metadata
title: Post Training Techniques Guide
emoji: 🚀
colorFrom: purple
colorTo: yellow
sdk: docker
app_port: 8501
tags:
- streamlit
pinned: false
short_description: A visual guide to post-training techniques for LLMs
license: mit
🔧 Beyond Pretraining: A Visual Guide to Post-Training Techniques for LLMs
This deck summarizes the key trade-offs between different post-training strategies for large language models — including:
- 📚 Supervised Fine-Tuning (SFT)
- 🤝 Preference Optimization (DPO, APO, GRPO)
- 🎯 Reinforcement Learning (PPO)
It also introduces a reward spectrum from rule-based to subjective feedback, and compares how real-world models like SmolLM3, Tulu 2/3, and DeepSeek-R1 implement these strategies.
This is a companion resource to my ReTool rollout implementation and blog post.
📎 Download the Slides
🤝 Reuse & Attribution
This deck is free to share in talks, posts, or documentation — with attribution.
Please credit:
Jen Wei — Hugging Face 🤗 | X/Twitter
Optional citation: “Beyond Pretraining: Post-Training Techniques for LLMs (2025)”
Licensed under MIT License.
— Made with 🧠 by Jen Wei, July 2025