Go-Browse: Training Web Agents with Structured Exploration
Authors: Apurva Gandhi
Graham Neubig
Venue: ICLR 2026 Poster (2026)
Keywords: LLM Agents
Web Agents
Synthetic Data
Exploration
Primary area: datasets and benchmarks
TL;DR: We propose Go-Browse, a method to automatically collect diverse and realistic web agent data at scale through structured exploration in web environments.

Abstract: One of the fundamental problems in digital agents is their lack of understanding of their environment. For instance, a web browsing agent may get lost in unfamiliar websites, uncertain what pages must be visited to achieve its goals. To address this, we propose Go-Browse, a method for automatically collecting diverse and realistic web agent data at scale through structured exploration of web environments. Go-Browse achieves efficient exploration by framing data collection as a graph search, enabling reuse of information across exploration episodes. We instantiate our method on the WebArena benchmark, collecting a dataset of 10K successful task-solving trajectories and 40K interaction steps across 100 URLs. Fine-tuning a 7B parameter language model on this dataset achieves a success rate of 21.7% on the WebArena benchmark, beating GPT-4o mini by 2.4% and exceeding current state-of-the-art results for sub-10B parameter models by 2.9%.

Published as a conference paper at ICLR 2026
GO-BROWSE: TRAINING WEB AGENTS WITH STRUCTURED EXPLORATION
Apurva Gandhi Graham Neubig
Carnegie Mellon University Pitisburgh, PA (apurvag, gneubig @cs. cmu edu
ABSTRACT
One of the fundamental problems in digital agents their lack of understanding of their environment. For instance, a web browsing agent may get lost in unfamiliar websites, uncertain what pages must be visited achieve its goals. To address this. we propose Go-Browse a method for automatically collecting diverse and realistic web agent data at scale through structured exploration of web environments. Go-Browss achieves efficient exploration by framing data collection search, enabling reuse of information across exploration episodes. We instantiate our method the WebArena benchmark, collecting a dataset of 10K successful task-solving trajectories and 40K interaction steps across 100 URLs. Fine-tuning a 7B parameter language model on this dataset achieves success rate of 21.7% on the WebArena benchmark, beating GPT-4o mini by 2.4% and exceeding current state-of-the-are results for sub-10B parameter models by 2.9%
graph
INTRODUCTION
Despite their impressive and often superhuman performance in other domains, most pretrained L.Mss do not perform well on GUI-based web agent tasks. For instance, on the WebArena benchmark (Zhou al.) where humans achieve a 78% success rate, frontier models like GPT-40 (OpenAl 2024a) and GPT-40- NPTT-0-MIIN MINI OpenAI 2024b) score only 38% and 19% respectively, while smaller model like QWEN-2.5-7 2B INSTRRCCT Yang et al.) (024)) scores only 8%. On the other hand, models trained specifically for GUI-based interaction score much better, with C.A 7-SONNE (Anthropic 2025) scoring 45.4% and COMPUTER -USING AGENT (OpenAl 2025) achieving 58%. This gap suggests that training on agent-specific interaction data is crucial for realizing effective web agents.
But collecting high-quality web agent data presents its own set of challenges. Human-generated trajectories offer one source for quality demonstrations but are notoriously expensive and time- consuming to collect for the vast datasets required. One class of methods tries to automatically scale numan-generater data or use humans- -ettsn-----ttt--ooo loop in the dataset collection process (Shen et al. 2024] Zhou et al. 2024] Lai et al. 2024). Another line of work attempts to improve scalability further by proposing fully unsupervised and automatic methods for data generation; for example, by generating synthetic demonstrations from WikiHow-style tutorial articles (Ou et al.) or by building an exploration policy that collects data by interacting with websites (Murty et al. 2024a b).
Among these unsupervised methods, the latter ones that directly explore web environments of interest perform significantly better than those that use indirect and more generic knowledge from the internet (16% (Murty et al. (2024b) vs. 6% (Ou et al.)) success rate). This gap underscores a fundamental problem in digital agents: their lack of prior understanding of the environments they are deployed on. Learning from tutorial or even a human- generated demonstration on how to cancel an ongoing order on Amazon unlikely to transfer the myriad of other websites that web agent may need to interact with. Instead, agents are likely to be more successful if they learn directly from environments they will encounter.
In this work, we introduce GO-BROWSE, a method that automatically collects diverse, realistic, and tailored web agent data through systematic and structured exploration of websites. In particular,
We release our code, dataset and models httpt::///// //gi wwt.ttttthh..eggg com/ ApGa Hrowse

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Published as a conference paper at ICLR 2026
NovExplorrr Provosed Teoks traigttttitte Quer turates
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Figure I: Overview of the GO-BROWS! algorithm for web agent data collection for website. GO-BROWSE's outer-loop (left) maintains an exploration frontier of discovered but not yet fully explored webpages. HO-BRowSE's inner loop (right) explores each webpage in the frontier by (1) Proposing tasks for that webpage that are grounded interaction; (2) Checking the feasibility of those tasks; and (3) Sampling trajectories and discovering new webpages by solving feasible tasks.
GO-BROWSE iteratively builds up a graph of previously visited URLs while collecting data. This allows to reuse information across exploration episodes, resetting new episodes previously discovered promising webpages, and continuing exploration from there. This improves exploration efficiency over previous unsupervised data collection methods (Murty al. 2024b a), which have minimal reuse of information across episodes. Furthermore, resetting to previously discovered webpages allows Go- BROWSE to decouple the challenge of web navigation (finding the correct page) from that of local task solving (performing actions on that page). We demonstrate that this decoupling facilitates a bootstrapping effect, enabling even weaker pretrained L.LMs to collect higher- quality data, since local task execution is often less demanding than website navigation, which often requires camain-speciffe knowledge. GO-B BROW SE draws inspiration from previous RL works like Go-EXPLE Go-EXXPLORE (Ecoffet al., 2019 2021), which used analagous -eet--lee-e strategies to solve games like Montezuma" Revenge that have notoriously difficult exploration challenges.
To evaluate GO-BROwE BR, we instantiate it on the WebArena benchmark, collecting dataset of 10K successful task-solving trajectories as well as ~1KK unsuccessful trajectories across 100 distinct URLs. Finetuning WWVNN-.5--B-INNTTII on WebArena. This result surpasses NNETNAV (Murty et al. 20245), the current state-of-the-art results for sub-10B parameter models by 2.9% and beats GPT-4O-MINI by 2.4%.
dataset achieves task success of 21.7%
2 BACKGROUND
2.1 LLM WEB AGENTS
Following previous work (Chezelles al. 2024 Zhou et al.), web agents implemented using the ReAct pattern Yao et al. (202) where at each timestep f we prompt the LLM with a state 8, and ask produce action dt. Executing 0, in the browser generates new state 8t+1-
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We include several components in each 8p: the task or goal 9, a flattened accessibility tree repre- sentation of the current webpage, a description of the action space, the history of previous actions and any eπrors encountered when executing the last action. The action space includes primitive operations represented as python functions like c i ck (id), scroll (dx, dy), type (text) and wend_msg_tee _urrrrrr (msg), which the agent uses to interact with the browser environment. Each action de produced by the LLM consists of a chain-of- complete action space and prompt template are detailed in the Appendix
and a python function call. The
A trajectory = { ( 82, 022 ..., ST, (T) represents a sequence of states and actions taken by the agent in attempting to complete a task. Trajectories terminate either when a maximum horizon length T is reached or when the agent performs a terminal action (such as send _end_msg_______ee_eeee_e mag_ttstt _o usr (msg) ) For each trajectory, we define reward model R(g,7) 7) E (0.1) that evaluates task completion success. The binary reward indicates whether the agent successfully completed the specified task (R(g.7) 7) = 1) or failed (R(g.7) 7) 0) by the end of the trajectory. In this work, we leverage the BROWSERGYM [Chezelles al. 2024 python package to implement our web agent.
2.2 EXPI.ORATION POLICIES FOR DATA COLLECTION
To collect web agent data in an environment through direct interaction, we need to design a method that can explore the environment effectively to gather diverse and high-quality demonstrations, which we refer to as an exploration policy. We can classify past work on building exploration policies into two main categories: interaction and instruction-first.
