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.
Social threads
Published or syndicated by
Related research
2026.09 · Intelligence Infra