README.md

---
license: mit
task_categories:
- reinforcement-learning
- question-answering
language:
- en
tags:
- web-navigation
- preference-learning
- reward-modeling
size_categories:
- 1K<n<10K
---

# Web Agent Grouped Graph Dataset

This dataset contains web navigation tasks in grouped graph format with full history and candidate actions for training reward models.

## Dataset Description

- **Format**: JSON Lines (JSONL) - One entry per step with grouped candidates
- **Size**: ~2.8K step entries from 2.8K tasks
- **Domains**: GitLab, OpenStreetMap, Reddit, Shopping, Shopping Admin

## Data Format

Each line in `graph_dataset.jsonl` represents a single step with all candidate actions grouped together:

```json
{
  "task_id": "...",
  "goal": "Find product X and add to cart",
  "domain": "shopping",
  "step_index": 3,
  
  "history": [
    {"state_id": "S0", "screenshot": "...", "url": "...", "obs": "..."},
    {"state_id": "S1", "screenshot": "...", "url": "...", "obs": "..."},
    {"state_id": "S2", "screenshot": "...", "url": "...", "obs": "..."},
    {"state_id": "S3", "screenshot": "...", "url": "...", "obs": "..."}
  ],
  
  "current_state": {
    "state_id": "S3",
    "screenshot": "path/to/current.png",
    "url": "http://...",
    "obs": "accessibility tree..."
  },
  
  "candidates": [
    {
      "label": "gold",
      "action": "click('153')",
      "next_state": "S4",
      "next_screenshot": "path/to/next.png",
      "next_url": "http://...",
      "next_obs": "..."
    },
    {
      "label": "negative",
      "action": "click('88')",
      "negative_type": "hard_negative",
      "reason": "Leads to wrong page",
      "next_state": "S_bad1",
      "next_screenshot": "path/to/bad1.png",
      "next_url": "http://...",
      "next_obs": "..."
    }
  ]
}
```

## Key Features

- **Full History**: Complete trajectory up to current state
- **Grouped Candidates**: All possible actions (gold + negatives) from same state
- **Next-State Screenshots**: Screenshot paths for all candidate outcomes
- **Rich Metadata**: Task ID, domain, goal, step index
- **Negative Types**: Classification (easy_negative, hard_negative, detour_negative)

## Usage

```python
import json

# Load dataset
with open('graph_dataset.jsonl', 'r') as f:
    for line in f:
        entry = json.loads(line)
        
        # Access task info
        goal = entry['goal']
        step_idx = entry['step_index']
        
        # Access history
        history = entry['history']
        current_state = entry['current_state']
        
        # Process candidates
        for candidate in entry['candidates']:
            if candidate['label'] == 'gold':
                print(f"Gold action: {candidate['action']}")
            else:
                print(f"Negative: {candidate['action']} ({candidate['negative_type']})")
```

## Training Reward Models

This format is ideal for:
1. **Preference Learning**: Compare gold vs negative actions from same state
2. **Reward Modeling**: Predict which action leads to goal completion
3. **Action Ranking**: Rank all candidates by predicted reward

Example:
```python
# For each step entry
gold = [c for c in entry['candidates'] if c['label'] == 'gold'][0]
negatives = [c for c in entry['candidates'] if c['label'] == 'negative']

# Train model to rank gold higher than all negatives
reward_gold = model(entry['current_state'], gold)
rewards_neg = [model(entry['current_state'], neg) for neg in negatives]
```

## Statistics

See `conversion_stats.json` for detailed statistics.

## License

MIT License