labels across the two SFT training stages by steps.
Android Control (Low)
Android Control (High)
Stage
Human Annotation
Total
Reasoning- Free SFT
21.3k 7.2k
14.3k
62.2k
97.9k
Reasoning SFT
4.1k
12.2k
23.6k
D.2 HUMAN ANNOTATION
Following the approach in Xu et al. (2024), we collected a portion of human-annotation data and additionally employed model-driven self-exploration on the online interactive environment, performing online rollouts and selecting the correct action sequences for training.
Privacy Protection In this work, the data collection process for model training inevitably involves capturing page screenshots and corresponding XML files. To address this, a privacy protection interface that does not retain data applied before storage, ensuring that all sensitive information is filtered out in advance and that only sanitized data is preserved. Building upon this foundation, the overall scope of privacy protection is defined cover both textual and visual information, with sensitive elements being systematically identified and anonymized. For textual data, the detection process primarily employs regular expression based rules recognize personal identifiers such as social media accounts, vehicle registration numbers, shipment tracking codes, order numbers, bank account details, email addresses, passport information, national identification numbers, and phone numbers. Once detected, these elements are replaced with standardized masking tokens that indicate the category of the information without revealing the actual content, thereby preserving semantic integrity while mitigating risks of exposure. For visual data, the privacy protection mechanism extends to the automatic detection of faces, QR codes, and text extracted from images through optical character recognition. Detected sensitive regions within images are then masked obfuscated, producing new visual outputs in which private content is effectively concealed. Taken together, these measures form a comprehensive privacy- drivacy-presevvation strategy that integrates rule-based textual analysis with multimodal image processing, ensuring robust safeguards against the leakage of personal or identifying information throughout the data collection and annotation pipeline.
D.3 ANDROIDLAB
D.3.1 DATA PROCESSING
The AndroidLab (Xu al. 2024) dataset consists of an online evaluation containing 138 problems, all of which are equipped with verifiable evaluation mechanisms. In our study, we directly adopt the SR (Success Rate) metric provided by AndroidLab the primary reward indicator. However, since AndroidLab does not include corresponding verifiable training set, we construct one by means of a model-driven expansion strategy. Specifically, we utilize the applications included in Androidl .ab to automatically generate candidate training tasks, which are subsequently subjected to manual verification to ensure both feasibility and non-overlap with the test set. Through this process, compile total of 1, training problems.
Since ground- truth test methods do not accompany these generated problems, it is not possible to rely on the same direct evaluation mechanism used for the test set. To address this issue, we train reward model that serves as an auxiliary evaluator of correctness. This reward model employed in two stages of our training pipeline: (i) during the Reasoning SFT stage, it functions filter to ensure the quality and validity of training data; and (ii) during the Reinforcement Learning stage, provides learnable reward signal that guides policy optimization.
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D.3.2 REWARD MODEL FOR ANDROIDLAB
Since the AndroidLab environment does provide rule-based rewards for training data, adopt a VLM-based reward model to supply reward signals during reinforcement learning. Specifically, we first execute all training and test tasks multiple times using condium-capabilig VLMs, including different versions of our models and proprietary VLMs, generating execution traces. Then, strong proprietary VL.Ms assign binary scores to each trace. For scoring, each model receives the full task screenshot sequence concatenated into single image, along with step-by-step action descriptions, and instructed produce a reasoning process before outputting score. For training-sel tasks (non-rule-based), use the majority vote among the three scores the label and retain the reasoning; for test-set tasks, we use the existing rule-based reward as the label and keep only the model scores and reasoning consistent with it. This process yields several thousand labeled samples, which to fine-tune GLM-4.J V-9B-Thinking Thinking as the base model. After training. we evaluate the reward model on curated of 1000 AndroidLab traces with verified labels, selecting the best-performing version (86% accuracy) for online reinforcement learning.
System Prompt for Trace Evaluation The following system prompt guides the VLM in determin- ing whether an agent has successfully completed a task.
You are an expert in determining whether a task has been successfully and completely completed. You will receive:
1. The task description.
2.
page states in XML format.
3. The agent's action descriptions.
4. A single image containing screenshots of all steps. Green rectangles and crosses mark tap regions and positions. Green arrows indicate swipe directions.
Red text shows typed input.
Action formats and meanings:
do (action="Tap", element= (1,, y2])
Tap on the specified screen region; green cross is the tap point.
do (act "action="Launch,, app="xxx")
Launch the specified app.
do (actioo="Iype", text="xxx")
Enter the specified text (shown in red).
do (actioo=="sipp", 2lement=(x1,y1,xx y2], direction="x", dist="x")
Swipe in the indicated direction; green arrow shows the swipe path.
do (act (actiion="ionn_Pres"
Long press on the specified region. do (action-"Back")
Navigate back to the previous screen. finish (message="xxx")
End the task with the given message.
Scoring rules:
the task is fully and correctly completed, output a score of 1; otherwise, 0.
Output format:
<analysis> Step 1 analysis: <Your analysis>
Step 2 analysis: <Your analysis>
Final step analysis: <Your analysis> </analysis>
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<ans> [Your score] </ans>
AndroidWorld
AndroidLab
0.80
Training Roward
0.70
0.60
0.20
40 60
80
100
40 60
80
1000
Step
Step
Figure 14: Trajectory-level rewards with 95% CIs on training sets, with the reasoning SFT version of Dwen-2.55VLL-Instruce as backbone model. showing consistent performance growth.
Analysis We compared the training curves of AndroidWorld and AndroidI (trained simultane- ously), as shown Figure [4 The training curve of AndroidWorld is smoother compared that of AndroidLab, which we attribute to the use of rule-based reward in AndroidWorlo whereas AndroidLab relies on reward model. However, the reward model cannot fully match the accuracy of the blsed reward. Therefore, we believe that evaluating whether broader range of tasks has been successfully completed without relying on rule-based rewards remains an important challenge for future work.
