Reinforcement Learning: The Paradigm Shift of Decentralized AI
强化学习：去中心化 AI 的范式转变
Summary
Post-training and reinforcement learning are becoming central to capability scaling. Their verification and coordination needs align naturally with decentralized compute and crypto incentives.
Why it matters
DeepSeek-R1 signalled that reinforcement learning is no longer just an alignment tool but a continuous intelligence-enhancement pathway — one Web3 is structurally suited to coordinate.
Key ideas
Pre-training, SFT and RL differ sharply in how decentralizable they are.
Verifiability and incentives make RL a natural fit for crypto coordination.
Analysis spans Prime Intellect, Gensyn, Nous, Gradient, Grail and Fraction AI.
Versions
Chinese and English versions of this report.
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