Technical Notes on Modern LLM Training Methods: DPO, RLVR/GRPO, PEFT, MoE, Distillation, RLAIF, Long-Context, Synthetic Data, Model Merging, and Curriculum Learning
Year: 2026
Type: article
Abstract: This collection gathers ten short technical notes covering modern methods used to train and adapt large language models: direct preference optimization, RLVR with GRPO, parameter-efficient fine-tuning (LoRA, QLoRA, DoRA), sparse mixture-of-experts and upcycling, knowledge and self-distillation, RLAIF and constitutional AI, long-context extrapolation, synthetic data for self-improvement, model merging and capability composition, and curriculum learning for data efficiency. Each note summarizes the core ideas, trade-offs, and practical considerations of the corresponding technique.
Author: Santos, Dheiver
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7171317764
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