ACE-Bench: Agent Configurable Evaluation with Scalable Horizons and Controllable Difficulty under Lightweight Environments

1. 1Identify the limitations of existing agent benchmarks, such as high environment interaction overhead and imbalanced task horizon and difficulty distributions
2. 2Design a unified grid-based planning task that addresses these limitations, such as filling hidden slots in a partially completed schedule
3. 3Implement ACE-Bench with scalable horizons and controllable difficulty to evaluate agent performance
4. 4Use ACE-Bench to compare and improve agent architectures and training methods

AI researchers and engineers on a team benefit from ACE-Bench as it provides a more reliable and efficient way to evaluate agent performance, allowing them to focus on improving agent capabilities

💡 ACE-Bench provides a more efficient and reliable way to evaluate agent performance, allowing for more accurate comparisons and improvements

🤖 Introducing ACE-Bench: a new benchmark for evaluating agents with scalable horizons and controllable difficulty 🚀