D.4
WORLD
D.4.1 DATA PROCESSING
The AndroidWorld dataset (Rawles et al. 2024) comprises 116 test problems, each equipped with verifiable reward signals. Since native action space differs from ours, we adapt the operation code to align with our action space. AndroidWorld further provides randomized initialization parameters, which enable the generation of large number of training tasks with verifiable rewards by replacing predefined substitutable components in the tasks (e.g., input content) and by varying the initial device states. To prevent overlap with the evaluation set, we remove all tasks whose specific content and initial states coincide with those in the test set. From the remaining pool, we curate 2,000 tasks to serve as the training set.
D.5 ANDROIDCONTROL
D.5.1 DATA PROCESSING
Each case in the AndroidControl Li et al. 2024) dataset includes episode_i idd, goal, screenshots, accessibility screenshot seeeeshot_heiihtttt and stte__inssttttssssstt impp_nnsstuutionns. The format of input information and output actions differs from that of our action space, and we provide transformation details in Table & Certain transformations are required for an evaluation to be performed. The dataset consists of 8,444 test samples, 690 validation samples, and 74,7 14 training samples.
screenshot_widthh actions,
Transformation from Accessibility Tree to XML We first transform the input information accessibility
to XML. The XML obtained from mobile pages contains nodes with the following attributes: index, resource-id, class, package, content-des checkable, checked, clickable, enabled, focusable, focused, scrollable. dong-clickable password, selected, and bounds. We extract and set these correspond- ing attributes from each node of the accessibility tree. This ensures that the converted XML. is
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Table The correspondence between the AndroidControl action space and MobileRL action space, as well as the judgment details for each action.
AC Action
MobileRL Action
Evaluation Details
click
Tap
Consider correct if the action type matches and the click point falls within the Tap bounding box.
long_press
Long Press
Consider correct if the action type matches and the long_press point falls within the Long Press bounding box.
scroll
Swipe
Consider correct the action type matches corresponds to the S Swipe direction.
scroll direction
open_app input_text
Launch
Consider correct if the action type matches and the Launch app matches. Consider correct if the action type matches and the Type text F1 score with the input_text text higher than Consider correct Consider correct if the action type matches. Consider correct if the action type matches.
Type
navigate home navigate back
Home
action type matches.
Back
wait
Wait
consistent with the XML obtained directly from mobile pages. We recursively process through steps follows:
Recursively process the three levels of access accessibility_tte
Set the corresponding XML attribute for each attribute of the node. window, and node.
Compress the converted XML into compressed_ __M format.
Here is a transformation example:
Accessibility node:
nodes {
bounds laaidd__i____ in_screen {
right: 1080
bottom: 2400
class_nane name: wingee...widget. FrameLayytt package_name, "com. zcho meeting"
-1
text_seelattioneendt -1 window_idd 1733
is_enabled: true
ss_visibll_too_ser: true actions id: 4
actions
id:
actions
id: 64
actions
id: 16908342
XML node:
<node index="O" text="" resource-id=""
class=" aanissiadddiid.wwi Wwligge..Framenni FrameLayout" package="cone zoho meet ing"
content-desc="* checkable=" checked=" false"
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App
Example instruction
Chrome
Open Chrome and switch incognito browsing mode.
Gmail
Compose a new email Gmail to a alice@cxamplc com with the sub- ject Meet ing Remindez and the body Please do not forget our meet ing at 10 AM tomorrow.
GoogleMaps
Search the nearest hospital Google Maps.
TikTok
Post the latest video from the gallery with the caption Weekend vibes in TikTok.
temu
Search hat temu and sort results by price from low to high.
Table 9: Representative examples from the out-of-domaij public-use apps evaluation suite. The complete set of 106 tasks is provided in the online test sheet in the supplementary material.
clickable="false" "tabled="true"' focusable="falses focused="false" sfrollable="fasee password="falser selected="false bounds=" [0, 01 [1080, 2400)
Transformation from Point to Bounding Box The grounding actions AndroidControl use points. Since grounding actions use bounding boxes rather than points, conversion of grounding coordinates required. We attempt to match all Ul boxes on a page. If the point falls within a UI box, the original click coordinates are converted the coordinates of that UI box.
Evaluation Details Our evaluation metric follows Lian et al. (2025), it provides open-source complete evaluation code, as shown in Table Notably, since our grounding actions use bounding boxes instead of points, we have modified the judgment logic for positioning operations. A grounding action considered correct if the point coordinates the original action fall within the UI box predicted by the model. For the AndroidControl Low evaluation, only conduct single-turn conversations with the agent. The provided information includes the instruction for the current step, the screenshot, and compressed XML data. For the AndroidControl High evaluation, maintain history with the agent and replace incorrect actions. The provided information includes the total task, the screenshot for the current step, and compressed XML data.
E ADDITIONAL EXPERIMENT
E OUT-OF-DOMAIN EVALUATION ON PUBLIC-USE ANDROID APPLICATIONS
To evaluate whether the improvements transfer beyond Andrroddoorld/Anddorddddddb ments, construct an oft-of-domain evaluation suite on public-use Android applications. This suite measures the extent to which trained policy generalizes to previously unseen apps and user goals that are outside the academic benchmarks.
environ-
Applications and task coverage. We include 21 widely used apps spanning system utilities, pro- ductivity, navigation, e-commerce, and social media: Settings; Booking, Expedia; Chrome; Contacts, Gmaïl, Google Drive, GoogleDocs, GoogleKeep. GoogleCalendar; GoogleMaps, GooglePlayStore; Joplin, OsmAnd; Quora, Reddit; TikTok, Twitter (X); temu, Broccoli. For each app, manually design 4-7 realistic langralee tasks, leading to total of 106 tasks (e.g., 9 for Chrome, 6 for Gmail, for TikTok, and 7 for temu).
