Add README

README.md

@@ -1,20 +1,46 @@
+---
+license: mit
+task_categories:
+- reinforcement-learning
+- question-answering
+language:
+- en
+tags:
+- web-navigation
+- preference-learning
+- reward-modeling
+size_categories:
+- 10K<n<100K
+---
+
 # Web Agent Graph Dataset
 
-This dataset contains web navigation tasks in graph format with single-step negatives.
+This dataset contains web navigation tasks in graph format with single-step negatives for training reward models.
+
+## Dataset Description
+
+- **Format**: JSON Lines (JSONL)
+- **Size**: ~18K entries from 2.8K tasks
+- **Domains**: GitLab, OpenStreetMap, Reddit, Shopping, Shopping Admin
+
+## Data Format
+
+Each line in `graph_dataset.jsonl` is a JSON object with three main sections:
 
-## Format
+### graph_data
+Task metadata including domain, root_url, and goal description.
 
-Each line in `graph_dataset.jsonl` is a JSON object with:
+### node_data
+Current state information with URL, accessibility tree observation, and screenshot path.
 
-- `graph_data`: Task metadata (domain, root_url, goal)
-- `node_data`: Current state information (url, observation, screenshot path)
-- `step_data`: Action and outcome
-  - `action`: The action taken
-  - `obs`: Next state observation (axtree_txt, url)
-  - `step_number`: Step index in the trajectory
-  - `is_positive`: Boolean indicating if this is a positive (gold) or negative action
-  - `negative_type`: (Only for negatives) Type of negative: "easy_negative", "hard_negative", or "detour_negative"
-  - `negative_reason`: (Only for negatives) Explanation for why this action is negative
+### step_data
+Action and outcome with:
+- `action`: The action taken
+- `obs`: Next state observation
+- `step_number`: Step index
+- `is_positive`: True for gold actions, False for negatives
+- `negative_type`: Classification (easy_negative, hard_negative, detour_negative)
+- `negative_reason`: Explanation for negative actions
 
 ## Usage
 
@@ -25,19 +51,15 @@ import json
 with open('graph_dataset.jsonl', 'r') as f:
     for line in f:
         entry = json.loads(line)
-        # Process entry
-        print(entry['graph_data']['goal'])
-        print(entry['step_data']['action'])
-        print(entry['step_data']['is_positive'])
+        goal = entry['graph_data']['goal']
+        action = entry['step_data']['action']
+        is_positive = entry['step_data']['is_positive']
 ```
 
 ## Statistics
 
-See `conversion_stats.json` for dataset statistics including:
-- Total tasks and entries
-- Positive vs negative distribution
-- Domain breakdown
+See `conversion_stats.json` for detailed statistics.
 
-## Citation
+## License
 
-If you use this dataset, please cite the original work.
+MIT License