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Post-training-based RL intuition, from the policy-gradient theorem through PPO to modern versions like GSPO and CISPO, is essential for judging whether a new algorithm is fake or has real potential.
Roughly 25% of the book teaches how to think about RL algorithms, from the policy-gradient theorem to PPO and modern variants like GSPO and CISPO, framing this intuition as crucial for telling whether a new algorithm is fake or genuinely promising. ✦ AI generated
Nathan Lambert · Interconnects · 2026-08-10 · original ↗
By word or page count, the book is about 25% RL. This seems appropriate. If there's one thing the book is doing it's teaching people how to think about various RL algorithms. This intuition, from the policy-gradient theorem to PPO to modern versions like GSPO and CISPO, are crucial to understanding if a new algorithm is fake or has potential (no new algorithm will be proven right out of the gates).
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- ·About 25% of the book covers RL
- ·Teaches how to think about RL algorithms
- ·Policy-gradient theorem to PPO to GSPO, CISPO
- ·Crucial for judging fake vs. real algorithms
- ·No new algorithm is proven out of the gates
- ·Use intuition to assess genuine potential
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