Task examples. The tasks are formulated as natural-language instructions that reflect realistic user goals, such as connecting to specified WiFi network, composing and sending emails, performing web searches in incognito mode, interacting with social media and shopping apps. Table 9 shows representative examples.
Annotation protocol and scoring. For each task, the agent executes the instruction in the corre- sponding app. We record the full trajectory (screens, actions, and final state), and human annotators
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Model
(error)
0.5 (partial)
(success)
Overall
MobileRL-9B w/o ADAGRPO
44.76%
3.92%
51.32%
53.28%
MobileRL-9B
42.25%
3.67%
54.09%
55.92%
Table 10: Results on the out-of-domain public-use apps evaluation suite (106 tasks across 21 apps). We report the empirical proportions of trajectories receiving scores 0.5, and and the overall average score. The full MobileRL-9B model with ADAGRPO achieves a higher average score and larger fraction of fully successful trajectories, while reducing the fraction of complete failures.
assign a discrete score in 0.,, where indicates failure (the final state does not satisfy the instruction), 0.5 indicates partial success unacceptable redundant operations, and 1 indicates success without unacceptable redundancy. We report the average score over all 106 tasks, along with the empirical proportions of each score.
Models and results.
We evaluate two agents in this out-of-domain setting:
1. MobileRL-9B w/o ADAGRPO:
the agent trained with our standard pipeline but without the proposed difficulty-adaptive GRPO (ADAGRPO) reinforcement learning.
2. MobileRL-99: the full agent with reinforcement learning.
Table 10 summarizes the proportions of each score category and the overall average score.
On this out-of-domain suite, MobileRL-9B with ADAGRPO improves the overall average score from 53.28% to 55.92% (+2.7 points). Since these apps and tasks are not used during RL, the results indicate that the gains from ADAGRPO are not limited to the in-doman benchmarks.
E.2 DIFFICULTY-AWARE EVALUATION ON ANDROIDWORLDD
F.2.1 ANDROIDWORID ANNOTATIONS AND BASELINE DIFFICULTY
AndroidWorld provides public metadata for each evaluation task, including (i) human-annotated difficulty label diff ffi culty_rav (easy/med um/hard), (ii) an estimated optimal number of steps S (shortest-path length), and (iii) semantic tags describing interaction and reasoning types (c.g., mwwtimliiiie mww.mmllapppp.... app, complex ui wttps_uuuuddssssddieeee understanding, screen screennreedingg math_ cel_ccuniiin ing, memorization, informettoon transcription tata_entry).
All analyses below rely solely on this public metadata and deterministic rules over task templates; we do introduce additional human annotation.
E.2.2 CONSTRUCTION OF ADDITIONAL DIFFICULTY DIMENSIONS
To disentangle horizon length from semamtic/cognitive difficulty, we derive several complementary difficulty views based on AorloidWorld.
Semantic / reasoning-related features From the AndroidWorld tags and the task template text, we derive three binary features for each task. We set is wet.ttpralreeasonniiooo
if the low- ercased template contains temporal keywords (c.g., week, today, tomorrow, date, time); set is ss__ggregationn__ cecoooorrssssiinnn duration, distance); and we set i m__muutii goal=1 if it explicitly deseribes multi-stage goal (e.g., contains and then or multiple clauses with then).
it contains aggregation cues (e.g., how many, total, sum,
These features complement the AndroidWorld semantic tags by explicitly capturing different kinds of reasoning and coordination requirements: temporal reasoning, numerical aggregation, low-level system control, filesystem operations, and multi-stage goals.
and buckets (S1/S2/S3) We define mantic complexity score by combining (i) reasoning-relatee tags (math_ (math_counting.. memor memorization. intiiionnnniomommm www.omatioooeeeerrrrooooooooo traascriittattnnnniiiiiinn
Semantic complexity
R
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(ii) perceptioo/UULrrltteee tags P {complex: ui (ii)) the presence of multi_app and (iv) the three binary features above.
Let tags_set set be the set of Android tags for a task. We compute:
reason
[tags_set n Rl,
perception_see score = [tags_set n Pl multi_app_
app flag llmulti app tags_set].
The semantic complexity score is then
semantic_scoe score reason score + perception_ score multi_app__aae alp_flag
+ is Leteetepoa_reeaeeeeee
+ is sss_aagreggaiionnnassiiii
+ is si__mutti_goaal
Weights chosen give higher importance reasoning. multi-app composition, and multi-goal structure, while still accounting for perceptual/UI complexity and operational difficulty.
We sort tasks by semantic score and split them into three equal-sized buckets: S1 (lowest tertile), S2 (middle tertile), and S3 (highest tertile).
Combined difficulty score and buckets (D1/D2/D3) To obtain unified oie-dimensional notion of difficulty, combine horizon and semantic complexity. Let S be the optimal step count for task, and define
S Smin
norm_steps Smax Smin
the normalized step count in [0, 1] over all tasks. The combined difficulty score is
combined, cuubbinedddifiicultyyyoo ddifficulty_scorr 0.5 norm_steps 1.0 semantic_scomm _core.
Here semantic complexity receives higher weight (1.0) than horizon (0.5) to avoid simply treating long paths as intrinsically more difficult.
We sort tasks by combined_ sireeedddddfiittt_seeeeeeee and again split them into three equal-sized buckets: D1 (low), D2 (medium), and D3 (high) combined difficulty.
