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

MarkTechPost / 5/2/2026

💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage
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Key Points

  • The article provides a hands-on coding tutorial for post-training large language models using the TRL (Transformer Reinforcement Learning) library ecosystem.
  • It walks through a staged workflow starting from a lightweight base model and applying Supervised Fine-Tuning (SFT) first.
  • It then covers Reward Modeling (RM) to shape the training objective before moving to preference- and group-based optimization methods.
  • The tutorial explains how to apply Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) reasoning-based training techniques.
  • Overall, it presents a complete practical progression of LLM post-training methods from SFT through DPO/GRPO within a single guide.

In this tutorial, we walk through a complete, hands-on journey of post-training large language models using the powerful TRL (Transformer Reinforcement Learning) library ecosystem. We start from a lightweight base model and progressively apply four key techniques: Supervised Fine-Tuning (SFT), Reward Modeling (RM), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO). Also, we […]

The post A Coding Guide on LLM Post Training with TRL from Supervised Fine Tuning to DPO and GRPO Reasoning appeared first on MarkTechPost.

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