WebArena / README.md
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metadata
license: other
tags:
  - cua-lite
  - gui
  - sft
task_categories:
  - image-text-to-text
configs:
  - config_name: default
    data_files:
      - split: validation
        path:
          - '*/*/validation*parquet'
          - '*/*/validation/*.parquet'
          - '*/*/validation/*/*.parquet'
  - config_name: Qwen3-VL-32B-Instruct
    data_files:
      - split: validation
        path:
          - '*/*/validation/Qwen3-VL-32B-Instruct.parquet'
          - '*/*/validation/Qwen3-VL-32B-Instruct/*.parquet'
  - config_name: Qwen3.5-27B
    data_files:
      - split: validation
        path:
          - '*/*/validation/Qwen3.5-27B.parquet'
          - '*/*/validation/Qwen3.5-27B/*.parquet'
  - config_name: Qwen3.5-9B
    data_files:
      - split: validation
        path:
          - '*/*/validation/Qwen3.5-9B.parquet'
          - '*/*/validation/Qwen3.5-9B/*.parquet'
  - config_name: gpt-5.5
    data_files:
      - split: validation
        path:
          - '*/*/validation/gpt-5.5.parquet'
          - '*/*/validation/gpt-5.5/*.parquet'

cua-lite/WebArena

WebArena web-use agent rollout trajectories staged from ~/cua-lite-all-agents/.exps/eval/browsergym.webarena/2026-07-19T01-32_07f599ab7a/run_0. The HF validation split contains one config per model.

Origin

Load via datasets

from datasets import load_dataset

# entire dataset
ds = load_dataset("cua-lite/WebArena")

# just one named subset (config)
ds = load_dataset("cua-lite/WebArena", "Qwen3-VL-32B-Instruct")

You can also filter by metadata.platform / metadata.task_type / metadata.others.* after loading; every row carries a rich metadata struct (see schema below).

Schema

Each row has these columns:

column type notes
images list[Image] embedded PNG/JPEG bytes; HF viewer renders thumbnails
messages list[struct] OpenAI-style turns with role + structured content
metadata struct {platform, task_type, extra_tool_schemas, valid_actions, others{...}}

Coordinate values in messages are normalized to [0, 1000] integers.

Image-dedup (grounding.* / understanding cohorts). These cohorts are single-image-per-row and many rows share the same screenshot, so to avoid re-embedding identical image bytes once per instruction they are stored folded: one row per unique screenshot (image embedded once), carrying an extra _folded column — a JSON string with the authoritative list of {messages, metadata} members for that screenshot. The row's top-level messages is the members concatenated for viewer convenience. use cohorts are not folded. Use lite.data.hf.download to consume this repo — it unfolds automatically back to one row per instruction; reading the parquet directly yields the folded form.

Layout

<platform>/<task_type>/<split>/shard-NNNNN-of-NNNNN.parquet                  # single-variant cohort
<platform>/<task_type>/<split>/<variant>/shard-NNNNN-of-NNNNN.parquet        # multi-variant cohort
  • platform ∈ {desktop, mobile, web}
  • task_type ∈ {understanding, grounding.action, grounding.point, grounding.bbox, use} — used verbatim as the dir component
  • HF config names are <platform>.<task_type> by default (e.g. mobile.grounding.action) — UNLESS the dataset was staged with --config-names, which sets verbatim, explicitly-chosen config names (see the configs: block above for the authoritative list). The agent registry lookup key in code is <agent>@<platform>@<task_type> (e.g. qwen3_vl@mobile@grounding.action); only this user-facing token uses . between platform and task_type, because @ triggers a 403 on the dataset-viewer's signed image URLs.
  • HF split names stay train / validation (the datasets library blacklists <>:/\|?* in split names; everything else is fine in config_name)
  • validation is an in-distribution held-out slice (never used in training); test is reserved for out-of-distribution benchmark datasets

Stats

platform task_type variant train validation
web use Qwen3-VL-32B-Instruct 0 812
web use Qwen3.5-27B 0 812
web use Qwen3.5-9B 0 812
web use gpt-5.5 0 792

Local mirror & SFT export

For local workflows (SFT export, dedup, mixing across datasets), use lite.data.hf.download to mirror this repo back to the canonical local layout:

$CUA_LITE_DATASETS_ROOT/cua-lite/WebArena/
  images/<hash[:2]>/<hash>.<ext>                          # content-addressed image store
  <platform>/<task_type>/<split>[/<variant>].parquet      # rows reference images by relative path

Rows in the local parquet have images: list[str]; bytes are extracted to the image store. lite.train.export.export_sft consumes the local form directly with --image-root=$CUA_LITE_DATASETS_ROOT.

  • Total unique images: 33,676
  • Image store size: 3.47 GB

Notes

Staged via lite.data.hf.stage from rollout log-roots: /home/haoranliu/cua-lite-all-agents/.exps/eval/browsergym.webarena/2026-07-19T01-32_07f599ab7a/run_0/Qwen_Qwen3-VL-32B-Instruct, /home/haoranliu/cua-lite-all-agents/.exps/eval/browsergym.webarena/2026-07-19T01-32_07f599ab7a/run_0/Qwen_Qwen3.5-27B, /home/haoranliu/cua-lite-all-agents/.exps/eval/browsergym.webarena/2026-07-19T01-32_07f599ab7a/run_0/Qwen_Qwen3.5-9B, /home/haoranliu/cua-lite-all-agents/.exps/eval/browsergym.webarena/2026-07-19T01-32_07f599ab7a/run_0/gpt-5.5 (row filter: none).

License & citation

other