E.2.3 EVALUATION PROTOCOL AND PASS@K K COMPUTATION
For each AndroidWorld task, evaluate both MobileRL w/o AdaGRPO and MobileRL.
For given task with e successful rollouts out of n total, we compute pass@k @k for (1,2,4,8) using the standard unbiased estimator:
pass@k =
with & truncated at most For each difficulty category (e.g., easy vs. hard, HI vs. H3, SI vs. S3), we report the average pass @k across all tasks in that category for both the MobileRL w/o AdaGRPO and MobileRL models.
Here we provide some examples of tasks and their difficulty ratings in Table I1 us select long but simple tasks, well short tasks that require multiple types of understanding.
Such ratings allow
E.2.4 RESULTS DIFFICULTY
We summarize pass@k performance under all difficulty definitions. Full numerical results are provided in Tables 12 17
Below we highlight the main trends.
Across Aorlo's original difficulty labels (Table 12 ). the MobileRL model achieves consistent improvements over the MobileRL w/o AdaGRPO baseline at all difficulty levels, with particularly notable gains on medi um and hard tasks. This shows that AdaGRPO is beneficial not only for easy, well-structured tasks but also improves the agent's robustness on challenging interactions.
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Table 11: Examples of tasks and their human-annotated difficulty labels and structural properties. These annotations allow us to select long but simple tasks as well as short tasks that require multiple types of understanding.
Human-annotation Difficulty hard
Horizon Label
Semantie Label
Combined
Task
Label
What activities did I do [date] in the Open- Tracks app? Answer with the category only. If there multiple categories, format your swer in comma separated list.
short
S3
D3
Create playlist in Retro Music tirled "(playlist name)" "" with the following songs, in order: [names). Then export the playlist to the Downloads directory the device.
hard
medium
S2
D)3
Add the following recipes into the Braccoli app: [recipes].
medium
long
S1
D1
Table 12: Results grouped by AndroidWorld's original difficulty label difficulty.
Difficulty SFT@1
RL@1
SFT@2
RL@2
SFT@4
RL@4
SFT@8
RL@8
easy
0.773
0.867
0.795
0.878
0.811
0.889
0.823
0.908
medium
0.499
0.614
0.536
0.637
0.572
0.668
0.606
0.706
hard
0.208
0.247
0.230
0.289
0.248
0.313
0.260
0.316
Using proposed semantic complexity buckets (Table 13 AdaGRPO improves performance across all three levels and substantially narrows the gap between low- and high-complexity tasks (e.g., strong relative gains S3). This suggests that RL primarily strengthens reasoning, multi-step coordination, and Ul-understanding capabilities.
The combined difficulty buckets (Table 14), which integrate horizon and semantic factors, show similar pattern: while easy tasks (D1) benefit only slightly, the largest relative improvements occur in the highest combined difficulty bucket (D3). This indicates that AdaGRPO specifically boosts tasks requiring both long-horizon interaction and semantic reasoning.
Finally, the binary semantic features (Tables 15-17 further confirm this trend. AdaGRPO im- proves performance across all feature-defined splits (iemporal reasoning, aggregation, and multi-goal structure). In particular, multi-goal tasks reeiesentiteennnnggg some of the most structurally complex interactions- see substantially larger relative gains despite lower absolute performance.
Overall, across every difficulty definition, AdaGRPO consistently improves model performance, with the largest relative gains occurring in high-complexity settings. These results support our claim that AdaGRPO training meaningfully increases the agent's ability to handle difficult, long-horizon and reasoning-intensive AndroidWorld tasks.
E.3 INPUT MODALITY OF BASELINES
Some baselines operate under different state representations (e.g., screenshot-only vs. screen- shot+XMI.). When reporting baseline results, we follow a consistent principle: (i) prefer the best officially reported score when available; otherwise, (ii) evaluate using the model's recommended phone-use format; and otherwise (iii) evaluate within our pipeline and explicitly state the input format.
In our comparisons, models such as GPT-4o are evaluated with screenshot-only input, while some models without official phone-operation template are evaluated using default state representa- tion parrennsho++MLL) For GUI-specialized agents with prescribed templates (e.g., UI-Tars), we follow their official operation templates avoid artificially lowering performance.
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Table 13: Results grouped by semantic complexity bucket irmpaattc_buuccee (0t0kk///S////////) (S1/S2/S3).
Semantic
SFT@1
RL@
SFT@2
@L@2
SF
@L@4
SFT@8
RL RL@8
S1
0,677
0.758
0.704
0.773
0.729
0.799
0.749
0.840
S2
0.583
0.653
0.604
0.698
0.627
0.728
0.648
0.744
S3
0.487
0.615
0.518
0.618
0.538
0.618
0.552
0.618
Table 14: Results grouped by combined difficulty bucket (DI/D2/D3).
Combined
SFT@
RL@1
SFT@2
RL@2
SFT@4
RL@4
SFT@8
RL@8
D1
0.791
0.862
0.818
0.874
0.845
0.890
0.862
0.913
D2
0.523
0.605
0.543
0.630
0.560
0.656
0.580
0.697
D3
0.468
0.592
0.502
0.615
0.527
0.629
0.546
0.632
E.4 VISION-ONLY SETTING AND ROBUSTNESS TO MISSING XML
XML metadata may be incomplete for some real-world apps, and some elickable regions may be covered by the XML hierarchy. We therefore assess two settings on AndroidWorid: (i) evaluating the same trained MobileRL-999 checkpoint with screenshot-only input (i.e., removing XML at test time), and (ii) re-rumning the full pipeline in pure vision-only mode where state representations and action grounding are based on screenshots only.
Evaluating MobileRL-9B under the extreme XML-unavailable condition yields non-trivial score of 24. 4.6%% on Android despite the mismatch between the training and testing formats.
