DSWorld: A Data Science World Model for Efficient Autonomous Agents
Researchers present DSWorld, a framework that models data science execution environments to predict state transitions before actual execution. DSWorld integrates structured state construction, cost-aware routing, lightweight execution, and an LLM-based simulator, enabling reinforcement learning-based agent training to be accelerated by approximately 14x and search-based inference by 3-6x. The framework also outperforms the strongest LLM baseline by 35.6% on transition prediction tasks.
Why it matters: This work offers a substantial reduction in computational cost for autonomous data science agents, improving their efficiency and practicality for real-world applications.
Full story at: arXiv AI/ML ↗