--- pretty_name: WebJev license: apache-2.0 language: - en multilinguality: monolingual size_categories: - 10K` in the options) | | `WAIT` | wait for the page to update | | `PRESS_ENTER` | submit the focused field | | `PRESS_ESCAPE` | close a menu or pop-up | | `GO_BACK` | return to the previous page | | `DONE` | the goal is visibly satisfied | | `BLOCKED` | no available action can make progress | **Model interface.** The dataset suits decision models that score options rather than generate free text. We render each instance as follows: ```text Context: {context} Question: {question} Options: (A) {option} (B) {option} … Answer: ( ``` - The option order is shuffled. - Options are labelled `A`, `B`, … and then `AA`, `AB`, … for large candidate sets. - The model is trained with cross-entropy over the option labels at the answer position. - `num_tokens` reports the length of this rendering under the Qwen3.5 tokenizer. ## Data distribution ![Distributions of WebJev](assets/distributions.png) *(a) Action labels of action-prediction instances. (b) Composition by question type. (c) Candidate-set size of element-grounding instances. (d) Context length.* ### Composition | Task type | Question type | Structured observation | Textual observation | Total | |---|---|---:|---:|---:| | Action prediction | `operation` | 31,050 | 0 | 31,050 | | Element grounding | `click_target` | 25,465 | 6,197 | 31,662 | | Element grounding | `type_target` | 541 | 604 | 1,145 | | Element grounding | `select_target` | 227 | 38 | 265 | | **Total** | | **57,283** | **6,839** | **64,122** | ### Actions The action labels follow the natural frequency of decisions during web navigation. Clicks and scrolling dominate, and terminal decisions (`DONE`, `BLOCKED`) account for 8.6%. | Action | Instances | Share | |---|---:|---:| | `CLICK` | 14,364 | 46.3% | | `SCROLL_DOWN` | 6,135 | 19.8% | | `TYPE_TEXT` | 3,527 | 11.4% | | `DONE` | 2,397 | 7.7% | | `GO_BACK` | 1,553 | 5.0% | | `WAIT` | 1,137 | 3.7% | | `SCROLL_UP` | 713 | 2.3% | | `PRESS_ENTER` | 362 | 1.2% | | `SCROLL_REGION` | 355 | 1.1% | | `BLOCKED` | 259 | 0.8% | | `SELECT` | 226 | 0.7% | | `PRESS_ESCAPE` | 22 | 0.1% | ### Grounding difficulty Grounding questions are hard: the correct element must be chosen among many look-alike candidates of the same page. - **Click targets** are the largest group. Their candidate sets are multi-scale (32 / 64 / 128 / all elements), which exposes models to increasing distractor density. - **Select targets** often list long option menus. - **Type targets** usually choose between a few input fields. | Question type | Instances | Min | Median | 90th percentile | Max | |---|---:|---:|---:|---:|---:| | `operation` (action prediction) | 31,050 | 3 | 7 | 9 | 13 | | `click_target` | 31,662 | 2 | 32 | 86 | 137 | | `type_target` | 1,145 | 2 | 2 | 4 | 12 | | `select_target` | 265 | 2 | 26 | 81 | 131 | | all grounding | 33,072 | 2 | 32 | 84 | 137 | ### Context length | Subset | Instances | Median | 90th percentile | Max | |---|---:|---:|---:|---:| | Action prediction | 31,050 | 3,056 | 6,902 | 16,371 | | Element grounding | 33,072 | 5,665 | 11,366 | 16,383 | | Structured observations | 57,283 | 4,102 | 9,332 | 16,383 | | Textual observations | 6,839 | 6,102 | 11,587 | 16,376 | | **All** | 64,122 | 4,262 | 9,730 | 16,383 | ### Language - The tasks, instructions and candidates are in English. - Page text is predominantly English. In 0.9% of the instances a non-Latin script makes up at least 20% of the observation: CJK 434, Cyrillic 65, Devanagari 53, Arabic 36, Greek 5. - Localized pages in other Latin-script languages also occur. ### Label sources | Label source | Instances | Share | Description | |---|---:|---:|---| | `live_annotation` | 43,890 | 68.4% | action chosen by the LLM annotator on the live page, kept after verification | | `trajectory` | 20,011 | 31.2% | target taken from a successful agent trajectory and verified | | `trajectory_corrected` | 221 | 0.3% | trajectory target replaced by the reviewer's corrected target | ## Dataset structure ### Configs and splits | Config | Instances | Content | |---|---:|---| | `all` (default) | 64,122 | all instances | | `action_prediction` | 31,050 | next-action questions | | `element_grounding` | 33,072 | click, type and select target questions | The dataset is released as a single `train` split. Instances that share a `task_id` come from the same task, and instances that share an `instance_id` come from the same decision step: its action and grounding questions, or the same grounding question at different candidate scales. Use `task_id` to build leakage-free held-out splits. ### Data fields | Field | Type | Description | |---|---|---| | `id` | string | unique instance id | | `task_type` | string | `action_prediction` or `element_grounding` | | `question_type` | string | `operation`, `click_target`, `type_target` or `select_target` | | `action` | string | action prediction: the gold action (see the action space). Grounding: the operation being grounded | | `context` | string | the observation (model input) | | `question` | string | the instruction and question (model input) | | `options` | list[string] | the candidates (model input) | | `gold` | int32 | index of the correct candidate in `options` | | `answer` | string | `options[gold]` | | `num_options` | int32 | number of candidates | | `num_tokens` | int32 | length of the rendered instance (Qwen3.5 tokenizer) | | `context_format` | string | `structured` or `text` | | `website` | string | the task's target website | | `page_url` | string, nullable | URL of the observed page (structured observations) | | `task_source` | string | public task