We also re-trained the full pipeline in the vision-only mode (using GLM-4.1 V-9B--B-BaBBa Basse as the backbone) and obtained the following results on AndroidWorld:
Stage
Screenshot-only
Screenshot+XML
Reasoning Free SFT
47.5
48.1
Reasoning SFT
62.6
66.2
AdaGRPO
79.3
80,2
These results suggest that, with the current model capacity and training recipe, vision-only training pipeline can approach the performance of screenshot+XXMi while we expect further improvements with stronger visual grounding.
E.5 IMPACT OF THE ANDROIDLAB REWARD MODEL..
To better understand the bias introduced by the Androidl .ab reward model, we first randomly sample 300 trajectories from different historical checkpoints and manually annotate their labels. The resulting confusion matrix TP 100, FP 34, TN 155, and FN 11, corresponding an overall accuracy of 0.85, precision of 0.74, recall of 0.91, and Fl score of 0.82. These statistics indicate that the dominant error type is false positives, where incorrect trajectories successful. Manual inspection suggests that the reward model sometimes over-relies on the textual think and act descriptions while overlooking whether the corresponding GUI operations have actually taken effect on the screen.
mistakenly classified as
To further quantify the impact of this 86%-aceuracy reward model on RL., we perform an additional experiment. We take the original Androidl .a test set (which consists entirely of tasks with verifiable rewards), randomly split it into two equal subsets, and treat one subset as a new training set and the other as new test set. We then re-run the RL algorithm under two conditions: (i) using the verifiable reward as the trajectory-Ievel reward during training, and (ii) using the AndroidL ab reward model as the trajectory-leve reward, while in both cases evaluating with the verifiable reward subset, as shown in Figure [5] Starting from the same initial verified success rate of 44.4, training with verifiable rewards improves the test verified reward to 63.7, whereas training with the reward
the test
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Table 15: Results grouped by temporal reasoning indicator is is__eenorra__eaaonieee
temporal rresscning
SFT@1
RL@@ 1
SFT@2
RI.@2
SFT@4
RL.@4
SFT@8
RI.@8
0
0.616
0.705
0.642
0.721
0.664
0.743
0.683
0.769
1
0.543
0.671
0.578
0.704
0.617
0.742
0.651
0.780
Table 16: Results grouped by aggregation/counting indicator isseegregationn
is is_ aggregation reasoning
SFT@1
RL@1
SFT@2
RL@2
SFT@4
RL@4
SFT@8
RL@8
0
0.618
0.701
0.644
0.717
0.665
0.738
0.684
0.765
0.463
0.661
0.500
0,700
0.543
0.739
0.580
0.778
model improves it to 59.1. This result indicates that, under our training configuration, the errors of the AndroidLab reward model do not get excessively amplified by the RL process.
F EXTENDED BACKGROUND ON PRIOR APPROACHES
Length-related penalties and controlling reasoning length. Recent RLVR work for LLMs increasingly treats chain-of-thouud thouhtt (CoT) length as an explicit optimization dimension. LASER proposes step-wise length-based reward shaping and adaptive variants (LASER-D/DE) that tune the effective length budget according to task difficulty, achieving better accuracy-efficiency trade-offs on math benchmarks (Liu et al., 2025). LACONIC instead augments the RL objective with a length cost applied only to tokens beyond a target budget, enforcing token limits during training while preserving task reward as the main signal (Anonymous) 2025). Thinking Fast and Right dynamically adjusts the weight of a length penalty based on current model accuracy, increasing the penalty when the model already accurate and relaxing it when accuracy drops, thereby stabilizing the trade-off between correctness and length (Su & Cardic 22025). All of these can be interpreted as practical instances of potential-based reward shaping (Ng al. 1999) specialized sequence length.
Our SPA reward extends length-based shaping ideas from text-only RLVR for LLMs to embodied GUI agents, but now operates on trajectory length rather than token count. Instead of relying on raw token length or judge-defined conciseness, SPA uses the empirically observed shortest successful trajectory length for each mobile task as a data-driven baseline and rescales the terminal reward of each successful rollout according to its relative path length. In doing so, SPA simultaneously constrains correct trajectories from degenerating into unnecessarily long, looping explorations, while avoiding the opposite failure mode of naively rewarding early termination that leads to incorrect outcomes: only successful trajectories are the best-known path for the same task. This yields arouu-relative. trajectory-leve shaping that is compatible with binary verifiable rewards GRPO, directly reflecting interaction efficiency in the mobile GUT environment.
and their rewards are modulated relative to
Curriculum learning. In classical reinforcement learning, curriculum learning studies how to order tasks training samples that agents acquire complex skills gradually; Narvekar al. (2020) provides general framework over task generation, ordering, and stopping criteria, and surveys stacher-student and self-paced methods Atari and continuous control domains. In the LLM era, curriculum ideas have been adapted to RL-based post-training: WebRI. implements self-evolving curriculum web agents by turning failure trajectories into new navigation tasks and feeding them back into online RL loop with learned reward model, so that the task distribution adapts as the agent improves (Qi et al.). Sun et al. (2025) further couples RLVR with curriculum like difficulty targeting via Difficulyy-argette Online Data Selection, which selects prompts whose current success rate is around 50% and combines this with rollout replay to focus GRPO-style training on "on-the-margie margin" problems.
