# vectorblock.io

- **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:** [Suvasis Mukherjee](https://cerebralvalley.ai/u/suvasis)
- **GitHub:** https://github.com/dronomyio/hackathon_opendev.git
- **Hugging Face:** https://huggingface.co/spaces/openenv-community/chessecon?logs=build
- **Gallery:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/69

ChessEcon is a live multi-agent reinforcement learning environment where two LLM agents — Qwen 2.5-0.5B (White, trainable) and Llama 3.2-1B (Black, frozen) — compete at chess for real economic stakes. Every game deducts entry fees, awards prize pools, and updates agent wallets, creating a closed economy where strategic play has financial consequences.
White trains in real-time using GRPO (Group Relative Policy Optimization) with LoRA adapters, learning from game outcomes as rewards. After 138 training steps, White achieves a 96.4% win rate with wallet growth from 100 to +1,104 units — demonstrating that a 0.5B model can learn dominant chess strategy purely from economic reward signals.
The system exposes an OpenEnv 0.1 compliant REST + WebSocket API, making it pluggable by any external agent. A live React dashboard streams games, GRPO metrics, and wallet balances in real-time at hackathon.adaboost.io, running on 4× RTX 3070 GPUs via Cloudflare Tunnel.

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Markdown version of https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/69. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt