From the entrepreneur’s corner #81: Distillation of a Large Language Model
Distillation is a process used in developing and refining large language models (LLMs) to enhance their performance. While language models are initially trained on vast amounts of text data, further refinement, such as supervised fine-tuning or reinforcement learning, helps align the model with specific goals. For supervised fine-tuning, the model learns to imitate high-quality outputs by training on example completions. These completions often come from a more advanced, pre-existing model rather than human-generated data or the model being refined.
This technique is common in industry, especially where intellectual property and terms of service are critical. For instance, organizations like OpenAI are rumored to leverage their most powerful models, such as GPT-5 or beyond, to generate completions used in distillation. Similarly, other companies may distill knowledge from models like OpenAI’s or their proprietary systems.
Meta has also demonstrated a related approach. While not explicitly distillation, they used their 405B model as a reward system to guide model development. Distillation streamlines the process of transferring expertise from a larger, more complex model into a smaller or less resource-intensive one, making it an efficient and practical method for advancing LLM capabilities in controlled, targeted ways.
