📘 Reinforcement Learning: A Paradigm Shift for Decentralized AI Networks 🧠 Training Paradigm Pre-training builds the base; post-training is becoming the main battleground. RL is emerging as the engine for better reasoning and decisions, with post-training typically costing ~5–10% of total compute. Its needs—mass rollouts, reward-signal production, and verifiable training—map naturally to decentralized networks and blockchain primitives for coordination, incentives, and verifiable execution/settlement. ⚙️ Core Logic: “Decouple–Verify–Incentivize” 🔌 Decoupling: Outsource compute-intensive, communication-light rollouts to global long-tail GPUs; keep bandwidth-heavy parameter updates on centralized/core nodes. 🧾 Verifiability: Use ZK or Proof-of-Learning (PoL) to enforce honest computation in open networks. 💰 Incentives: Tokenized mechanisms regulate compute supply and data quality, mitigating reward gaming/overfitting.