Large Language Model (LLM) Training Algorithm

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A Large Language Model (LLM) Training Algorithm is a deep neural model training algorithm that can be implemented by an LLM training system (optimizes large language model parameters) to support LLM training tasks.



References

  • (Kumar et al., 2025) ⇒ Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip H. S. Torr, Salman Khan, and Fahad Shahbaz Khan. (2025). “LLM Post-Training: A Deep Dive Into Reasoning Large Language Models.” doi:10.48550/arXiv.2502.21321
    • NOTES:
      1. **Post-Training Algorithm Taxonomy**: The paper establishes a clear taxonomy of post-training algorithms (Figure 1), demonstrating how LLM training algorithms extend beyond initial pre-training to include fine-tuning (SFT), reinforcement learning (PPO, DPO, GRPO), and test-time scaling—showcasing the complete optimization lifecycle for LLM parameters.
      2. **Parameter-Efficient Training Algorithms**: The paper's coverage of LoRA, QLoRA, and adapter methods (Section 4.7 and Table 2) illustrates how modern LLM training algorithms can optimize selective subsets of parameters rather than all weights, directly confirming your wiki's categorization of "Parameter-Efficient Training Algorithms."
      3. **Reinforcement Learning for Sequential Decision-Making**: The paper's explanation of how RL algorithms (Sections 3.1-3.2) adapt to token-by-token generation frames LLM training as a sequential decision process with specialized advantage functions and credit assignment mechanisms—extending beyond the traditional gradient descent approaches in your wiki.
      4. **Process vs. Outcome Reward Optimization**: The comparison between Process Reward Models and Outcome Reward Models (Section 3.1.3-3.1.4) demonstrates a unique aspect of LLM training algorithms not explicitly covered in your wiki: optimization can target either intermediate reasoning steps or final outputs.
      5. **Hybrid Training-Inference Algorithms**: The paper's extensive coverage of test-time scaling methods (Section 5) reveals that modern LLM training algorithms can span the traditional training-inference boundary, with techniques like Monte Carlo Tree Search and Chain-of-Thought representing algorithmic approaches that continue model optimization during deployment.