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LLM
Post-training
Cover & TOC
Foundations & framing
  • 01 What is post-training?
  • 02 Probability, policies & gradients
  • 03 From next-token to behavior
  • 04 The alignment problem
Instruction tuning & supervised fine-tuning
  • 05 Instruction tuning is born
  • 06 The SFT stage in practice
  • 07 Synthetic & self-generated data
RLHF & the preference era
  • 08 Learning from human preferences
  • 09 Optimizing against the reward
  • 10 RLHF scales to language
  • 11 Reward models
  • 12 RLAIF & Constitutional AI
RL fundamentals & PPO
  • 13 Policy gradients & REINFORCE
  • 14 Value, advantage, baselines
  • 15 TRPO to PPO
  • 16 PPO for RLHF in practice
Offline & direct preference optimization
  • 17 Reward hacking & over-optimization
  • 18 Direct Preference Optimization
  • 19 The DPO zoo
  • 20 Rejection-sampling alignment
RLVR & the reasoning era
  • 21 Bootstrapping reasoning
  • 22 Process vs outcome rewards
  • 23 Inference scaling & o1
  • 24 RL from verifiable rewards
  • 25 GRPO & DeepSeek-R1
Modern algorithms, agentic RL & the frontier
  • 26 GRPO refinements
  • 27 Scaling open post-training
  • 28 Agentic & tool-use RL
  • 29 On-policy distillation
  • 30 RL scaling laws
  • 31 Generative reward models
  • 32 Recap
§ Glossary
Section 23

Inference scaling & o1

Test-time compute and the reasoning model

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Process vs outcome rewards
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RL from verifiable rewards
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