Interaction-first Exploration Policy. Interaction approaches (Murty et al. 2024a b). such as NNETNAV roll out an agent (e.g., a prompted pretrained LLM) to explore website with general exploration instructions (e.g., persona simulation) instead of a concrete task (e.g., Add Nintendo Switch to cart). The collected trajectories are then labeled with concrete tasks in retrospect using another prompted LLM, which we call a Labeler (L). We call each rollout here an exploration episode. Algorithm I provides pseudocode for this process.
A benefit of interaction-first approaches is that the collected trajectories may potentially explore deeper parts of the website that might not be immediately apparent in the initial state. But since each exploration episode operates independently, there's 's significant redundancy in exploration- agents may often revisit the same parts of websites across different episodes, leading to similar task demonstrations. Additionally, without specific task guidance, agents may spend considerable time collecting trajectories that do not yield interesting or useful tasks.
Instruction-fire first Exploration Policy. Unlike interaction first approaches, instruction-firs exploration policies (Lai et al. 2024 Murty al., 2024a| Zhou et al. 2024) first generate potential tasks and then attempt to solve them. In this approach, prompted LLM task proposer P observes a state 8, and generates a set of plausible tasks g = {g1- .2........ 9K| grounded in the webpage's 's observed content and functionality. A pretrained policy A then attempts to solve each task 9i. generating trajectories (So, 4o, 81, -1.-.. } for each proposed task. Finally, a reward model R(g,, T: E (0, } evaluates whether each trajectory successfully completes its corresponding task, and successful pairs (gi, 7:) where R(g,, T.) = are added to the dataset D. Algorithm 2 provides pseudocode for this approach.
=
This approach leverages an LLM's prior knowledge efficiently generate diverse, useful and contextually relevant tasks, but has limitations: proposed tasks are typically limited to the currently observed page, and the LLM may occasionally hallucinate infeasible tasks about unobserved parts of the website. This is because task proposal in these methods is typically anchored to an initial static observation. To address this, works like PAE (Zhou et al. 2024) require screenshots of human demonstrations across the website to gain additional context for task proposal; instead, our work implements the task proposer P with agents that explore and gather their own context automatically.
GO-BROWSE
We propose GO-BROWSE, which addresses the limitations of past interaction-first and instruction- first approaches. For each website of interest, Go-BROWSE builds systematic map of previously discovered webpages by treating exploration as graph traversal problem. It maintains an exploration frontier of discovered but not yet fully explored webpages and progressively explores and expands this frontier by proposing and solving tasks that encourage both local webpage exploration and finding new webpages for global frontier expansion.
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Algorithm 1 Interaction-fits Exploration
Algorithm 2 Instruction- first Exploration
I: < Agent()
I: P + TaskProposer()
2: L < Labeler()
2: A <- Agent()
3: D +-
3: R RewardModell)
D +-
5:
for website W E W do
5: for website E W do
6:
for 1. N iterations do
6: + InitialState(W) G P
7:
+ In))))
7:
8:
g Exploration instructions.
8:
for task g E g do
9:
7 + SampleTTajectory 80.9) s0.g)
9:
-SampleTrajectoryAAA. 50, g)
10:
D. < I(T))
10:
if R(g.
then
11: D < D DUD,
11:
D + DU {(g 7)
12: end for
12:
end if
end for
13:
end for
14:
14:
end for
15:
return D
15:
return D
Figure 2: Comparison of common styles of exploration policies for web agent data collection.
Fig. iillustrates the Go- Go-BROWSE algorithm and Algorithm 3 provides pseudocode. Specifically, GO- BROWSR builds up a graph G = (V. E), where nodes v E are unique URLs and edges e trajectories between them. As shown in Fig. its outer loop (left) resembles graph traversal (e.g.. breadth-first search), while an inner loop (right) resembles instruction- exploration.
In each outer loop iteration, Go- BROWSE first selects webpage from the frontier and then performs the following inner loop to collect data and explore u: (1) Propose navigational and local tasks for using the NavExplon orer and PageExpl orer modules, (2) Check feasibility of proposed tasks module, and (3) Sample trajectories by solving feasible tasks
with the with the Solvers module. We describe these modules below. GO-BROWSE'S s outer loop enforces global coverage of the website while the inner loop thoroughly explores each discovered webpage.
NavExplorer: Frontier Expansion and Navigational Task Collection. The NavExplore cr module is responsible for proposing navigational tasks to webpages that neighbor the current webpage v in the graph. This is similar to the TaskProposer module in instruction proaches, but instead of just asking the LLM propose tasks from static observation, we instead implement NavExple orer as web agent itself. We instruct with a goal to find neighbor- ing webpages through interaction with the current webpage, and propose navigational tasks to reach them. We do the latter by extending the action space of the NavExplorer agent with an add ttaksssstttto ciso_dattaa dataset (tasks: tuple [str]) function. Designing NavExplore rer as a web agent empowers it to perform its own purposeful exploration and ground proposed tasks on dy- namically obtained observations. To keep the exploration process efficient and prioritize nodes added to the frontier, havvxplores is asked to prioritize adding tasks for navigating to new webpages that are likely to have common and useful tasks that user might want to perform. Full prompts for the NavExplorer modules are provided in Appendix A.2
ap-
PageExplorer: Local Page Exploration and Task Collection. The PageExplores is similar to the NavExplores except that it is responsible for proposing tasks local to the current webpage e. does so by asking an LLM generate a set of plausible tasks that user may want to perform on the current webpage (prompts in Appendix A.2) The tasks generated by the P PageExplorer help generate training data that thoroughly explore the functionality each webpage.
CeasibilityCheekkr Task Filtering and Trajectory Sampling. The Feasibil ityChecker module filters the tasks proposed by the previous two modules by (1) using a strong pretrained L.MM agent (e.g., computer-use trained LLM) to try and solve each task, and (2) using pretrained VLM- as-a-judge to check if the sampled trajectory solves the task. We sample up to Nmaz trajectories, stopping if sample success. Proposed tasks with least successful trajectory are considered feasible and kept the dataset along with their corresponding trajectories, while the rest are discarded.
Solvers: Prefixed and Unprefixed Sampling. The Sol Solvers sample additional trajectories for the filtered, feasible tasks, but can use cheaper models to sample larger number of trajectories. Additionally, Sol vers perform mix of prefixed and umprefixed sampling. In prefixed sampling,
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Algorithm 3 Go-Browse
I: Initialize Datasct D t- 0, Graph G = (V, Fronticr F
2: Initialize Modules: NavExplorer, PageExplorer, FeasibilityChccker, Solvers, RewardModel R for each website W; E do Froot <- GeeRootURL((W))) Add V/ooi and
5: while F is not empty do
6:
7:
GelCurrentState(()
Propose navigation and local tasks
8:
Gux + Nwwexxpoorerprroose__aaae
9:
Giocal + PageExppoorr.prooossstaaaa
10:
Groopssed Gun U Gooca
Giosible
Filter for feasible tasks and collect initial trajectories
12:
for task g E Gpesposed do (is feasible, thew) Feassblityyheeekerchekk,, and cand__oollettg... se, R. Nmaz)
13:
14:
if is_ feasible then
15:
Add (g, Th.) D: Add 9 Gressiie if Unew is a new discovered URL then Add Nnew to V and F;; Add new edges to
16:
17:
18:
end if
19:
end if for
20:
> Sample additional prefixed and unprefixed trajectories
21:
for feasible task E Gieasibee do
22:
Treetised f Solvers.samplcigg u. R. prefixed=Truc)
23:
D D {gg 7) ?
24:
Smat + CecStatt(GetRoooURLL.
25:
Tomprefised <- sample. Smm, R. prefixed=False)
26:
D 4- D {gg 7) T
27:
end for
28:
end while
29:
end for
30:
31:
return D
the agent tries solve g starting from the current webpage v, while in unprefixed sampling, the agent has to solve g starting from the root node of the webpage (c.g., usually the homepage or dashboard). Prefixed sampling makes the agent's job easier by decoupling navigation (finding the webpage) from task solving locally on that webpage. As we discuss in Section 6 prefixed sampling has higher success rates, letting us bootstrap from even weaker pretrained models. Still, it is useful to sample unprefixed trajectories to instill long-horizon task-solving and exploratory behaviors in the agent.