In our setting, we adopt a variant of curriculum learning through failure-centric curriculum filtering. Rather than only up- -p-weighting medium-difficuly prompts, FCF explicitly tracks commands that
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Table 17: Results grouped by multi-goal indicator is_multi_
is multi -goal
SFT@1
RL@)
@F
RL @
SFT@4
RL@1
SFT@8
RL@8
0
0.625
0.707
0.652
0.723
0.674
0,739
0.691
0.762
0.042
0.317
0.075
0.407
0.120
0.484
0.158
0.500
Trajectory-leve Verifiable Reward
0.6
Veriflable
0.5
Reward model
0.4
0.1
0.0
15
Training Steps
Figure 15: AndroidLab RL training with verifiable vs. reward-model rewards. TraadddTrajcctoy-eeeee verifiable reward on the AndroidLab subset when training with (i) verifiable rewards and (ii) the AndroidLab reward model as the trajectory-level signal.
remain persistently unsolved in the mobile GUI environment and down-weight or temporarily cools them in the sampling distribution. This allows us, under strict interaction and compute budgets, concentrate RL updates subset of tasks that are more likely to yield improvement, thereby increasing training efficiency while still maintaining exposure to harder tasks over time.
Replay-based methods. Experience replay has long been a key mechanism to improve sample efficiency and stabilize learning in off- [chaul et al. (2016) introduce Prioritized Experience Replay (PER), in which transitions are sampled from a replay buffer with probabilities proportional their temporal-difference error, so that more "informative" experiences are reused more often; they show substantial gains in Deep Q-Networks on Atari compared to uniform replay. In large-scale RL for LLM-based device-control agents, DistRL employs an asynchronous distributed framework in which many workers interact with Android environments in parallel and send full trajectories to a centralized replay buffer; its A-RIDE algorithm uses Distributed Prioritized Experience Replay (DPER), assigning each trajectory priority that is weighted combination of its average absolute TD error, average importance-sampling ratio, and average policy entropy, so that highly off-policy, high-error, and exploratory trajectories are replayed more frequently, with priorities periodically recomputed under the latest policy (Wang et al. 20244b). In LLM post-training, Trajectory Balance with Asynchrony (TBA) decouples exploration and learning: swarm of searcher nodes continuously populates global replay buffer with off off-polic pelicylcc trajectories, while trainer node samples from this buffer based on reward or recency optimize trajectory-balant balance objective for LLMs (Bartoldson) et al. 2025).
Motivated by the high sampling cost and heavy-tailed difficulty of mobile GUI tasks, we introduce AdaPR as prioritized replay scheme tailored to our setting. Instead of generic replay keyed by TD-error or recency, we maintain a buffer of successful, high-advantage trajectories, and assign higher sampling probability to rare but informative successes while still mixing in fresh on-policy rollouts. In this way, AdaPR makes the most of expensive environment interactions, maximally reuses high-advantage advantage behavior, and stabilizes GRPO training. Moreover, it is tightly integrated with our SPA reward shaping and FCF curriculum modules, rather than being an isolated system-level optimization.
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G THE USE OF LARGE LANGUAGE MODELS
We claim that during the whole process of this work, Large Language Models(LLMM) are used only polish our manuscript for better readability. We first draft paper version without any help from LLM. Upon drafting this version, we applied LLM to check for typos, grammar errors, and possible ambiguities in the writing expression.
Notably, no LLM usage is upplied in the research methodology or experimental design of this work. All ideas in algorithm design and experiment settings come from the authors of the paper. All data analysis and experiments are actually carried out by the authors of the paper.
H LIMITATION
Reward Model We train a reward model to gain reward signals during the training on the Android- Lab environment. The training data is obtained from strong proprietary VLMs through majority voting, but it is noteworthy that bias still exists in those data. Therefore, the reward model is inevitably born slightly biased from a perfect judge on agentic tasks, which brings about unsatisfying results during online RL in some cases. In particular, while MOBILERL-7B and all ablation studies are carried out with the existence of the reward model, the best model, MOBILERL-9B is gained from online RL purely on the AndroidWorld environment. In observation of this, we conclude that general and unbiased verification model is of significant importance and necessity, which is proposed aspect of our future work.
Difficulties with Public-Use Apps We evaluate out- oft-of-domain generalization on public-use apps via human-evaluated suite (Appendix E.T). However, training and evaluating on public-use apps at scale remains challenging due to the lack of reproducible and efficient protocol in the open domain. Existing approaches typically require human execution of tasks and manual assessment of outcomes, which are costly and difficult to scale.
Moreover, public-use apps often introduce practical challenges. Many require user login, cannot be run reliably virtual machines, or lack mechanisms restore application states, making large-scale online rollouts particularly challenging from an engineering perspective. We plan to explore solutions for more scalable training and evaluation public-use apps in future work.
Limitation of prediction based on XML We observed that certain applications on the Android platform, especially those with large popularity and of comprehensive functions(e.g. Tok, etc.), lack sufficient information about all interactable elements in their XML hierarchy data. To successfully perform complex tasks on these Apps. the current format we chose in model prediction, which is based on bounding box information contained in XML data, needs adaptation toward the general format built on solely visual information. This adaptation is, in the end, inevitable while challenging, so we leave it for our future work to obtain a generalized vision model with no need for XML. data.
Training Time and Resource Consumption Another limitation of our work lies in the substantial resource demands of Android virtual devices. Each device requires more than 5 GB of memory and over 30 GB of storage, which restricts the number of rollouts that can be run concurrently. To address this, we employed four CPU machines with 1 TB of memory each, dedicated solely rollouts. Although we implemented distributed framework that supports multi-machin concurrent sampling, the system can only stably sustain up 256 parallel rollouts, with each step taking more than 10 minutes.
This constraint severely limits further scaling of training steps, as training 100 steps already takes more than 25 hours. In addition, large-scale image inference and network transmission introduce significant overhead, further increasing the computational burden.
