A Coding Guide on LLM Post Training with TRL from Supervised Fine Tuning to DPO and GRPO Reasoning

A hands-on tutorial shows how to post-train a small Qwen model with TRL on limited hardware.

The workflow covers supervised fine-tuning, reward modeling, direct preference optimization, and GRPO.

LoRA and PEFT are used to keep training efficient and low-cost, even on a Google Colab T4.

The piece highlights a practical pipeline for aligning language models with instructions, preferences, and verifiable rewards.