@rohanpaul_ai: Should your agent's training environment look like your eval set? AgentMercury says no, and shows that worlds built fro…
Summary
AgentMercury shows that training AI agents in simulated business environments generated from plain descriptions can transfer effectively to evaluation benchmarks, even if the training worlds are unrelated. The system improved performance through fine-tuning on construction traces.
View Cached Full Text
Cached at: 08/28/26, 03:34 AM
Should your agent’s training environment look like your eval set?
AgentMercury says no, and shows that worlds built from business scenarios transfer further.
Train an agent inside a fake company that has nothing to do with your benchmark, and it still gets better at your benchmark.
AgentMercury generated 4,783 simulated companies from plain business descriptions, each with its own services, tools and database, then pulled training tasks out of them afterward.
Building the worlds turned out to be learnable too. One model authored a valid company for only 3.3% of 30 unseen briefs, and 83.3% after fine-tuning on the construction traces, matching Claude Opus 4.8.
– arxiv. org/abs/2608.20634
Title: “AgentMercury: Your Agent Can Synthesize Verifiable Environments for Business Scenarios at scale”
Similar Articles
AgentMercury: Your Agent Can Synthesize Verifiable Environments for Business Scenarios at scale
AgentMercury introduces a scalable framework for synthesizing verifiable environments from business scenarios, enabling reinforcement learning agents to improve performance on enterprise and out-of-domain benchmarks.
Training Needs Trustworthy Worlds: Verified Synthetic Web Environments for Agent Learning
This paper introduces a framework for constructing verified synthetic web environments to improve the training of web agents, demonstrating enhanced performance and transferability across benchmarks.
Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Agent-World introduces a self-evolving training framework for general agent intelligence that autonomously discovers real-world environments and tasks via the Model Context Protocol, enabling continuous learning. Agent-World-8B and 14B models outperform strong proprietary models across 23 challenging agent benchmarks.
@chl260: your agent isn't the bottleneck. your environment is. frozen benchmarks stop teaching the moment the agent outgrows the…
The post argues that frozen benchmarks are inadequate for AI agent development, proposing a shift from improving agents to harnessing the environment instead.
@jeongminby98858: New paper Release paper: https://arxiv.org/abs/2608.20634 project: https://minstar.github.io/AgentMercury/index.html… h…
A new research paper introduces AgentMercury, a scalable framework for synthesizing executable environments from business scenarios, which improves agent training performance on various benchmarks.