Relation to Instruction-First and Interaction-First Approaches. We can think of Go-BROWSE's inner-loop interaction between the NavExplorer, PageExpl orer and Feas ibssibill lobllitchhccee as form of instruction-first exploration. But unlike typical instruction- first approaches that only start at the root node (homepage, dashboard, etc.) of the website, Go- BROWSE'S inner-loop is initialized with new pages from the frontier at every iteration. This addresses the localized exploration of instruction-fi thermore, by using web agents for task proposal, Go-BROWs enables more grounded task proposal based real observations. CO-BROWSE also addresses the exploration efficiency limitation of interaction-fire approaches by reusing information from past episodes. Since each iteration of the outer-loop resets exploration previously discovered webpage, Go-BROwSE, can reduce redundancy and instead spend more budget on exploring novel parts of the website.
approaches by enforcing global website coverage. Fur-
DATA COLLECTION
We collect dataset (Go- S- WA) by running Go-BRow the WebArena benchmark (Zhou et al.).. which consists self-hosted websites. representing clones of common domains: Shopping Admin (CMS), Shopping, Reddit, Gitlab, and Map. We explore 20 different URLs for each

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of the five domains, collecting tasks across 100 distinct URLs. While experiments focus these domains, we note that the same data collection pipeline can be run on other websites of interest.
For the NavExploree orer perform up to 15 steps of interaction with CLAUDE-3.7 1.1E-3.7-SONIA 7-SNNETT (An thropic 2025). For the PageExp we perform up to 20 steps with GPT-40 (OpenAl 2024a) and 10 steps with CLAUDE-3. 1.O0-3..--SNNET.. Appendix [B.]]]]]] provides analysis of some of the com- plementary differences in behavior between these models. The Feas bil.lihhhchcceeeeeeeeeeee uses BRAUDB-3.7-SONNEEE to try solving proposed tasks, with maximum of 3 tries, and uses GPT- 40-based 'VLM-as-a-judge' reward model (adopted from Pan et al. (022); Wang et al. (025 We keep maximum of 30 feasible tasks per URL. For the Solvers we use GPT-40-B MINI and QWEN-2.5-7B ----INUTTCTT Task solving is limited to maximum horizon length of 10 steps. The So vers sample 2 prefixed trajectories and unprefixed ones. For all interaction steps in the dataset collection process we use temperature of 0.7. Overall, Go- Sro-BROSFE- WA took ~ 8975.57 to collect; for a detailed cost analysis, see Appendix [.2
Table D shows the composition statistics of the GO-BROWSE- WA dataset. The dataset contains similar proportions of successful trajectories from each model we use to sample trajectories (Fig. B) While here we only finetune on the success steps, we release all steps in our dataset, including failures. The dataset also includes alternate representations of webpage observations (accessibility tree, HTML, and screenshots); although, we only use the accessibility tree for finctuning experiments.
Table 1: Dataset statistics on the 5 WebArena domains (20 pages explored/domain).
Qwen-2.5 7B Instruct
GPT-40 Mini
Chande-3,7 Sonnet
Success
Failure
Total
33,9%
Trajectories
9,504
17,245
26,749 196,462
Figure 3: Proportion of successful trajecto- ries from each model the Go- Go-Browse-VV WA dataset.
Steps
39,339
157,123
Unique tasks
3,422
FINE-TUNING SETUP AND RESULTS
For our experiments, we train -.VN-2255-77 IIS-UUTTUCT by performing supervised finetuning on only the success trajectories of our dataset. Finetuning hyperparameters are provided in Appendix C For fair comparison, dataset (Murty et al. 2024b) which consists of 45K interaction steps across the WebArena domains.
also train a model using the same parameters on the NNETNAV- -W
We benchmark the finetuned models on the 812 WebArena benchmark using BROWSERGYM (Chezelles et al. 2024). Correctness of each task is evaluated using task-specific reward functions provided by WebArena. We use a temperature of 0 for the models when benchmarking.
Table shows the success rates of the finetuned models on WebArena as well as other pretrained models. Our model, HO-BBROWSE-B, achieves success rate of 21.7% overall on the WebArena tasks, outperforming the other models in the table. The model by 13.4% and the finetuned NNETNAV-7B model by 2.9%. Notably, Go-BROwSE-7B also outperforms GPT-40-MINI by 2.4%. Appendix B.3 provides results of performing statistical significance testing with paired bootstrap tests.
model outperforms the
pretrained SWWEN-2.-7BBIINSTRUU
Looking at individual domains, Go-BROWSE-77B scores higher than GPT-40-MIN and NNETNAV- 7B all domains except for Gitlab. Notably, GO-BRoWsE-7B beats NNETNAV-7B by 11% on the Shopping Admin domain and 7% on the Reddit domain.
Model
SR (%
We also evaluate our models on Online-Mind2Web (OM2W) (uue) et al., 2025), an out-of-domail benchmark with 300 tasks across 136 live websites. GO-BROwsE-7B still maintains lead over NB even in this generalization experiment, though-ar expected-moode perform worse this setting compared the in-domain WebArena. GPT-40-MINI also scores much worse on OM2W. Appendix B.4 includes further analysis and discussion, showing that on OM2W websites that are similar to WebArena (In- Domain-Adjacee websites), GO-BROWSE approaches the performance of GPT-40- MIN-I--INII (<1% % SR difference) while still performing better than NNETNAV-75 by 3%.
NNetNav-7B
4.00
Go-Browse-7B
5.33
GPT-4o-mini
9.33
Table 2: Online-M2W results.

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Published as a conference paper at ICLR 2026
Table 3: Success rates on WebArena tasks, Bold indicates the best result in each category.
Model
Overall (%)
Admin (%)
Shopping (%)
Reddit (S)
Gitlab (%)
Map (%)
Closed Models.
GPT-4O-MINI
19.3
19.3
20.9
15.6
GPT-40
37.6
35.7
32.3
50.9
36.7
37.5
CLAUDE-3.-SONNETT
45.4
37.4
37.0
58.8
$2.0
47.7
weer-weights 7B Models.
SVEN-2.5-7B-INSTRIUG
NNETNAV-7B
18.8
14.3
20.3
23.7
19.9
17.2
Go-BROWsE-7B
21.7
25,3
22.4
30.7
15.3
17.9
ANALYSIS
Go-Bro No-Browse Generates Diverse Tasks, Fig. compares the distribution of tasks in the NNETNAV- WA and GO-BROWSE--V WA datasets. We follow Murrty et al. (2024b) in using GPT-40-MINI to that NNETNAV shows tendency
cluster dataset tasks into higher-level intent categories. We to have larger wedges in its task distribution, indicating redundancy in exploration since each episode independent. This pattern is particularly evident in domains that challenging navigate, such as Shopping Admin, because a larger number of episodes will navigate to the same easy-to-find webpages, leading to similar tasks; harder to find webpages, even if discovered by episode, may rarely be revisited in future episode. GO-BROWSE addresses this issue by resetting to previously encountered webpages. Even if a page difficult navigate to, once it discovered, Go-BROwsE makes sure it is thoroughly explored in future episodes.
Go- CO-BROWSE-- WA also exhibits more balanced distribution across domains; NNETNAV contains disproportionately large number of Gitlab tasks and relatively few Reddit tasks. These observations align with our model performance results Table where NNET only outperforms Go- BROWSB-7B on the Gitlab domain, and performs notably worse on Shopping Admin and Reddit.
Go-Browse's Successful Trajectories Go Deeper. Fig. 5 plots how deep into the website the finetuned models go when solving tasks. On the left, when considering all trajectories, we that both GO-BROWSE and NNETNAV behave similarly, but on trajectories where only GO-BROWSE was successful (middle), we see that the depth distribution is more right-skewed, suggesting that GO-BROWSE owes some of its wins to its tendency to solve longer-horizee tasks. we look at the NNETNAV-only successful trajectories (right), again showing that going deeper is a unique characteristic of Go- Bee-BROOS'S successes.
significant difference in behavior,
Table 4 shows URL patterns with the largest difference in success trajectory visits between Go- BROWSE and NNETNAV. GO-BRowsE's successes more frequently involve navigating to deeper URLs, such as editing specific product attributes viewing particular order details. Notably, Go-
Foou
Srayting Mori
Shoedrg
Tacuk
Shass PO
N00
(a) NNETNAV- Task Distribution
(b) Go- NO-BBOWSEE-VE WA Task Distribution
Figure 4: Task diversity of the Go-Browse and NNetNav datasets. Zoom to read sub-task labels.
