Post-Training of Large Language Models: A Survey

on Nlp, Survey, Llm, Post-training, Fine-tuning, Alignment

Introduction

This post examines the comprehensive survey on post-training techniques for large language models, covering methods to improve model performance, alignment, and safety after initial pre-training.

Key Topics Covered

  • Post-Training Paradigms and Methodologies
  • Supervised Fine-Tuning (SFT)
  • Reinforcement Learning from Human Feedback (RLHF)
  • Constitutional AI and AI Safety
  • Instruction Tuning
  • Alignment Techniques
  • Evaluation Metrics for Post-Trained Models
  • Challenges and Best Practices

Summary

[Add your summary and insights from the survey paper here]

References

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