README.md

metadata

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:

{
  "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

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:

# 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