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deepmind.google·4d ago
AI Engineering
Hugging Face Blog·4d ago·5 min read
Turning Database Grading into RL Rewards: Post-Training Agents with ThinkingBox
TL;DR
ThinkingBox was developed to evaluate AI agents by comparing their final database states with expected outcomes, improving task success rates significantly. For the Qwen3.8-27B model, success rates increased from 51.70% to 60.78%, demonstrating the effectiveness of this approach.
✦ Why It Matters
Implement ThinkingBox to enhance the evaluation of your AI agents and improve their performance metrics.
Key Takeaways
- Implement executable checks to assess AI agent performance.
- Use offline policy-relative difficulty to select training tasks.
- Focus on repeatable success metrics for production reliability.
- Fine-tune models with GRPO to improve task completion rates.
How It Works
ThinkingBox evaluates AI agents by comparing their actions to expected database states, using executable checks as rewards during training. This method allows the model to learn from both successes and failures, enhancing its ability to perform reliably in real-world scenarios.