collection of the task | | `label_source` | string | `live_annotation`, `trajectory` or `trajectory_corrected` (see Label sources) | | `task_id` | string | task identifier, for grouping | | `instance_id` | string | decision-step identifier, for grouping | Only `context`, `question` and `options` are model inputs, and `gold` is the target. The remaining fields describe the instance and are meant for analysis and splitting. ### Example ```json { "task_type": "element_grounding", "question_type": "click_target", "action": "CLICK", "context": "{\"page\": {\"url\": \"https://www.spanishdict.com/\", \"title\": \"SpanishDictionary.com | English to Spanish Translation, Dictionary, Translator\", \"text\": \"Learn Spanish\\nTranslation\\nConjugation\\n…\"}, \"elements\": [{\"role\": \"link\", \"index\": \"1\", \"label\": \"SpanishDictionary.com Homepage\", \"operations\": [\"CLICK\"], \"position\": {\"x\": 213, \"y\": 0}}, …], \"recent_actions\": [{\"action\": \"Translate Spanish or English\", \"kind\": \"fill\", \"text\": \"spring\", \"page_changed\": true}]}", "question": "{\"goal\": \"… The user's original task: On https://www.spanishdict.com: Search for the word 'spring' on SpanishDict and identify two different Spanish translations that represent distinct meanings. …\", \"operation\": \"CLICK\", \"rules\": [\"Advance the user's entire goal from the CURRENT page using one operation. …\"]}", "options": ["1: {\"element\": \"[1] SpanishDictionary.com Homepage\", …}", "…", "14: {\"element\": \"[14] spring\", \"role\": \"option\", …}", "15: {\"element\": \"[15] spring break\", \"role\": \"option\", …}", "…"], "gold": 12, "answer": "14: {\"element\": \"[14] spring\", \"current_value\": \"0\", \"role\": \"option\", \"selected\": \"false\"}", "num_options": 28, "num_tokens": 3949, "context_format": "structured", "website": "spanishdict.com", "task_source": "MolmoWeb", "label_source": "live_annotation" } ``` ### Usage ```python from datasets import load_dataset ds = load_dataset("Lexmount/WebJev", "all", split="train") grounding = load_dataset("Lexmount/WebJev", "element_grounding", split="train") def render(ex): labels = [chr(65 + i) for i in range(26)] + [a + b for a in map(chr, range(65, 91)) for b in map(chr, range(65, 91))] options = "".join(f"\n({labels[i]}) {o}" for i, o in enumerate(ex["options"])) return f"Context:\n{ex['context']}\n\nQuestion: {ex['question']}\nOptions:{options}\nAnswer: (" # leakage-free split by task tasks = sorted(set(ds["task_id"])) held_out = set(tasks[: len(tasks) // 20]) train = ds.filter(lambda ex: ex["task_id"] not in held_out) valid = ds.filter(lambda ex: ex["task_id"] in held_out) ``` ## Dataset construction ![Construction of WebJev](assets/pipeline.png) WebJev was built in four stages. 1. **Task curation.** We build on the task collections of MolmoWeb and WebGym. We keep tasks on real, publicly reachable websites that need no account. 2. **Agent-in-the-loop collection.** An LLM annotator (DeepSeek-V4.1-Flash) operated a production-grade browser agent on the live websites, in isolated cloud browser sessions. - At every step, the agent's observation and questions were recorded exactly as served. - The annotator's choice was then executed, and its effect on the page was recorded with it. - A successful demonstration of the same task guided the annotator. It is never part of the model input, so each instance must be answerable from the observation alone. 3. **Multi-signal verification.** Each candidate label was checked against complementary signals: - execution evidence and a meaningful page-state change; - trajectory-level loop detection; - agreement under independent re-annotation with shuffled options and without guidance; - adjudication by an independent verifier for terminal and ambiguous decisions. A final review restored valid decisions that conservative checks had rejected and removed near-duplicates. About half of all recorded decisions survived verification. 4. **Grounding enrichment.** Successful steps of agent trajectories on the same websites were converted into additional element-grounding instances. - Candidate sets are built from the actionable elements of the same page, with mined hard negatives. - Each question is rendered at several candidate scales. - An LLM reviewer checks the target, and a blind challenge removes candidates that could also be correct. - A textual observation view adds format diversity. **Quality control.** - Independent audits of stratified samples, checked against page screenshots, found 98% of the annotated labels correct or acceptable. - Every instance fits in 16K tokens without truncation. - Exact duplicates were removed. ## Licensing and access - **Licence.** WebJev is released under the [Apache License 2.0](LICENSE). You may download it, use it to train and evaluate models, including commercial ones, and redistribute it. Please keep the licence and attribution notice. - **Third-party rights.** Page text, URLs and element labels were captured from publicly accessible websites and remain the property of their owners. The task descriptions derive from public task collections under their own licences, and the labels were produced with DeepSeek models under their terms of use. The Apache licence covers Lexmount's contribution (collection, labels, annotations and structure). It grants no rights in third-party content. ## Citation ```bibtex @misc{lexmount2026webjev, title = {WebJev: Training Web Agents to Complete Real Tasks on the Live Web}, author = {{Lexmount}}, year = {2026}, howpublished = {\url{https://huggingface.co/datasets/Lexmount/WebJev}} } ``` ## Contact Lexmount, via the Lexmount organization on Hugging Face.