Vibrant Labs

The bottleneck for RL-trained LLMs isn't algorithms — it's environments. We have GRPO and compute, but no scalable way to create diverse, realistic training environments. EnterpriseSimulator explores one approach: growing RL environments from simulated worlds instead of hand-crafting them. 1. Simulate a rich world — A Smallville-style multi-agent sim where LLM-powered customers, staff, and managers interact, producing organic scenarios. 2. Mine tasks — A task miner extracts RL-ready scenarios automatically from simulation data. 3. Train via OpenEnv — Each task becomes a gym-like env with reset/step/reward. The agent interacts with simulated customers. Reward = resolution + satisfaction + efficiency. The key insight: world simulation is environment generation. You don't write scenarios — you grow them. This pattern (simulate → mine → train) could generalize to any domain.