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Reviews:
[{"review_id": "QnGZf1YMaZ", "reviewer": "Reviewer_ybyQ", "summary": "This paper proposes an online agentic reinforcement learning framework named MOBILERL, designed to enhance mobile GUI agents. The framework aims to solve challenges in developing GUI agents with RL, such as the heavy-tailed distribution of task difficulty and inefficient large-scale environment sampling. The core of MOBILERL is its Difficulty-ADAptive GRPO (ADAGRPO) algorithm. This algorithm stabilizes training and improves sample efficiency through three strategies including Difficulty-Adaptive Positive Replay (AdaPR), Failure Curriculum Filtering (FCF), and Shortest-Path Reward Adjustment (SPA).", "questions": "1. Given that MobileRL uses both images and XML as input, did the baseline models also utilize XML?\n\n2. Appendices F and D.1 seem to indicate that the 80.2% score on AndroidWorld was achieved by a model trained only on that dataset. Does this mean the 53.6% score on AndroidLab came from a separate model using a different training strategy?\n\n3. The FCF strategy permanently removes tasks with consecutive failures from the training pool. Could this distort the training objective, leading the model to achieve a high success rate simply by \"giving up\" on difficult tasks and optimizing for the remaining, simpler task distribution?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "ADAGRPO improves training stability and efficiency through three key strategies : 1) Difficulty-Adaptive Positive Replay (AdaPR) to amplify the learning signal from rare, informative successful trajectories; 2) Failure Curriculum Filtering (FCF) to optimize computational budget by dynamically down-weighting or removing persistently unsolvable tasks ; and 3) Shortest-Path Reward Adjustment (SPA), which uses reward shaping to encourage more efficient (shorter) solutions. The framework's effectiveness is thoroughly validated on two major benchmarks, AndroidWorld and AndroidLab, where the MOBILERL-9B model achieves state-of-the-art success rates of 80.2% and 53.6%, respectively, significantly outperforming previous results.", "weaknesses": "1. I question the generality of the MobileRL-9B model's SOTA results. Appendix F and D.1 state the best 9B model on AndroidWorld was trained \"purely on the AndroidWorld environment\". This raises doubts about whether the 53.6% score on AndroidLab in Table 1  came from the same model. Attributing SOTA scores from different training strategies to a single model name (MobileRL-9B) may be inconsistent.\n\n2. there is no testing on \"out-of-domain\" public-use apps.", "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": "sTKpZUAGPp", "reviewer": "Reviewer_Q2uz", "summary": "The authors introduce MobileRL, an online agentic reinforcement learning framework for mobile GUI control. They highlight large-scale environmental interaction and heavy-tailed task difficulty as current challenges in scaling up RL for GUI control. To address this, they build on top of GRPO with three main components: (1) off-policy sampling from a buffer of previously successful trajectories, (2) a curriculum that downweighs and filters out low-success rate tasks, and (3) reward shaping to encourage a shortest-path solution. After performing SFT using reasoning and non-reasoning trajectories, they apply MobileRL to achieve, at the time of writing, the state of the art in native model (non-agent-system-based) GUI control.", "questions": "1.\tThe problem formulation (line 150) implies that the agent is provided with no form of memory, only the most recent screenshot. The results seem surprising for this setting. Can the authors please clarify that this is correct?\n2.\tIn line 225, could you please clarify whether the SPA reward is still broadcast to every timestep?\n3.\tIn Figure 3b, are the trajectory-level rewards before or after the SPA penalty has been applied?\n4.\tIn Figure 3b, could the authors provide some intuition on why the MobileRL w/o SPA & AdaPR collapses? Specifically, why does only the combination of these two components prevent collapse? Intuitively, introducing oversampling of positive trajectories would avert collapse. But it is less intuitive that the SPA reward (in isolation from oversampling) would prevent collapse, since this reward changes the learning problem but not necessarily the dynamics.\n5.\tCan the authors please clarify whether difficult tasks are only removed for training, but still included for testing? Naturally, there would be instant gains in reward from removing tasks.\n6.\tCould the authors please clarify whether MobileRL w/o FCF includes AdaPR or SPA?\n7.\tIt is unsurprising that the reward ceiling for MobileRL w/o FCF is lower, since low-reward tasks have been removed. However, why does the curve in Figure 3b start so much lower? It is my understanding that tasks are removed throughout training after at least two epochs (line 260), which suggests that all lines in the figure should start with the same reward.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "Originality: To the best of my knowledge, prior work has not applied oversampling of successful trajectories, reward shaping to encourage shortest paths, and curriculum learning to the GUI and multi-turn agent-LLM setting. The reward shaping is particularly interesting, since encouraging shortest paths has been underexplored in this setting, and the specific formulation is a good contribution.\n\nQuality: The work is of high quality. The core contributions are sufficiently explained, and the experimental analysis is comprehensive and supportive.\n\nClarity: The work is generally very clear.\n\nSignificance: Given the rapid extension of LLMs to agentic and multi-turn settings, and the growing interest in the GUI setting, this work is very significant. This is especially evident in Android World, which, at the time of writing, is SOTA for a native model (non-agentic system). Open-sourcing the RL codebase is also a significant contribution to open research practices, although the current link is dead; this should be rectified in the rebuttal.", "weaknesses": "Originality: It could be made more evident how the specific components have been explored in other domains using LLMs, RL, and multi-turn interaction. For example, it is now commonplace to avoid vanishing learning signal through sampling strategies (e.g. DAPO). Similarly, curriculum learning and reward shaping exist in other non-LLM RL settings. It would be helpful to clarify whether the specific contributions, such as the SPA reward, are present in different settings.\n\nQuality: Some experiments and conclusions may be slightly misleading, possibly due to a lack of clarity at points, as I explain below.\n\nClarity: Some aspects seek clarification, which I expand on in the questions section below.