--- title: How Big Models Teach Small Models to Be Smart | daily.dev description: Knowledge distillation trains a small 'student' model to replicate the behavior of a large 'teacher' model, producing a separate, deployable model rather than... canonical: https://daily.dev/posts/how-big-models-teach-small-models-to-be-smart-3wi68af3j twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: How Big Models Teach Small Models to Be Smart | daily.dev og:description: Knowledge distillation trains a small 'student' model to replicate the behavior of a large 'teacher' model, producing a separate, deployable model rather than... og:url: https://daily.dev/posts/how-big-models-teach-small-models-to-be-smart-3wi68af3j og:image: https://api.daily.dev/og/posts/3Wi68AF3j.png og:image:alt: How Big Models Teach Small Models to Be Smart og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # How Big Models Teach Small Models to Be Smart **[ByteByteGo](https://daily.dev/sources/bytebytego)** · 12 min read · 9 upvotes · 0 comments ## Summary Knowledge distillation trains a small 'student' model to replicate the behavior of a large 'teacher' model, producing a separate, deployable model rather than a compressed version. The key mechanism is soft labels — the teacher's full probability distribution over outputs — which carry richer information than hard labels, enabling the student to learn relational structure between categories. Three main methods exist: output distillation (matching final probabilities), feature distillation (matching internal representations), and synthetic data distillation (fine-tuning on teacher-generated examples), with the last being most common due to API-only access to many models. Real-world results like DeepSeek's 7B model outperforming a 32B model on math benchmarks show strong but narrow gains. Key limits include the teacher setting a performance ceiling, capacity gaps between teacher and student hurting transfer, architecture mattering more than size, and unintended traits being passed from teacher to student. The newest direction automates the entire distillation loop — data generation, fine-tuning, and evaluation — with the teacher model driving the process end-to-end. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How did DeepSeek's distilled 7B model outperform a 32B model on math benchmarks? A DeepSeek 7-billion-parameter student model, fine-tuned on training examples generated by a large reasoning model, scored higher than a 32-billion-parameter model on a competition mathematics benchmark. The distilled family ranged from 1.5B to 70B parameters, with smaller versions compact enough to run on a single graphics card, though such gains are typically limited to narrow tasks like math and code rather than general knowledge. _Comparing model sizes against real benchmark results is easier when tracking distillation research on daily.dev._ ### What is the difference between knowledge distillation and model compression like quantization or pruning? Compression methods such as quantization and pruning shrink an existing model by lowering numeric precision or removing low-contribution parts, so the result is still the same model, just smaller. Distillation instead trains a completely separate model with its own parameters to behave like a larger teacher model; the two techniques are often combined, distilling first and then quantizing the resulting smaller model. _Understanding this distinction helps when deciding how to shrink models for production on daily.dev._ ### Why can training a student model on a teacher's soft labels work better than training on hard labels? Soft labels are the full set of probabilities a teacher model assigns across possible answers, such as cat at 0.70, dog at 0.25, fox at 0.05, rather than a single correct label. This extra structure, sometimes called dark knowledge, shows how categories relate to each other, giving the student a stronger training signal and letting it reach good performance from far fewer examples than training on plain labels alone. _Weighing training signal choices like this matters for anyone building smaller models, a topic worth following on daily.dev._ ## Similar posts on daily.dev - [A Gentle Introduction to Model Distillation](https://daily.dev/posts/a-gentle-introduction-to-model-distillation-mzqzy6iaj) · Machine Learning Mastery · 49 upvotes · 2 comments - [​Implement Knowledge Distillation from Scratch​](https://daily.dev/posts/implement-knowledge-distillation-from-scratch--vieqsxirg) · Daily Dose of Data Science \| Avi Chawla \| Substack · 1 upvotes · 0 comments - [What 'distilling' an AI model actually means and why it matters to self-hosting open LLMs](https://daily.dev/posts/what-distilling-an-ai-model-actually-means-and-why-it-matters-to-self-hosting-open-llms-mdfuhkoy8) · XDA Developers · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#deep-learning](https://daily.dev/tags/deep-learning) [View this post on daily.dev](https://daily.dev/posts/how-big-models-teach-small-models-to-be-smart-3wi68af3j) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"How Big Models Teach Small Models to Be Smart","url":"https://daily.dev/posts/how-big-models-teach-small-models-to-be-smart-3wi68af3j","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/how-big-models-teach-small-models-to-be-smart-3wi68af3j"},"datePublished":"2026-08-05T15:34:38.144Z","dateModified":"2026-09-14T07:28:57.356Z","description":"Knowledge distillation trains a small 'student' model to replicate the behavior of a large 'teacher' model, producing a separate, deployable model rather than...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/45c95e90426ebb9039592883b840d831?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/45c95e90426ebb9039592883b840d831?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"ByteByteGo","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"ByteByteGo","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/35be29234ee14d01a9cd049c52e12753","url":"https://daily.dev/sources/bytebytego"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/how-big-models-teach-small-models-to-be-smart-3wi68af3j","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":9},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,llm,deep-learning","timeRequired":"PT12M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"ByteByteGo","item":"https://daily.dev/sources/bytebytego"},{"@type":"ListItem","position":3,"name":"How Big Models Teach Small Models to Be Smart"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/how-big-models-teach-small-models-to-be-smart-3wi68af3j#faq","mainEntity":[{"@type":"Question","name":"How did DeepSeek's distilled 7B model outperform a 32B model on math benchmarks?","acceptedAnswer":{"@type":"Answer","text":"A DeepSeek 7-billion-parameter student model, fine-tuned on training examples generated by a large reasoning model, scored higher than a 32-billion-parameter model on a competition mathematics benchmark. 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