vectorblock.io

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.