Synthetic Computers at Scale for Long-Horizon Productivity Simulation

Problem
This paper addresses the gap in the literature regarding the simulation of long-horizon productivity tasks within user-specific computer environments. Existing methodologies often lack the ability to create realistic and scalable synthetic data that accurately reflects the complexities of directory structures and content-rich artifacts typical in professional settings. The authors present a preprint work that proposes a novel approach to generating these environments, which is crucial for training agents in productivity scenarios.

Method
The core technical contribution is the “Synthetic Computers at Scale” methodology, which involves creating synthetic environments that mimic real user computers. Each synthetic computer features realistic folder hierarchies and content-rich artifacts, such as documents, spreadsheets, and presentations. The methodology employs two agents: one generates user-specific productivity objectives requiring multiple deliverables over a simulated month, while the other acts as the user, navigating the filesystem, coordinating with simulated collaborators, and producing the required artifacts. The authors conducted preliminary experiments with 1,000 synthetic computers, each requiring over 8 hours of agent runtime and averaging more than 2,000 interaction turns. The training compute requirements are not explicitly disclosed, but the scalability suggests significant computational resources are necessary to generate millions of synthetic environments.

Results
The simulations yielded substantial experiential learning signals, leading to marked improvements in agent performance. The authors report significant enhancements in both in-domain and out-of-domain productivity evaluations, although specific metrics or baseline comparisons are not detailed in the abstract. The results indicate that the proposed methodology effectively supports agent learning in complex productivity tasks, suggesting a robust framework for future research.

Limitations
The authors acknowledge that their approach is still in preliminary stages, with the current experiments limited to 1,000 synthetic computers. They do not discuss potential biases in the synthetic environments or the generalizability of the agent performance improvements across diverse real-world scenarios. Additionally, the computational cost associated with scaling to millions of synthetic environments is not addressed, which may pose practical challenges for widespread adoption.

Why it matters
This work has significant implications for the development of agents capable of self-improvement in long-horizon productivity tasks. By providing a scalable framework for creating realistic synthetic environments, it opens avenues for more effective reinforcement learning strategies that can adapt to various professional contexts. The ability to simulate diverse user scenarios could enhance the training of AI systems, making them more adept at handling complex, real-world productivity challenges. This foundational substrate could lead to advancements in agentic reinforcement learning, ultimately improving the efficiency and effectiveness of AI in professional settings.

Authors: Tao Ge, Baolin Peng, Hao Cheng, Jianfeng Gao
Source: arXiv:2604.28181
URL: https://arxiv.org/abs/2604.28181v1