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Trajectories
Unique
NNetNav
So Browss WWetlov
Go Bronse Whethaw
Go Browse
Nhechov
Denvdyy
0:34
0.79
0.54
4. URL Depth by Trapectory
05
15 1.3 Depth reached by Trajectory
Depth by Trajedory
Figure 5: Distributions of maximum URL path lengths (depth) achieved by trajectories across all trajectories (left), trajectories where only Go-Browse was successful (middle), and trajectories where only NNetNav successful (right). Go-Browse owes some of its wins its tendency to go deeper. Note, depth is calculated as the number segments in the URL path.
Table 4: Top URLs by difference in visit count (GO-BROWSE (GB) vs. NNETNAV (NN)).
More Visits
URL
GB Visits
NN
Diff
Depth
By
10
7
2
/t //dit//
cshopping_edee
add
6
5
NN
2 5
2
<git http:/abb///aerr///epp//////aaea
2
0 3
BROWSE exhibits significantly higher visit counts these deeper URLs, including several that NNETNAV never successfully visited. For instance, Go-BROWSE visited URLs for editing product attributes and searching Reddit and more times respectively, with NNETNAV having or 0 visits to these. Conversely, while NNETNAV more frequently visits URLs related to creating new projects or forks in Gitlab, the difference in visitation counts is comparatively smaller. It also interesting to observe their differing strategies for Reddit: GO-BROWSE tends to use the more direct search functionality, whereas NNETNAV attempts find forums by navigating to the by_submi page. This aligns with our earlier finding that RO-BROWSE tends to succeed on tasks requiring deeper navigation.
Prefixed Sampling Bootstraps Weaker Models. Fig. plots success rates prefixed and unprefixed sampling against the depth of the node (URL) on which the tasks sourced. Overall, prefixed sampling leads to higher success rates. The difference especially pronounced as depth increases: it is harder to find deeper nodes again when starting from the root. The difference is also especially ap- parent for weaker models like QWEN-2.5- 7B-INSTRUCT. Prefixed sampling thus allows us to bootstrap from weaker pre- trained models, enabling creation of higher quality data compared to what pretrained models generate on their own.
0.3
Success Rate
0.1
Medee Uhorefess
Owen mecies)
Owen Unprdttes
0.0
5-B
9-12
13:16
17-20
Node Range
Figure 6: Prefixed sampling leads to higher success rates, especially on deeper nodes, particularly for weaker models like (WEN-2.5--BBIINSTE depth is shortest trajectory length to reach node from the root node, calculated using Dijkstra's 's algorithm.
Node
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Feasibility Checker Improves Exploration Efficiency. During data collection, 403 tasks were filtered out by the Feas beasibiiityChecker. This corresponds to a reduction of 3.2K trajectory rollouts (~ 29.4k steps). This is 13% reduction in steps for the same amount of positive data.
Go-Browse's Outer Loop Improves Website Coverage. We analyze the effect of Go- RR-W'R outer loop by varying how many discovered URLs we reset to when proposing tasks, while
(Resets / Tasks)
Unique URLs
fixing the total proposed tasks at 30 per domain. The 1 1/30 case corresponds to Go-Browse without its outer loop (proposing 30 tasks for just a single URL per domain). As the number of reset nodes increases, the number of unique URLs visited grows steadily, demonstrating that Go- Doo-rr outer loop is critical for broader website coverage and more representative data.
(1 / 30)
183
(5 6) (15/2)
214
260
Table 5: Unique URLs visited
across 5 domains with varying of resets.
RELATED WORK
LLM Web Agents. We build on multiple works on LLM web
agents like the WebArena benchmark (Zhou et al.) and BrowserGym (Chezelles al. 2024) which both provide infrastructure for building and evaluating web agents and also provide an action space
and observation features that leverage in ReAct-based web agents (Yao al. (023)) There is also a line of works that augment the action space (e.g., with developer APIs or self-learned workflows) (Song et al., 2024 Wang et al., 22025] Zheng al. 2025) or perform form of in-context learning by
extending observations with additional context from past interactions (Wang et al.
2024 Murty et al.
2024a Wang al., 2025). Our work instead improves the base agentic capabilities of LLMs while
keeping the scaffolding minimal.
Synthetic Data Generation for Web Agents. Past works on generating synthetic data for web agents have focused on either generating data from static indirect knowledge on the internet (e.g.. tutorial articles) (Ou et al.) or by logging direct interactions with websites (Shen et al.) 2024 Lai al. 2024; Murty et al. [2024a] b Zhou al. 2024). Among the latter methods, interaction first methods (Murty et al. (024b) seem to work unsupervised, while instruction-fiee methods (Zhou
al. 2024 Shen et al. 2024 Lai et al. 2024) have typically required human in-the-looo loop to provide additional context. our work, we build an unsupervised instruction first method that can gather its context via exploration. We also note more recent, concurrent instruction first/hybrid methods for unsupervised collection of web agent data (Pahuja et al., 2025) Sun et al., 2024; Trabucco et al., 2025). A key difference is Go-BROWSE's focus deeply exploring websites by explicitly building and leveraging its own web graph. This helps Go-BROwSE collect high-quality and high-coverage data that enables training state-of-the-arl model for WebArena.
Exploration Methods in Reinforcement Learning. There is also rich line of work from the
RL community on improving exploration in agents (Bellemare et al. 2016 Pathak et al., 2017] Burda et al., 2019; Ecoffet et al. 2019 202T). Of these, our method takes the most inspiration from Go-Explore (Ecoffet et al.) 2019; 2021) 1) which uses analogous reset-then-explore strategies to share information across episodes to improve exploration efficiency in the context of Atari games.
CONCLUSION AND LIMITATIONS
In this work, we propose GO-BROWSE, fully unsupervised and scalable method for collecting web agent data through structured exploration of websites. We release, Go-Brow -Ro-BR WA, a dataset 9.5K successful and 39K unsuccessful task solving trajectories obtained while exploring the WebArena environments. We show that simple supervised finetuning of 7B parameter LILM on this dataset leads to significant improvements success rates web agents over the the previous satt--of-the-ane for sub- 1OB parameter models and also beats GPT-40-MINI We thoroughly analyze the characteristics of our dataset and trained models, showing that trajectories that lead to models that are able better and more deeply navigate explored websites.
WA contains high-quality and diverse
There are a number limitations in our current experimental setup that open promising avenues for future research. Expanding data collection to broader range of websites beyond the five WebArena domains would allow us to generate even larger datasets. While our current method achieves strong results using 7B model trained only successful trajectories, incorporating the signal from the

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39K unsuccessful trajectories by exploring alternative training objectives (e.g.. RL-based objectives) and scaling up model size may unlock even greater performance improvements. Finally, while using LLMs helps scale training data, this risks introducing biases from models and prompts that may propagate to agent behavior, requiring careful auditing and mitigation before deployment.
REPRODUCIBILITY STATEMENT
We release our code, dataset and models at https: hitts://ggituke com/ Hamm/ApGa//oo ApGa/Go-Browse with documentation on how reproduce our dataset collection and experiment runs. Additionally, we describe dataset collection hyperparameters in Section and finetuning hyperparameters in Section 5
and Appendix
ACKNOWLEDGEMENTS
We would like to thank Aviral Kumar, Vijay Viswanathan, Yueqi Song and Zora Zhiruo Wang for insightful discussions and feedback. We also thank the CMU Foundation and Language Model (FLAME) Center for access to their compute cluster. This work is supported in part by Amazon.
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Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, David Vazquez, Nicolas Chapados, and Alexandre Lacoste. WorkArena: How capable are web agents at solving common knowledge work tasks? In Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp (eds.), Proceedings of the 41st International Conference Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 11642-11662. PMLR, 21-27 Jul 2024.
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Shikhar Murty, Christopher Manning, Peter Shaw, Mandar Joshi, and Kenton Lee. Bagel: boot- strapping agents by guiding exploration with language. In Proceedings of the 41 International Conference on Machine Learning, pp. 36894-36910, 2024a.
