# Vibrant Labs

- **Event:** [OpenEnv Hackathon SF](https://cerebralvalley.ai/e/openenv-hackathon-sf)
- **When:** Mar 7 at 9:00 AM – Mar 8 at 12:00 AM (PST)
- **Where:** Shack15, San Francisco, CA
- **Team:** [Jithin James](https://cerebralvalley.ai/u/jjmachan)
- **GitHub:** https://github.com/jjmachan/EnterpriseSimulator/tree/main
- **Website:** https://colab.research.google.com/github/jjmachan/EnterpriseSimulator/blob/main/notebooks/train_grpo.ipynb
- **Demo video:** https://youtu.be/qiv88w9bYY0
- **Hugging Face:** https://huggingface.co/spaces/jjmachan/enterprise-sim-support
- **Gallery:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/73

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.

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Markdown version of https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/73. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt