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LatentGUIWorld

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jiahaolu commited on
Commit
9385a8c
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verified ·
1 Parent(s): cb9a259

Release LatentGUIWorld supervised training trajectories and images

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  1. .gitattributes +193 -0
  2. NOTICE.md +2 -0
  3. README.md +70 -9
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NOTICE.md CHANGED
@@ -9,3 +9,5 @@ scene resources and icon assets accompany the LatentGUIWorld benchmark.
9
  Rotation backgrounds originate from Open Images; source references are retained
10
  in the scene metadata where provided. Image rights remain with their respective
11
  owners.
 
 
 
9
  Rotation backgrounds originate from Open Images; source references are retained
10
  in the scene metadata where provided. Image rights remain with their respective
11
  owners.
12
+
13
+ Rotation training backgrounds include imagery from Open Images and The Metropolitan Museum of Art. Background source references are listed in `training/attribution.jsonl`.
README.md CHANGED
@@ -3,7 +3,7 @@ pretty_name: Learn from Move — LatentGUIWorld
3
  language:
4
  - en
5
  size_categories:
6
- - n<1K
7
  tags:
8
  - gui-agents
9
  - interactive-environments
@@ -39,14 +39,19 @@ configs:
39
  data_files:
40
  - split: test
41
  path: data/ten_choice_exocentric.jsonl
 
 
 
 
42
  ---
43
 
44
  # Learn from Move: LatentGUIWorld Benchmark
45
 
46
  LatentGUIWorld is the interactive GUI benchmark introduced in
47
  **Learn from Move: the Next Step for GUI Agents**. It contains **900 test episodes**
48
- across six environments, with **150 episodes per environment** and a
49
- **1280 × 720** viewport.
 
50
 
51
  [Code and environment runtime](https://github.com/ti-mm/LearnFromMove)
52
 
@@ -70,8 +75,9 @@ The two environments in each task family share 150 paired scene identities,
70
  giving 450 scene pairs and 900 episodes. The `pair_id` identifies the shared
71
  scene; the `episode_id` identifies a particular environment episode.
72
 
73
- All Hugging Face configurations expose a `test` split. The default `all`
74
- configuration combines the same six subsets into 900 rows. The `distribution`
 
75
  field identifies the 75 IID and 75 OOD episodes in each Drag environment.
76
  Rotation and Ten-Choice each use fixed test sets without an IID/OOD subdivision,
77
  so their `distribution` values are `null`.
@@ -83,7 +89,7 @@ from the 80 training messages. Rotation uses 150 test background images
83
  disjoint from the 1,000 training backgrounds. Ten-Choice and Rotation therefore
84
  evaluate held-out content across their full test sets.
85
 
86
- ## Load episode records
87
 
88
  ```python
89
  import json
@@ -109,6 +115,53 @@ The runtime consumes the full configuration, including hidden dynamics and
109
  success criteria. Agent observations consist of task instructions, screenshots,
110
  and the structured metadata selected by the runtime's observation contract.
111
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
112
  ## Environment interaction
113
 
114
  This dataset contains scene configurations, rendering assets, and the Ten-Choice
@@ -124,6 +177,7 @@ snapshot_download(
124
  repo_id="OpenMOSS-Team/LearnFromMove",
125
  repo_type="dataset",
126
  local_dir="benchmark",
 
127
  )
128
  ```
129
 
@@ -155,6 +209,12 @@ cases/ten_choice/*/meta.json
155
  cases/ten_choice/*/index.html
156
  cases/ten_choice/*/assets/icon.svg
157
  assets/rotation/*.jpg
 
 
 
 
 
 
158
  ```
159
 
160
  The JSONL files provide a browsable view of the episodes in `manifest.json`.
@@ -163,6 +223,7 @@ Scene paths in both representations resolve against the dataset root.
163
  ## Attribution
164
 
165
  The original release license is included in [LICENSE](LICENSE).
166
- Rotation backgrounds originate from Open Images; their source references are
167
- retained in scene metadata, and image rights remain with their respective
168
- owners. See [NOTICE.md](NOTICE.md).
 
 
3
  language:
4
  - en
5
  size_categories:
6
+ - 100K<n<1M
7
  tags:
8
  - gui-agents
9
  - interactive-environments
 
39
  data_files:
40
  - split: test
41
  path: data/ten_choice_exocentric.jsonl
42
+ - config_name: sft
43
+ data_files:
44
+ - split: train
45
+ path: training/data/*.jsonl.gz
46
  ---
47
 
48
  # Learn from Move: LatentGUIWorld Benchmark
49
 
50
  LatentGUIWorld is the interactive GUI benchmark introduced in
51
  **Learn from Move: the Next Step for GUI Agents**. It contains **900 test episodes**
52
+ across six environments, with **150 episodes per environment**, and
53
+ **24,000 training trajectories** yielding **154,713 SFT examples**.
54
+ Observations use a **1280 × 720** viewport.
55
 
56
  [Code and environment runtime](https://github.com/ti-mm/LearnFromMove)
57
 
 
75
  giving 450 scene pairs and 900 episodes. The `pair_id` identifies the shared
76
  scene; the `episode_id` identifies a particular environment episode.
77
 
78
+ The six environment configurations expose a `test` split. The default `all`
79
+ configuration combines these subsets into 900 test rows. The `sft` configuration
80
+ exposes the supervised examples through a `train` split. The `distribution`
81
  field identifies the 75 IID and 75 OOD episodes in each Drag environment.
82
  Rotation and Ten-Choice each use fixed test sets without an IID/OOD subdivision,
83
  so their `distribution` values are `null`.
 
89
  disjoint from the 1,000 training backgrounds. Ten-Choice and Rotation therefore
90
  evaluate held-out content across their full test sets.
91
 
92
+ ## Load test episode records
93
 
94
  ```python
95
  import json
 