Shikhar Murty, Hao Zhu, Dzmitry Bahdanau, and Christopher D Manning, Nnetnav: Unsupervised learning of browser agents through environment interaction in the wild, In Scaling Self-Improving Foundation Models without Human Supervision, 2024b.
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OpenAL. GPT-4o: omni-modal flagship model.
https://openai com wtt./onne//////////eeteeete he hello-gpt-40/ May 2024a. Model announced 13 May 2024, accessed 15 May 2025.
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Vardaan Pahuja, Yadong Lu, Corby Rosset, Boyu Gou, Arindam Mitra, Spencer Whitehead, Yu Su, and Ahmed Awadallah. Explorer: Scaling exploration-drivee driven web trajectory synthesis for multi- modal web agents. arXiv preprint arXiv:2502.1 1357, 2025.
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Junhong Shen, Atishay Jain, Zedian Xiao, Ishan Amlekar, Mouad Hadji, Aaron Podolny, and Ameet Talwalkar. Scribeagent: Towards specialized web agents using production-scale workflow data. arXiv preprint arXiv:2411. 155004, 2024.
Yueqi Song, Frank F Xu, Shuyan Zhou, and Graham Neubig. Beyond browsing: Api-based web agents. CoRR, 2024.
Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, et al. Os-genesis: Automating gui agent trajectory construction via reverse task synthesis. arXiv preprim arXiv:2412.11923, 2024.
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Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried, and Graham Neubig. Agent workflow memory. arXiv preprint arXiv:2409. S.t.224409.0742,, 2024.
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Boyuan Zheng, Michael Y Fatemi, Xiaolong Jin, Zora Zhiruo Wang, Apurva Gandhi, Yueqi Song, Yu Gu. Jayanth Srinivasa, Gaowen Liu, Graham Neubig. et al. Skillweaver: Web agents can self-improve by discovering and honing skills. arXiv preprint arXiv:2504.07077, 2025.
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Shuyan Zhou, Frank F Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, et al. Webarena: A realistic web environment for building autonomous agents. In The Twelfth International Conference Learning Representations.
Yifei Zhou, Qianlan Yang, Kaixiang Lin, Min Bai, Xiong Zhou, Yu-Xiong Wang, Sergey Levine, and Erran Li. Proposer-agent-evalaaee (pae): Autonomous skill discovery for foundation model internet agents. arXiv preprint arXiv:2412.13194, 2024.
A WEB AGENT IMPLEMENTATION DETAILS
A.1 WEB AGENT ACTION SPACE
Table 6 shows the action space used for web agent experiments, adopted from the BrowserGym
framework Drouin et al. (2024).
Action Type
Description
noop (wait_ms) ms)
Do nothing for specified time.
click (elem)
Click element.
hover (elem)
Hover element.
(ellm, value)
Type into element.
keyboard_presa (key_comb)
Press key combination.
scroll (x, y)
Scroll horizontally vertically.
select_option (elem, options)
Select multiple options.
peto(url)
Navigate to a url.
go_back ()
Navigate the previous page.
go_forward
Navigate the next page.
new_tab()
Open new tah.
cab_closh
Close the current tab. Bring tab to front.
tab focus (index)
send_msg_ (Lext)
Send a message to the user.
report_infeasibe (reason)
Notify user that instructions are infeasible.
Table 6: Web Agent action space.
A.2 PROMPTS FOR LM COMPONENTS
Prompt Template for Web Agents
# Instructions
You UI Assistant, your goal help the user perform using a web browser. Review the instructions from the user, the current state of the page and all other information to find the best possible next action to accomplish your goal. Your answer will be interpreted and executed hy program, make sure to follow the formatting instructions.
Goal (Goal)
#Action Space
{Action space description from Table
Here are examples of actions with chain-of of-tho reasoning:
("thought": "I now need click on the Submit button to send the form. will use the click action on the button, which has bid 12.". "action": "click('12))
("thought": "I found the information requested by the user, I will send the chat.", "action": "send. msg to user(" 'The price ("thought": "I have finished navigating to the Products page. I will inform the user that have completed the task.", "action": "send_msg__ to user(") have finished navigating to the Products page.")"
inch laptop is 1499 USD.")"
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# Current Accessibility Tree (Axtree Text)
# Error Message from Last Action
[Last Action Error)
History Past Actions
(Past Actions}
# Next Action
You will think step by step and produce your best action. Reflect your past actions, any resulting error message, current state of page before deciding on your next action. Provide your output as a single json with a thought and an action. All reasoning must be contained within the thought key of the json ouiput, and only single action must be provided for the action key. Future actions will you have finished performing the request, send message the in
be taken subsequently. concise and to the point manner.
Goal for NavExplorer
I am trying to collect a dataset to train a better web browser agent that can perform actions for users in a particularly interested to collect **vviggttoon tasks that are feasible to
web browser. For this. perform from the current web page. Navigation tasks are tasks requiring navigating a specific page.
Collect navigation tasks that require navigating to another webpage from this current page. You may click on links to try finding other interesting pages to collect tasks from. But if you do navigate to another page, instead of collecting tasks that page, make using go back' or goto'. We will collect tasks from these new pages later. When collecting navigation tasks, prioritize those that would likely have interesting/usefuul tasks on them over ones that likely won't give many useful tasks to collect.
navigate back the previous page
As you exploring, you add navigation tasks the dataset using the 'add_ sald_ttsss___ to dataset function.
When you are done exploring the current page, send a message to the user using "send msg to user' confirming this.
Be sure to prioritize adding navigation tasks to pages that a typical user of this web page would most often want to navigate to, over niche pages that the typical user would rarely frequent.
**Importam Remember that if you successful navigating corresponding task to the dataset as your next action before finding new pages.
page, you should add
Goal for PageExplorer
I am trying to collect dataset train a web browser agent can perform actions users web browser. For this, need to first collect tasks that are feasible to perform on the current web page. The tasks should be concrete (e.g., on amazon product page for product X. an appropriate task could be "Leave positive review for X" from X Y." where X and are specific locations).
maps website task could be "Show me driving directions
You may explore by performing actions on this web page if that helps to determine concrete tasks that are feasible.
Find the tasks that possible perform the current web page itself, without have navigate other links/uris. Though, you may find helpful to navigate through menus this page to get better idea what types of tasks are feasible. you accidentally go to a new url while trying to navigate items on the page, you can back to the previous page using the 'go back" function.
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Tasks usually of three types:
to obtain certain information from the webpage, such the information of a product, reviews, map info, comparison of map routes, etc. Site navigation: The user wants to navigate to a specific page.
Information seeking: The user
3. Content modification: The user wants to modify the content of webpage or configuration.
Be specific as you can while creating tasks. The web agent may start from a different web page when asked to complete the task and so may not have the current page context to understand the task. So, for example, avoid creating generie tasks like "Add item to cart" or "Print receipt for this order." Instead you want to create specific tasks like "Add Sony PS5 to cart" or "Print receipt for Martha Jone's order of the Nike Velocity Sweatpants from May 21, 2021"
I recommend the following order to collecting tasks: 1. First look for information seeking/extraction tasks that can be answered simply using information on the current page, requiring additional actions.
Collect navigation tasks that require navigating to another webpage from this current page. You may click to links to try finding other interesting pages to collect tasks from. But you do navigate to another page, instead of collecting tasks on that page, make sure to navigate back to the previous page using "go_ back". We will collect tasks from these new pages later. When collecting navigation tasks, prioritize those that would likely have interesting/useful tasks them many useful tasks to collect.
that likely won't ' give
Finally, you can try to find content modification tasks on the current page that require performing actions on the current page itself.
As you are exploring the page, you may find it helpful click on buttons, links, and other elements on the page to see they reveal any additional information or options could lead new tasks. You can also hover over elements to see if they provide any tooltips or additional context.
#mporraatt
When collecting tasks. focus more on the common tasks a typical user webpage would want to perform. Avoid niche tasks that are unlikely to relevant to the typical user of this website. For most common styles of tasks, it may be useful to include a few variants or related tasks to help the web agent learn frequently used skills.
As you are exploring, you can add tasks to the dataset using the 'add_tasks, tasss_tkksttt to dataset' function.
When you are done exploring, send a message to user using 'send msg to user' confirming this.
