30-Question Breakdown of LLM Post-Training: SFT, LoRA, RLHF, DPO, GRPO
techNmak · x · 2026-08-24
A comprehensive Q&A breakdown of how Large Language Models are trained and adapted after the initial pretraining phase, covering 30 key technical questions.
Key topics covered:
- SFT (Supervised Fine-Tuning): Workflows and data construction.
- Efficient Tuning: Principles and differences between LoRA and QLoRA.
- Alignment Techniques: RLHF (Reinforcement Learning from Human Feedback) and DPO (Direct Preference Optimization).
- Reasoning Enhancement: GRPO and reasoning post-training techniques.
The article systematically maps out the full technical stack from pretraining to deployment, suitable for developers seeking a deep understanding of model training mechanics.
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