Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents (2025)

Abstract: Recent success in large multimodal models (LMMs) has sparked promising applications of agents capable of autonomously completing complex web tasks. While open-source LMM agents have made significant advances in offline evaluation benchmarks, their performance still falls substantially short of human-level capabilities in more realistic online settings. A key bottleneck is the lack of diverse and large-scale trajectory-level datasets across various domains, which are expensive to collect. In this paper, we address this challenge by developing a scalable recipe to synthesize the largest and most diverse trajectory-level dataset to date, containing over 94K successful multimodal web trajectories, spanning 49K unique URLs, 720K screenshots, and 33M web elements. In particular, we leverage extensive web exploration and refinement to obtain diverse task intents. The average cost is 28 cents per successful trajectory, making it affordable to a wide range of users in the community. Leveraging this dataset, we train Explorer, a multimodal web agent, and demonstrate strong performance on both offline and online web agent benchmarks such as Mind2Web-Live, Multimodal-Mind2Web, and MiniWob++. Additionally, our experiments highlight data scaling as a key driver for improving web agent capabilities. We hope this study makes state-of-the-art LMM-based agent research at a larger scale more accessible.

## introduction graphical user interfaces (guis) serve as the primary medium for user interaction across digital environments. within the gui environment, llmbased agents @xcite have shown great potential in automating complex workflows for human users. these agents are designed to operate across diverse interfaces, including the web @xcite @xcite , desktop @xcite , and mobile platforms @xcite . navigating modern gui interfaces, which integrate textual, graphical, and interactive components, typically requires agents to possess visual grounding, long-term planning, and memory management capabilities. recent work @xcite has demonstrated the effectiveness of synthetic data for enhancing visual grounding @xcite @xcite and planning @xcite . developing end-to-end gui agents with long-term planning and grounding capabilities requires training on multi-step trajectory data @xcite . however, existing trajectory datasets are primarily human-annotated @xcite @xcite or leverage synthetic data just for task proposal curation @xcite . and human annotation is expensive to scale for collecting large and diverse training datasets. therefore, synthetic data has emerged as a promising alternative to human-annotated data @xcite @xcite @xcite . collecting trajectorylevel datasets presents unique challenges: 1) curating a diverse set of task intents at scale, 2) deploying an agent capable of interacting with a realworld environment to complete these tasks through a series of actions, and 3) verifying whether the task is accomplished by the executed action sequence. data diversity is essential for equipping generalist web agents with a broad range of skills. existing work on synthetic web trajectory generation employs self-instruct for task proposal generation @xcite . it formulates task proposals from homepages or parametric llm knowledge, task proposal: find today's deals on amazon. action: click on the 'today's deals' link. task refinement: find and purchase a lightning deal item with at least 30% off. action: click on the '36% off limited time deal amazon eero pro 6e mesh wi-fi router' link. overall task: buy the amazon eero pro 6e mesh wi-fi router on amazon. task refinement: proceed to checkout for the amazon eero pro 6e mesh wi-fi router with 36% off. action: click on the 'buy now' button for the amazon eero pro 6e mesh wi-fi router. task proposer task refiner task refiner task summarizer (a) proposal stage (b) refinement stage (c) summarization stage (d) verification stage overall task: buy the amazon eero pro 6e mesh wi-fi router on amazon. success ## related work recent advances in multimodal language models have facilitated the development of web agentsautonomous systems designed to interact with realworld websites to perform everyday tasks @xcite @xcite @xcite . early efforts to acquire trajectory data for training web agents primarily relied on crowd-sourcing @xcite . however, due to the high cost of human annotation, recent work has adopted synthetic data generation for largescale collection. autowebglm @xcite and guiact @xcite utilize llms to generate task proposals, which human experts subsequently annotate. openwebvoyager @xcite employs a web agent to execute autogenerated task descriptions. however, since these task descriptions are generated using llms without exploring a website, they fail to capture the full diversity of possible tasks on that website. another line of work, including synatra @xcite and @xcite , leverages web tutorials to guide web trajectory generation. meanwhile, concurrent effort @xcite employs an exploration-based trajectory generation in webarena's sandbox, while our work focuses on more realistic web agent evaluation on live websites. to address diversity limitations in prior trajectory synthesis work, we design a bottom-up web trajectory synthesis pipeline that explores websites dynamically while maintaining a coherent highlevel task intent. we refer readers to appendix f for further discussion. ## data recipe we design an automatic web trajectory synthesis pipeline that explores websites to generate diverse web trajectories. it utilizes playwright foot_1 to execute actions and collect metadata from real-world websites, starting from an initial url. 3 the metadata includes screenshots, html, a11y tree, and actions in grounded and natural language forms. ## experiments we use the synthetic trajectories generated by our pipeline to train small multimodal language models (slms) for web agent tasks. to ensure computational efficiency, we select 40k trajectories from the full set for training. we further refine this subset by filtering out trajectories that contain more than two scroll actions to mitigate potential model bias toward excessive scrolling behavior. finally, we use ~30k trajectories obtained after filtering to fine-tune multimodal language models like phi-3.5v @xcite and qwen2-vl-7b @xcite . for brevity, we denote the models trained on phi-3.5v and qwen2-vl-7b as explorer-4b and explorer-7b, respectively. to test the effectiveness of our data for web-based agentic tasks, we evaluate explorer-4b and explorer-7b on mind2web-live @xcite , multimodal-mind2web @xcite , and miniwob++ @xcite . ## conclusion in this work, we introduce explorer, a scalable framework for synthesizing web trajectories on a large scale. by leveraging thorough web exploration, explorer ensures diversity in both domains and the skills acquired by web agents. unlike previous approaches, our framework generates contextually grounded trajectories that adapt to realworld constraints, improving both task relevance and generalization. we instantiate this framework using urls collected from diverse sources. explorer outperforms existing web agent baselines by a significant margin on both online and offline web agent benchmarks. furthermore, our results highlight the critical role of data scale in enhancing web agents' performance. future work will focus on extending this framework to encompass a broader range of gui environments, such as operating systems with diverse applications. gui agents require specialized skills for different tasks, including information-seeking, operational, and navigation skills. efficient exploration of the environment to acquire these skills presents another promising avenue for future research.