Prompt for VL.M-as-a-judge Reward Model
You expert in evaluating the performance of web navigation agent. The agent is designed help a human user navigate website to complete a task. Given the user's intent, the agent's action history. the final state of the webpage, and the agent's response to the user, your goal is to decide whether the agent's execution is successful or not. Please be careful of each detail and strict about the evaluation process.
There are three types of tasks:
obtain certain information from the webpage, such as the information of product, reviews, map info, comparison map routes, etc. The bot's response must contain the information the user wants, or explicitly state that the information is not available. Otherwise, e.g. the bot encounters an exception and respond with the errоr content, the task is considered failure. Besides, be careful about the sufficiency of the agent's actions. For example. when asked to searches, and then return the top items. the ordering action is missing, the task likely to fail.
1. Information seeking: The
top-searched items shop, agent should order the items by the number
2. Site navigation: The user wants to navigate to specific page. Carefully examine the bot's action history and the final state of the webpage determine whether the bot successfully completes the task. No need to consider the bot's response.
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Content modification: The user wants modify the content webpage or configura- tion. Carefully examine the bot's action history and the final state of the webpage to determine whether the bot successfully completes the task. No need to consider the bot's response.
User Intent: [Goal}
Action History: (Last Actions]
The final state of the webpage provided as an accessibility tree: [Axtree Text)
The last snapshot the web page is shown in the image. {Screenshot)
B ADDITIONAL ANALYSES
B.I DESIGN CHOICES FOR TASK PROPOSAL
B.1.1 GPT-40 vs. SLAUDE-3.7-SONEE FOR PAGEEXPLORER TASK PROPOSAL
To understand how task proposal behavior differs based model choice, tag proposed Page- Explorer tasks using an LLM as navigational (Nav), cnermattion-seeinng (Info), or state/content- modifying (Mod) tasks (the same three categories mentioned in the PageExplorer goal). We also perform clustering of these tasks to measure diversity, similar to Section 6
The models differ in task proposal behavior shown in Table 7 (1) CLAUDD-3377 1.00R---.7-ONNEEE proposes almost momost double the number tasks with half the max step budget; (2) GPT-40 generates a more diverse set of tasks for the quantity proposed, especially Mod tasks, where GPT-40 has many more task clusters.
The efficiency of Claude allows us give a smaller max step budget when used PageExplorer. On the other hand, GPT-40 's diversity of Mod tasks justifies using it as well to complement Claude.
Table 7: Comparison of GPT-40 and S.AUDD--.7-SONNER for PageExplorer agents.
# Tasks
# Clusters
Model
Max # Steps (Per Node)
GPT-40 Nav
Info
Mod
Nav
Info
Mod
274
227
243
24
18
34
CLAUDE-3.7-SONNET
415
508
516
23
19
19
10
B.1.2 NAVEXPLORER vs. PAGEEXPLORER TASKS
Since navigational tasks are important for website coverage and are linked to Go-Browse's 's outer loop, we also explicitly add a NavExplorer agent with Claude (chosen for its efficiency) in addition the PageFxxlorer agents. This more than doubles the number of navigational tasks in the dataset.
Table 8: Comparison of Explorer types on navigation tasks.
Explorer Type
# Nav Tasks
Nav Task Clusters
NavExplorer
925
PageExplorer 689
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B.2 DATASET COLLECTION COST ANALYSIS
Table provides the cost per model during rollouts (both data collection and task proposal Panel A) and also the cost of trajectory evaluation using GPT-40 (Panel B). The overall cost of collecting SUUDE-3.7-SONNEE
NO-BROWSE--W. WA is $975.57. We note that for trajectory rollouts, the cost of GPT-40 and GPT-4O-MINI is significantly reduced due to lower prices for cached tokens. We observe that (~~ 53% of input tokens are cache reads average). We use the official OpenAl and Anthropic API pricing to compute their costs and use Together Al (Together AI) to estimate API cost for QWEN-2.5-7B I--SSRRUCT..
Table 9: Go-Browse cost analysis for rollouts (agents) and trajectory evaluation.
Panel A: Rollout Costs (Agents)
Model
Num. Trajs
Num. Steps
Avg. Cost / Step
Total Cost ($)
GPT-44-mini
11,695
95,314
0.0008
76.25
Qwen-2.5-7B Instruct
10,203
79,209
0.0025
198.02
Claude-3.7-Somne
5,102
24,532
0.0190
466.11
GPT-4o
103
789
0.0181
14.28
Total (Rollouts)
27,103
199,844
0.0037
754.66
Panel B: Trajectory Evaluation Costs
Model
Num. Trajs
Avg. Cost / Traj Eval
Total Cost
GPT-40
11,105
0.0199
220.91
Grand Total (Rollouts + Eval)
$975.57
B.3 PAIRED BOOTSTRAP TEST FOR WEBARENA RESULTS
We measure statistical significance using the paired bootstrap test with 10,000 bootstrap samples of the WebArena benchmark results. Our model is statistically significantly better than QWEN- 2.5-7B-INSTRUCT (p 0.001). It was also judged as better than GPT-40-MING (p 0. 001) and NNETNAV-7B (p 0.094). These results demonstrate a moderate degree of confidence our model's improvement over these baselines, with high win ratios for GO-BRoWsE--7B.
Table 10: Paired bootstrap tests of models vs. Go-BROWSE-78 on WebArena with 10K samples.
Win Ratio
Model Compared
Winner
p-value
(Baseline / Tie Go-BROWSE-7B)
CLAUDE-3.7-SONNE
CLAUDD--.7-SONNE
(1.000/ 0.000 0.000)
0.000
GPT-40
GPT-40
(1.000 0.000 0.000)
0.000
GPT-40-MINI
GO-BROWSE-7B
(0.085 0,024/ 0.892)
0.108
NNETNAV-7B
Go-Bro NO-BRowsE-7B
(0.076 / 0.018 0.906)
0.094
QWEN-2.5-7B-INSTHE
GO-BROWSE-7B
(0.000 0.000 1.000)
0.000
B.4 OOD ANALYSIS ON MILINE-MMND22EB (OM2W)
In this section, we provide further analysis and discussion of the OOD OM2W experiment presented in Section 5 First, we use an L.MM (GPT-40-Mi)) to classify each website in OM2W as being In-Domain-Adjacee (IDA) or truly Out-of-Domain (OOD) depending on how similar the OM2W tasks on these websites are to tasks on the WebArena websites. Fig F shows both the success rates on IDA and OOD websites OM2W across the benchmarked models. We note the following interesting insights: (1) OM2W's IDA tasks seem to be significantly harder for models compared to OOD tasks, as be seen by GPT-4O-MINI's much higher SR OOD tasks compared to IDA tasks; (2) On IDA tasks, CO-BROWSE- 7 performance is similar to GPT-40-MIN' performance (<1% % difference in SR) while having comfortable lead over NNETNAV-7B (3% difference in SR).
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This suggests that models trained with (O-BRROSE continue to perform well out-of-domain but similar websites to the ones explored during data collection.
Interestingly, NNETNAV-7B performs better than GO-BROWSE-7B truly OOD tasks. To un- derstand this better, and since OM2W provides difficulty labels per task, we also plot breakdown of IDA and OOD success rates for the different models by task difficulty in Fig. [8] Here, that while NNETNAV-7B OOD success rate increases over NO-BROWsE-77B success rate on these tasks, most of the increase comes from better performance on easy tasks. Finally, in Table II we provide many examples of IAD and OOD succeses/failures for the different models spanning different difficulty levels.
20.0
Domain Type
In-Domain-Ajjjce (IDA)
Distribution
15.0
Success Rate (%)
12.5
10.0
7.5
7.3%
6.0%
4.9%
2,5
2.3%
0.0
Ga Browse: 7B
NNetNav 78
GPT 4o mini
Figure 7: Breakdown of Success Rates of models on the Online-Mind2V benchmark based on whether tasks come from D--DDom---djaacoo (IDA) or truly Out-of-Domaii (OOD) websites.