\n\nSignificance: While the Android World results are significant, I encourage the authors to clarify that it is SOTA for native agents, but not all agentic systems (see the current leaderboard.", "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": "xAWcA1kbr3", "reviewer": "Reviewer_TUvD", "summary": "This paper introduces MobileRL, a framework for training mobile GUI agents through online reinforcement learning. The authors combine several mechanisms, Shortest-Path Reward Adjustment (SPA), Difficulty-Adaptive Positive Replay (AdaPR), and Failure Curriculum Filtering (FCF), under an algorithm called ADAGRPO, and deploy it in a large-scale distributed Android simulation environment. Experiments on AndroidWorld and AndroidLab show clear gains over previous baselines.", "questions": "N/A", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The problem setting is realistic and important. Training interactive mobile agents remains difficult, and the authors provide a complete end-to-end RL pipeline that improves performance.\n2. The engineering effort is great. Building a distributed Android environment with scalable parallel sampling is non-trivial.", "weaknesses": "1. Overall, while the paper addresses a practically interesting problem, I find its academic contribution is relatively limited. Most of the proposed techniques have clear analogues in existing RL literature, trajectory length penalty, prioritized replay, and curriculum learning, and are mainly repurposed for the GUI agent setting. From a conceptual standpoint, the paper’s novelty lies more in the system design and implementation scale than in introducing fundamentally new RL ideas or algorithms.\n2. I have some reservations about the mixture sampling strategy. The historical “successful” trajectories stored in the buffer are highly off-policy, while the underlying training objective (a clipped PPO-style GRPO variant) is fundamentally on-policy. As ADAGRPO employs a clipping mechanism, these off-policy samples may often fall outside the acceptable distribution range and thus get heavily clipped, thereby limiting their effective gradient contribution. This raises a question about sample efficiency and the real utility of replayed data. It would be helpful if the authors could provide some experiments to show the clip ratio of the off-policy samples or effective sample contribution.\n3. There is no training recipe in the provided codebase, which is a core component of the proposed framework. Did I miss it?", "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": "1OOkqLTnn7", "reviewer": "Reviewer_Dgh2", "summary": "This paper presents MobileRL, a framework for training mobile GUI agents using online reinforcement learning that addresses three key challenges: complex instruction following under sparse rewards, heavy-tailed task difficulty distributions, and sampling bottlenecks in large-scale mobile environments. The framework consists of a two-stage warm-up (reasoning-free SFT followed by reasoning SFT) and the ADAGRPO algorithm, which extends GRPO with three novel strategies: Shortest-Path Reward Adjustment (SPA) that rewards shorter successful trajectories, Difficulty-Adaptive Positive Replay (AdaPR) that maintains a buffer of challenging successful trajectories, and Failure Curriculum Filtering (FCF) that down-weights persistently unsolvable tasks. MobileRL-9B (based on GLM-4.1V-9B-Base) achieves good results.", "questions": "1. Circularity in Complexity Measurement\n\n> Question: The paper defines task complexity based on the number of steps taken, but this creates a circular dependency problem. If an agent gets stuck or takes inefficient paths, does a simple task become \"complex\"? Conversely, if an agent finds a shortcut, does a complex task become \"simple\"?\n> Whose trajectories define the step count - expert demonstrations, the agent being evaluated, or averaged across all agents?\nIf different agents take vastly different numbers of steps for the same task, how is complexity consistently defined?\n\n\n2. Confusion Between Task Difficulty and Solution Length\n\n> Question: Steps taken seems to conflate intrinsic task difficulty with solution length. A task requiring 25 careful, straightforward steps (e.g., filling a long form) might be less cognitively complex than a 10-step task requiring creative problem-solving or error recovery. How do you justify steps as a proxy for complexity? Consider requesting:\n> 1. Analysis using alternative complexity metrics (e.g., number of different apps involved, branching factor in state space, required reasoning depth)\n2. Breakdown of what actually makes tasks \"complex\" beyond length\n\n3. Technical Questions\n\n- 3.1 SPA mechanism: How sensitive is performance to the choice of α in Equation 2? Have you explored other reward shaping functions beyond the linear penalty? Does SPA risk encourage premature termination (early \"Finish\" actions)?\n- 3.2 AdaPR buffer dynamics: How does the replay buffer composition evolve during training? What percentage of training data comes from replay vs. on-policy sampling at different stages? Is there a risk of overfitting to replayed trajectories?\n- 3.3 FCF design choices: The three-epoch cooldown seems arbitrary. How was this chosen? Have you compared against more sophisticated curriculum learning approaches (e.g., prioritized sampling based on learning progress)?\n- 3.4 Reward model bias: Can you quantify the impact of the 86% accuracy reward model on AndroidLab? What types of errors does it make, and how do these propagate through RL training?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- Well-motivated approach: The paper clearly identifies real challenges in mobile GUI agent training (sparse rewards, heavy-tailed difficulty, sampling inefficiency) and proposes targeted solutions.\n\n- Strong empirical results: Substantial improvements over prior work across multiple benchmarks (e.g., +15.8% on AndroidWorld, +12.4% on AndroidLab compared to previous best).\n\n- Comprehensive ablation studies: The paper systematically evaluates each component (reasoning-free SFT, reasoning SFT, and each part of ADAGRPO), demonstrating their individual contributions.", "weaknesses": "- Dependence on reward model for AndroidLab: The AndroidLab training relies on a VLM-based reward model with only 86% accuracy, introducing potential bias. The paper acknowledges this but doesn't deeply explore its impact. The smoothness difference in training curves (Figure 11) suggests this is a significant limitation.\n\n- Limited evaluation on out-of-domain tasks: All benchmarks (AndroidWorld, AndroidLab, AndroidControl) use similar Android apps. Generalization to truly novel apps or real-world public applications is not demonstrated.", "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)