Post-training refers to the things done to a LLM after pre-training to improve its performance, such as supervised fine-tuning, RLHF, instruction tuning, etc. This is a critical step before releasing the LLM. Typically, this refers to things done by the company or group before releasing the LLM, not the things done afterwards to customize to a specific application. (For an example of this usage, see the GPT-4 technical report.)

• Kumar et al 2025 - LLM Post-Training: A Deep Dive into Reasoning Large Language Models
• Tie 2025 - A Survey on Post-training of Large Language Models

• Gao et al 2024 - How to Train Long-Context Language Models (Effectively) ACL version

• Alignment
• Instruction-Tuning
• RLHF