Distillation (AI)

Extracted Attributes

• Type

  Machine Learning Technique

• Benefits

  Efficiency, cost-effectiveness, smaller model size, inference optimization, model compression

• Field of Study

  Computer Science, Machine Learning, Deep Learning

• Primary Purpose

  Transferring knowledge from large, complex models to smaller, more efficient models

• Alternative Names

  Knowledge Distillation, Model Distillation

• Application Areas

  Deep learning, speech recognition, image recognition, natural language processing, chain-of-thought reasoning models, classification tasks

• Related Techniques

  Quantization, pruning, low-rank factorization, fine-tuning, RAG, dataset distillation

Timeline

• Knowledge distillation was first successfully demonstrated by Bucilua and collaborators. (Source: Web Search Results)

  2006

• Research into distillation as a tool in AI dates back approximately a decade. (Source: Web Search Results)

  2015

• The NovaSky lab at the University of California, Berkeley, showed that distillation works well for training chain-of-thought reasoning models. (Source: Web Search Results)

  2024-01

OpenAI

OpenAI, Inc. is an American artificial intelligence (AI) organization founded in December 2015 and headquartered in San Francisco, California. It aims to develop "safe and beneficial" artificial general intelligence (AGI), which it defines as "highly autonomous systems that outperform humans at most economically valuable work". As a leading organization in the ongoing AI boom, OpenAI is known for the GPT family of large language models, the DALL-E series of text-to-image models, and a text-to-video model named Sora. Its release of ChatGPT in November 2022 has been credited with catalyzing widespread interest in generative AI. The organization has a complex corporate structure. As of April 2025, it is led by the non-profit OpenAI, Inc., registered in Delaware, and has multiple for-profit subsidiaries including OpenAI Holdings, LLC and OpenAI Global, LLC. Microsoft has invested US$13 billion in OpenAI, and is entitled to 49% of OpenAI Global, LLC's profits, capped at an estimated 10x their investment. Microsoft also provides computing resources to OpenAI through its cloud platform, Microsoft Azure. In 2023 and 2024, OpenAI faced multiple lawsuits for alleged copyright infringement against authors and media companies whose work was used to train some of OpenAI's products. In November 2023, OpenAI's board removed Sam Altman as CEO, citing a lack of confidence in him, but reinstated him five days later following a reconstruction of the board. Throughout 2024, roughly half of then-employed AI safety researchers left OpenAI, citing the company's prominent role in an industry-wide problem.

Web Search Results

• How Distillation Makes AI Models Smaller and Cheaper

  But distillation, also called knowledge distillation, is a widely used tool in AI, a subject of computer science research going back a decade and a tool that big tech companies use on their own models. “Distillation is one of the most important tools that companies have today to make models more efficient,” said Enric Boix-Adsera (opens a new tab), a researcher who studies distillation at the University of Pennsylvania’s Wharton School. Dark Knowledge ------------------ [...] distillation worked in this setting,” said Dacheng Li, (opens a new tab) a Berkeley doctoral student and co-student lead of the NovaSky team. “Distillation is a fundamental technique in AI.” [...] Meanwhile, other researchers continue to find new applications. In January, the NovaSky lab at the University of California, Berkeley, showed that distillation works well for training chain-of-thought reasoning models (opens a new tab), which use multistep “thinking” to better answer complicated questions. The lab says its fully open-source Sky-T1 model cost less than $450 to train, and it achieved similar results to a much larger open-source model. “We were genuinely surprised by how well

• A pragmatic introduction to model distillation for AI developers

  Still, model distillation presents an exciting frontier in the AI world, offering a blend of efficiency and performance. As AI developers, integrating distillation into your workflows can lead to more efficient, cost-effective, and innovative AI solutions. The future of AI is not just in building larger and larger models. The future will be about developing more intelligent applications requiring smaller and smarter task-specific models. Model distillation is a key step in that direction. [...] If you’re an AI developer struggling to adopt Large Language or Large Vision models and trying to understand where model distillation fits in with other techniques like RAG, fine-tuning, and dataset distillation, this is the guide for you. ## Introduction ### A Brief Overview Model distillation, also known as knowledge distillation, is a technique that focuses on creating efficient models by transferring knowledge from large, complex models to smaller, deployable ones. [...] The ultimate purpose of model distillation is making models more inference-optimized as a form of model compression (without significant loss in performance and accuracy within the domain area of interest), whereas the focus of fine-tuning is improving task specific performance (with model size being relatively irrelevant). In addition to knowledge distillation, other compression and acceleration methods like quantization, pruning, and low-rank factorization are also employed.

• Knowledge distillation - Wikipedia

  In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have more knowledge capacity than small models, this capacity might not be fully utilized. It can be just as computationally expensive to evaluate a model even if it utilizes little of its knowledge capacity. Knowledge distillation transfers knowledge from a large model [...] Wikidata item Appearance move to sidebar hide From Wikipedia, the free encyclopedia Machine learning method to transfer knowledge from a large model to a smaller one [...] External links -------------- \[edit\] Distilling the knowledge in a neural network – Google AI Retrieved from " Category: Deep learning Hidden categories: CS1: long volume value Articles with short description Short description matches Wikidata

• LLM distillation demystified: a complete guide | Snorkel AI

  While a lack of _labeled_ data bottlenecks many AI projects, distillation can be bottlenecked by a lack of _unlabeled_ data. Imagine, for example, that you want a model to classify contract clauses into a dozen categories, but you have very few raw examples to train from. In a strict LLM to classifier model use case, you likely couldn’t get the performance you need. [...] However, the distilling step-by-step approachdeveloped by researchers at Google and Snorkel AI allows data scientists to fine-tune a small generative model for classification tasks on as little as one-eighth as much data as traditional fine-tuning would require. [...] LLM distillation positions a large generative model as a “teacher” and the smaller model as a “student.” The student model could be a simple model like logistic regression or a foundation modellike BERT. In the most basic version of distillation, data scientists start with unlabeled data and ask the LLM to label it. Data scientists then use the synthetically labeled data to train the “student” model, which will mirror the “teacher” model’s performance on the task defined by the original data

• Knowledge Distillation: Principles, Algorithms, Applications

  Knowledge distillation refers to the process of transferring the knowledge from a large unwieldy model or set of models to a single smaller model that can be practically deployed under real-world constraints. Essentially, it is a form of model compression that was first successfully demonstrated by Bucilua and collaborators in 2006 . [...] Knowledge distillation is performed more commonly on neural network models associated with complex architectures including several layers and model parameters. Therefore, with the advent of deep learning in the last decade, and its success in diverse fields including speech recognition, image recognition, and natural language processing, knowledge distillation techniques have gained prominence for practical real-world applications . [...] Offline distillation is the most common method, where a pre-trained teacher model is used to guide the student model. In this scheme, the teacher model is first pre-trained on a training dataset, and then knowledge from the teacher model is distilled to train the student model. Given the recent advances in deep learning, a wide variety of pre-trained neural network models are openly available that can serve as the teacher depending on the use case. Offline distillation is an established