Knowledge distillation is how a small, fast model learns to behave like a large, accurate one, keeping most of the quality while shedding the cost of running it. This category collects plain language breakdowns of the research that moves the field forward, from the classic KL divergence losses through newer token level and ranking based objectives. Every analysis explains the core idea, the math that makes it work, and the limitations the authors are honest about, and most ship with runnable code you can drop straight into your own training pipeline. It is built for practitioners who want to understand a method well enough to use it, not just cite it.
Most attempts to make a language model reason better work from the outside. Chain of Thought prompting, Tree of Thought, and their many descendants coax more careful step by step behavior out of a model that already exists. The authors…
Merging A Korean Model Lifts Llama Math Reasoning Read More »
Picture a firmware engineer with a microcontroller that has a few megabytes of flash and a product manager who wants a language model on it. She opens the specification for BERT Base and finds 109.5 million parameters. Then she notices…
ShareBERT Compresses BERT Using Its Own Embeddings Read More »
Motor imagery research has a long history, and most of it points at the hands. You imagine gripping something, a classifier tries to figure out which hand you meant, and there are entire competition datasets built around exactly that setup.…
How a Transformer MSC-T3AM Learns to Tell Your Left Leg From Your Right on EEG Read More »
Graph neural networks have posted strong results on node classification, link prediction and graph level tasks for years now, but the field’s research energy has overwhelmingly gone into model architecture. New attention mechanisms, new convolution operators, new ways of combining…
EDEN Distills a Directed Graph’s Own Hierarchy Into Better GNN Predictions Read More »
Two brain MRI scans arrive in the same batch. One is crisp, the tumor margin clean against healthy tissue, and any decent model would call it correctly. The other is smeared by motion, low in contrast, the boundary a guess.…
Context Aware Adaptive Knowledge Distillation for Tumor Detection Read More »
Knowledge distillation has a simple pitch. Train a big accurate model, then train a small cheap model to copy its answers rather than only the ground truth labels. The small model ends up smarter than it would from labels alone,…
FiGKD Distills Only the Teacher’s High Frequency Logits Read More »
Knowledge distillation has been one of the most reliable tools for making a smaller, deployable model perform closer to a large, expensive one. The classic recipe trains a big teacher network first, then trains a compact student to mimic the…
A Single Model Can Now Teach Itself Through Patch Swaps Read More »
Contrast enhanced imaging is genuinely useful, which is exactly why it became standard practice. In MRI, gadolinium based agents make the difference in signal between tumor tissue and normal tissue much easier to see, and in CT, iodinated contrast media…
A Contrast Free Tumor AI Owes Little to Its Text Model Read More »
A seven billion parameter model writes a clean, helpful answer, then you try to ship it. It will not fit on the phone, the latency blows past your budget, and the energy draw is not something a small product can…
ToDi: Per Token KL Divergence Control for LLM Distillation Read More »
A well funded lab spends months and a serious pile of compute training a frontier language model. A smaller competitor never touches that budget. Instead it sends a few thousand prompts to the public API, saves the answers, and fine…
DOGe: Stopping LLM Knowledge Theft With One Fine Tuned Layer Read More »