115
  success criteria. Agent observations consist of task instructions, screenshots,
116
  and the structured metadata selected by the runtime's observation contract.
117
 
118
+ ## Training data
119
+
120
+ The training set contains 4,000 trajectories per environment. Each SFT example
121
+ uses up to three recent screenshots and preceding assistant responses to predict
122
+ one Think/action response.
123
+
124
+ | Environment | Trajectories | SFT examples |
125
+ |---|---:|---:|
126
+ | Egocentric Drag | 4,000 | 24,452 |
127
+ | Exocentric Drag | 4,000 | 16,000 |
128
+ | Inner Rotation | 4,000 | 28,479 |
129
+ | Outer Rotation | 4,000 | 28,349 |
130
+ | Egocentric Ten-Choice | 4,000 | 31,169 |
131
+ | Exocentric Ten-Choice | 4,000 | 26,264 |
132
+ | **Total** | **24,000** | **154,713** |
133
+
134
+ ```python
135
+ from datasets import load_dataset
136
+
137
+ train = load_dataset("OpenMOSS-Team/LearnFromMove", "sft", split="train")
138
+ ```
139
+
140
+ Training rows contain `messages`, `images`, `tools`, and `metadata`.
141
+ `messages` contains the input conversation and target assistant response.
142
+ `images` contains dataset-relative paths and the image pixel limit.
143
+ `metadata` identifies the trajectory, environment, action index, and row index.
144
+ The target consists of a Think rationale followed by a JSON action.
145
+
146
+ Images are rendered by the accompanying environment runtime and stored as
147
+ lossless WebP files in tar archives (47.0 GiB). Install the GitHub package and prepare the
148
+ training files with:
149
+
150
+ ```bash
151
+ pip install -e '.[data]'
152
+ latentguiworld-prepare-training --download --dataset-root data/LatentGUIWorld \
153
+ --output data/training/benchmark/train.parquet
154
+ ```
155
+
156
+ Run these commands from the code repository. The preparation command downloads
157
+ and extracts the image archives and resolves local image paths in the output
158
+ Parquet. Pass that file to the `benchmark_sft` training recipe.
159
+
160
+ `training/trajectories.jsonl.gz` provides complete trajectories with task
161
+ instructions, ordered observations, and assistant responses. The final
162
+ observation follows the final action. `training/dataset.json` indexes the SFT
163
+ shards and image archives.
164
+
165
  ## Environment interaction
166
 
167
  This dataset contains scene configurations, rendering assets, and the Ten-Choice
 
177
  repo_id="OpenMOSS-Team/LearnFromMove",
178
  repo_type="dataset",
179
  local_dir="benchmark",
180
+ ignore_patterns=["training/**"],
181
  )
182
  ```
183
 
 
209
  cases/ten_choice/*/index.html
210
  cases/ten_choice/*/assets/icon.svg
211
  assets/rotation/*.jpg
212
+ training/data/*.jsonl.gz # Hugging Face SFT rows
213
+ training/data/*.parquet # Training Parquet shards
214
+ training/archives/*.tar # Lossless observation images
215
+ training/trajectories.jsonl.gz # Complete demonstration trajectories
216
+ training/dataset.json # Training data index
217
+ training/attribution.jsonl # Training background source references
218
  ```
219
 
220
  The JSONL files provide a browsable view of the episodes in `manifest.json`.
 
223
  ## Attribution
224
 
225
  The original release license is included in [LICENSE](LICENSE).
226
+ Rotation backgrounds originate from Open Images and, for 42 training
227
+ backgrounds, The Metropolitan Museum of Art. Source references are included in
228
+ scene metadata and `training/attribution.jsonl`. Image rights remain with their
229
+ respective owners. See [NOTICE.md](NOTICE.md).
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