Successes by Difficulty In Domain-Adjacent (IDA)
Successes by Difficulty Out-oo--Dstributiinn (000)
Difficlity
Difficulty
17.5
Easy
Medium
16
Medium
Hard
Hard
Nimber of Sucoesess
12.5
7.5
2.5
0.0
GPT-4a-mini
Go-Browse-78
NNetNav-7B
GPT-4o
NNetNav-78
Model
Figure 8: Breakdown of Success Rates of models the difficulty and whether the tasks are sourced from IDA or OOD websites.
benchmark by both
Table 11: Example tasks from In-Domain-Adjacent (IDA) and Cut-of-DDstribuute (OOD) website categories, showing successful (V) and failed (X) attempts. Difficulty: E=Easy, M-Medium, H-Hard.
Model
Domain
Result
Diff.
Website
Task
Go-Browse-78 7B
IDA
E
new.mta. info
Find the list of neighbor- hood maps for Brooklyn on now.mta.info.
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Model
Domain
Result
Diff.
Website
Task
Go- Browse-7B
IDA
M
ign.com
Find walkthrough for the game "The Legend of Zelda: Breath of the Wild" on ign. Find the most reviewed gluten- free multivitamins from CVS Health Brand under $15.
Go-Browse-7B
IDA
H
CVs. com
Ro-Brrosse-BB
IDA
E
google.com
Look for reviews of a Nest Hello Video Doorhell and fil- ter by 1-star ratings.
Go- Co-BBrosse-BB IDA
M
soundel oud.com
Browse a user homepage that reposted the top song from Top 50 Rock chart.
Go-Browse-7B
IDA
x
H
student.
Find the lowest-priced Student housing Liverpool Inter- national College which has been priced between 100 to 300 pounds and has private bathroom.
Go-Browse-7B
0OD
E
finance, yanicce.yhhcccomm
Show historical data for EUR/USD.
Go- Browse-Toi 7B
0OD
M
drugs. com
Find the Drug Interaction Re- port for Viagra and alcohol. Find the weight of baggage al- lowance for economy class Qatar Airways.
Go- CooBBrosse771 7B
0OD
E
gatarairways.com
Go-Browse-P7
00D
M
careers. wall mart.com
Find support services jobs in Bentonville. in the state of Arkansas.
Go-Browse-7BB
00D
H
healthgrades.com
Browse dermatologists within 10 miles of zip code 10019 and filter by only those who accept Blue Medicare Advantage.
NNetNav-7B
IDA
E
allrecipes.com
Open the reviews of recipe with beef sirloin.
NNetNav-7B
M
ign. com
Find walkthrough game "The Legend of Zelda: Breath of the Wild" on ign. View the list of the Most Pop- ular TV rotten tomatoes. Find nearest vet within 50 miles of zip 75228.
NNetNav-7B
IDA
E
rottentomatoes.co com
NNetNav-7B
M
akc.org
NNetNav-7B
IDA
X
II
bbb.org org
Get a quote from C and above- rated solar energy equipment company within 10 miles of Miami, Florida.
NNetNav-7B
0OD
E
finance Finance.yahoo, com
Find the closing stock price for Tesla March 17, 2023.
NNetNav-7B 0OD
M
careers. wawllmm wmalmat..... com
Find support services jobs in Bentonville, in state of Arkansas.
NNetNav-7B OOD
H
chase.com
Using a calculator to deter- mine how much I can have in my 401(k) account retire- ment, work from age 22 to 65, with an annual rate of re- turn of 3%, annual employee contributions of $8,000, and annual employer contributions of $8,000.
NNetNav-7B
0OD
X
B
healthline.com
Find the recommended dosage for Vivitrol.
18

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Published as a conference paper at ICLR 2026
Model
Domain
Result
Diff.
Website
Task
NNctNav-7B
OOD
M
petfinder.com
Find young cats in Seattle and show off the newest additions. Search for the ovulation calcu- the
NNetNav-7B
0OD
H
webmd. com
lator and enter Mar first date of the period and cal- culate the date of ovulation and pregnancy test day.
GPT-4o-mini
eventbrite.com
Browse the page with event planning tips on Eventhrite. Find the tech specs of the Mac- Book Pro 16-inch introduced November 2023.
GPT-4o-mini
IDA
M
apple.com
GPT-4o-mini
Seagueoflegende com
Browse final skin in list for the champion Ahri Browse apartments with least bedrooms and 2 bath- rooms and max price of $4000 per month.
GPT-4o-mini
IDA
B
craigslist.org
GPT-4o-mi mini
IDA
X
M
usps.com
Find the nearest location to zip code 54620 that offers size P.O. Boxes. Find an Airbnb Cleveland for three nights. The check- in date is the day after tomor- row. We have 2 adults. 2 kids. and pet. The budget $100 to S300 per night. Essential amenitics include free parking, washer, and gym. Browse the natural products database.
GPT-4o-mini
airbbb.com com
GPT-4o-mini
OOD
E
drugs. com
GPT-4o-mini
0OD
M
jobs. chrocicc.cccccom
Browse lenured/tenur-track faculty positions in Computer Sciences & Technology in Cal- ifornia.
GPT-4o-mini
OOD
H
chase. com
Using a calculator to deter- mine how much I can have in my 401 account retire- ment, if work from age 22 with an anmual rate re- turn of 3%, annual employee contributions of $8,000, and annual employer contributions of $8,000.
GPT-4o-mini
OOD
E
thumbtack. com
Find house eleaning service in 10001 on a weekly basis. Compare the U.S. ETP Odd Lot Rate (%) between Quartile 1 Quartile 4, viewing quar- tiles by price, and display the chart with a logarithmic scale on the vertical axis.
GPT-4o-mini
OOD
M
sec.gov
GPT-4o-mini
OOD
H
gov.uk
Check if visa required work in UK for longer than months in Hcalthcare as an American citizen.
19

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Published as a conference paper at ICLR 2026
C HYPERPARAMETERS AND ADDITIONAL EXPERIMENT DETAILS
For finetuning experiments, the following hyperparameters. We train for epochs the whole dataset maximum sequence length of 24K tokens. We use a learning rate 2e-5. We use batch size (1 per gpu) with gradient accumulation steps.
We used the following computational resourees. For finetuning with single NVIDIA 8xH100 node where cach H100 has 80GB of VRAM. Training took <.4 hours for each finetuning run. For datasct generation, nodes with of SLURM cluster in paralicl, with 256GB RAM and 8 CPUs allocated cach, one each per WebArena domain. We ran LLM inference servers on NVIDIA L40S GPUs to support inferencing with OWEN-2.5-7B-INSTREE
Overall, dataset generation took ~-3 weeks to complete.
In this work, besides generating our own Go-Bo BROWSE- WA datasct, we leverage the NNETNAV- WA dataset to build baseline. This dataset was released with the Apache License 2.0 license.
D LLM USAGE
An integral part of Go- BROWSE collecting data automatically employing LLMs explore and interact with websites. The usage of LLMs in this capacity been detailed extensively Section and throughout paper text. We also use LLMs to help with paper writing, particularly, to suggest revisions to phrasing of initial section drafts and to iterate on code for figures. All LLM generated revisions are further reviewed and revised by the authors before being included in the paper.
