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

This article provides a comprehensive guide on post-training techniques for Large Language Models (LLMs) using the TRL library. The focus is on four key methods: Supervised Fine-Tuning (SFT), Reward Modeling (RM), Direct Preference Optimization (DPO), and Group Reasoning Post-Optimization (GRPO). Here's a summary of each method discussed:

1. Supervised Fine-Tuning (SFT):

   • This involves training an LLM to generate responses based on human-labeled data.
   • The goal is to align the model’s outputs with desired behaviors through structured learning.

2. Reward Modeling (RM):

   • After SFT, RM trains a separate reward model that evaluates generated text according to human preferences.
   • This helps in understanding and quantifying human preferences for different types of responses.

3. Direct Preference Optimization (DPO):

   • DPO optimizes the LLM directly using preference data without needing a separate reward model.
   • It uses a low learning rate and controls divergence with a beta parameter to fine-tune the model efficiently.

4. Group Reasoning Post-Optimization (GRPO):

   • GRPO generates multiple responses for each prompt and

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