Post Training LLM

Preview Link: https://drive.google.com/file/d/1eA2sGYUc0uWgfLtKb3k0pFgAgTwEiZjM/view?usp=sharing

Modern AI capability isn't just pretrained—it is engineered in post-training. While pre-training builds document continuers, post-training builds usable products. However, post-training is fraught with subtle bugs: chat template divergence, lost end-of-turn gradient loss, reward overoptimization, and likelihood displacement.

Cracking LLM Post-Training System Design Interviews teaches the complete post-training stack in the order you actually build it: Evaluation arrives in Chapter 4, before any optimization chapter. Because an optimization loop with a noisy or gameable objective produces nothing but confident garbage.

Every chapter follows a strict contract:

• Mathematical Rigor: Full derivations of objective functions across SFT, DPO, GRPO, DAPO, and SimPO with every term explained
• Architecture & Trade-Offs: Multi-tier hardware breakdowns (from Apple M-series Macs to 8-node H100 clusters), memory arithmetic in gigabytes, and token throughput formulas
• First-Class Failure Modes: Equal billing for entropy collapse, likelihood displacement, reward hacking, advantage collapse, and alignment tax—complete with symptom, root cause, diagnostic metric, and intervention.
• Runnable Code & Labs: Fully functional code tied directly to the companion posttrain repository and a Streamlit UI harness.
• Interview Masterclass: Exactly 5 frontier-lab interview questions per chapter with exhaustive answers spanning mathematical derivations, production debugging, cost arithmetic, and case studies.