20


Reviews:
[{"review_id": "BUFeDLpySf", "reviewer": "Reviewer_Ur5A", "summary": "This paper proposes Go-Browse, a go-explore inspired method for gathering training trajectories for web agents. Go-Browse leverages an outer loop that selects a previously discovered webpage, then performs an inner loop that uses modules to propose navigation and local tasks within a page, then uses verifier and task solver to gather trajectories for feasible tasks. Experimental results show that Go-Browse improves over the comparable state-of-the-art method for trajectory generation, Nnetnav, and analyses show that the proposed method results in deeper and wider exploration of the web environments.", "questions": "- Can you clarify \"explore 20 different URLs for each of the five domains\" (L269)? Does this mean that you use 20 starting URLs?\n- What is the overall number of trajectories used for finetuning with Nnetnav (vs Go-Browse)?\n- For finetuning, are both prefixed and unprefixed trajectories used together?\n- What is the accuracy of the LLM judge? Is there any concern of label noise in the dataset from judge errors?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The proposed method is well motivated, and the adaptation of Go-Explore to exploring from a frontier of discovered webpages is intuitive and novel. \n- The proposed method shows clear effectiveness over a comparable state-of-the-art method.\n- The experiments and analyses are thorough, and all details as well as prompts are provided, enhancing reproducibility.\n- The paper is well-written and easy to follow.", "weaknesses": "- The method leverages claude-3.7-sonnet for trajectory gathering, and it is unclear whether this may be a significant advantage of the proposed approach over NnetNav.\n- I'm not sure I understand the purpose of the experiments on Online Mind2web, as the results seem to be evaluating WebArena-trained models on Online Mind2Web. However, my understanding is that the proposed method is more effective at exploring a given environment such as the websites in WebArena, while Online Mind2Web consists of entirely different websites.", "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": "3sDVKXZ0JB", "reviewer": "Reviewer_yijR", "summary": "This paper proposes a pipeline for generating web trajectories via structured exploration that maintains a graph of discovered pages and resets to promising frontier nodes. A feasibility checker filters proposed tasks by attempting them with a strong model and using a VLM as a judge. Solvers then sample extra trajectories (prefixed/unprefixed) with cheaper models to scale data. The system is then instantiated on WebArena for collection and evaluation. Also evaluated on Online-Mind2web.", "questions": "Is there a reason for choosing WebArena rather than the real-world web for trajectory synthesis? I would like to see results on Online-M2W in-domain after trajectory synthesis on those websites.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. Treating websites as a URL graph with a maintained frontier reduces redundant exploration across episodes and helps reach deeper states that matter for task completion. The outer loop/frontier mechanism is well-motivated and is validated by broader site coverage and deeper success trajectories.\n2. The dataset of ~10K trajectories is a valuable resource to the community to train web agents.", "weaknesses": "1. The evaluation results on Online-M2W are weak. While the authors say it is due to a different domain, it does not help sell their synthetic trajectory generation approach. The primary purpose of synthetic data generation for web agents is to improve their performance on real-world websites in the wild.\n2. This proposed pipeline may not work as well on the real-world websites, as these are dynamic and the graph can change during the course of exploration.", "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": "XCZBB8LgWH", "reviewer": "Reviewer_Bsi3", "summary": "Achieving effective exploration of web navigation agents can be challenging with existing approaches for the following reasons. Instruction-first approaches, which generate tasks (or task instructions) first and then navigate the web to solve it, are dependent on and limited by the prior information and knowledge about those web pages. On the other hand, interaction-first approaches, which navigate the web pages first and then post-process the collected trajectories, may be inefficient and less cost-effective. This work proposes a hybrid approch named Go-Browse. The authors view web navigation as expanding the graph of visited web pages and combine two types of explorations: global (outer loop) and local (inner loop) explorations. Local explorations are in-page explorations that happen in \"frontier\" nodes in the graph. With the use of vision-language models as judges, the tasks are used to gather trajectories and filtered. For scaling of the trajectory dataset, the authors perform additional sampling of trajectories on the filtered tasks by employing cheaper models to solve the tasks from the current web page (prefixed) or initial web page (unprefixed). Using the trajectory data collected in the WebArena environment, the authors fine-tune Qwen 2.5 7B and compare it with the baselines on Online-Mind2Web and WebArena to show the improved empirical performance.", "questions": "1. Is there empirical evidence of the applicability of Go-Browse to real websites with fair complexities? I believe this would be an important question for web navigation exploration approaches.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 1, "presentation": 3, "contribution": 2, "strengths": "1. Motivations  \nThe work and proposed approach are reasonably motivated. As described by the authors, exploration in web environments is one of the challenges for web navigation data collection. Especially, the shortcomings of interaction-first and instruction-first approaches mentioned by the authors could be a bottleneck for scalable trajectory data collection on the web.\n\n2. Presentation  \nThe manuscript provides comprehensive information. It contains figures and pseudo-codes that make it easy to follow the content of the paper. Also, there are many statistics and analyses included, which can suggest insights about the empirical results from different angles. Additionally, the authors also care about reproducibility. The manuscript presents the prompts used for different components of the proposed approach. They also share a link to the source codes for their experiments. Overall, this can encourage the adoption and extension of this work.", "weaknesses": "1. Scalability of the proposed exploration approach  \nThe authors use WebArena as the testbed for their exploration algorithm. However, the WebArena environment consists of concept/mockup websites and is limited in multiple aspects compared to real-world websites. Regardless of the number of unique web pages it provides, the structure of its websites and thus the possible patterns of navigation may not be diverse enough to test the scalability of the proposed approach. This is especially important, as exploration is more helpful and needed when there is more complexity in the environment.\n\n2. Insufficient set of baselines  \nThe authors compare the proposed method (Go-Browse) primarily with NNetNav, an interaction-first exploration method. On the other hand, while the authors mention instruction-first approaches as a relevant line of research, they do not perform an empirical comparison with such methods. Adding comparison with more baselines could make the submission stronger.\n\n3. Comparison with NNetNav  \nWhile NNetNav is the only exploration approach that is being compared with Go-Browse, it does not look like an apples-to-apples comparison to me. There are some components that are reasonably fair for both: the same base model (Qwen2.5-7B-Instruct), data from the same WebArena environment, and comparable numbers of samples (around 39k steps for Go-Browse and around 45k steps for NNetNav). However, I am concerned about other components. Importantly, the authors make use of Claude Sonnet 3.7 and GPT-4o for different purposes. Figure 3 also states that the trajectory data from Claude Sonnet 3.7 constitute 33.9% of the final (successful) trajectory data. On the other hand, for NNetNav, Llama 3.1 70B Instruct was used. Therefore, the strength of the employed models could have meaningfully contributed to the current success rate wins vs. NNetNav on WebArena (2.9%) and Online-Mind2Web (1.33%).", "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": "81tzJQdQ9y", "reviewer": "Reviewer_Hdmj", "summary": "This paper proposes Go-Browse, a structured exploration framework for collecting high-quality interaction data for web agents. By treating the web as a graph and using an outer-loop frontier expansion with an inner-loop task discovery process, the method enables more efficient coverage of websites and deeper navigation compared to prior unsupervised approaches. Fine-tuning a 7B model on the collected dataset yields new SOTA results among sub-10B models on WebArena.", "questions": "See the weaknesses", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Clear and intuitive idea: The graph-based formulation cleanly explains how and why structured exploration improves data efficiency, making the approach easy to understand and reproduce.\n\n- Strong motivation with practical impact: Training web agents without human demonstrations is important and timely, and the proposed method directly contributes toward scalable data generation.\n\n- Consistent empirical evidence: The paper presents solid improvements in both success rates and depth of exploration, demonstrating that the design choices (e.g., prefixed sampling) meaningfully contribute to better real-world web agent capabilities.", "weaknesses": "- Limited evaluation of generalization capability and lack of out-of-domain (OOD) analysis: In the Introduction (line 48-49), the authors argue that “agents are likely to be more successful if they learn directly from environments they will encounter.” While this claim is straightforward and somewhat obvious, it is impractical to train on all possible websites, and agents trained this way may be vulnerable to website updates. Therefore, generalization capability is crucial for web agents. However, the current paper’s analysis primarily focuses on in-domain performance, with insufficient analysis on OOD (Mind2Web) settings. It would be valuable to add additional results and analysis on OOD setups, such as (1) direct comparison with the baseline model and other methods, and (2) success and failure case analysis based on the similarity between in-domain training websites and OOD test websites\n\n- Limited applicability of the proposed algorithm for capturing real-world web usage patterns: While the proposed graph-based algorithm improves coverage by systematically exploring web environments, it may not align well with how users interact with the web in practice. Specifically, the framework assumes a navigation-first, leaf-level task completion structure, where the agent traverses through a hierarchy of pages before executing an action. However, many realistic tasks (e.g., flight booking) require frequent alternation between navigation and value input across multiple intermediate pages. This discrepancy reflects a deeper human–agent misalignment, as the method prioritizes coverage over modeling the natural, goal-driven behavior of real users, potentially limiting its ability to capture the tasks that matter most in real-world web interactions.\n\n- Lack of Analysis and Justification for VLM as a Judge:\nThe FeasibilityChecker component and the dataset filtering process seem to heavily rely on VLM as a judge, yet there is no analysis provided (e.g., robustness or correlation with human judges). Moreover, since the VLM judge evaluates success or failure based on screenshots, a justification is needed for whether this observation-based environment allows for accurate evaluations.", "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)