# Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models

arXiv: https://arxiv.org/abs/2504.13825 · HTML: https://mlsanity.com/paper/2504.13825/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in General knowledge distillation methods, one of 12 directions in Knowledge distillation.
- Builds on 103 of its 111 references that are in the map.
- First posted on arXiv 18 Apr 2025.

## Placed in

- Knowledge distillation / [General knowledge distillation methods](https://mlsanity.com/distill/general-knowledge-distillation-methods/)

## Builds on (most cited first, 30 of 103)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Decoupled Knowledge Distillation](https://mlsanity.com/paper/2203.08679/) (2022)
- [Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks](https://mlsanity.com/paper/2004.05937/) (2020)
- [f-Divergence Minimization for Sequence-Level Knowledge Distillation](https://mlsanity.com/paper/2307.15190/) (2023)
- [Understanding and Improving Knowledge Distillation](https://mlsanity.com/paper/2002.03532/) (2020)
- [Show, Attend and Distill:Knowledge Distillation via Attention-based Feature Matching](https://mlsanity.com/paper/2102.02973/) (2021)
- [Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models](https://mlsanity.com/paper/2404.02657/) (2024)
- [Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs](https://mlsanity.com/paper/2402.12030/) (2024)
- [Self-Knowledge Distillation in Natural Language Processing](https://mlsanity.com/paper/1908.01851/) (2019)
- [Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application](https://mlsanity.com/paper/2407.01885/) (2024)
- [Localization Distillation for Dense Object Detection](https://mlsanity.com/paper/2102.12252/) (2021)
- [Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation](https://mlsanity.com/paper/2112.04840/) (2021)
- [Distilling Linguistic Context for Language Model Compression](https://mlsanity.com/paper/2109.08359/) (2021)
- [Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?](https://mlsanity.com/paper/2206.14532/) (2022)
- [Knowledge Distillation in Deep Learning and its Applications](https://mlsanity.com/paper/2007.09029/) (2020)
- [Knowledge Distillation as Semiparametric Inference](https://mlsanity.com/paper/2104.09732/) (2021)
- [DisWOT: Student Architecture Search for Distillation WithOut Training](https://mlsanity.com/paper/2303.15678/) (2023)
- [Prime-Aware Adaptive Distillation](https://mlsanity.com/paper/2008.01458/) (2020)
- [G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation](https://mlsanity.com/paper/2108.07482/) (2021)
- [Pre-training Distillation for Large Language Models: A Design Space Exploration](https://mlsanity.com/paper/2410.16215/) (2024)
- [TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing](https://mlsanity.com/paper/2002.12620/) (2020)
- [Data Distillation for Text Classification](https://mlsanity.com/paper/2104.08448/) (2021)
- [HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers](https://mlsanity.com/paper/2302.09632/) (2023)
- [Frequency Attention for Knowledge Distillation](https://mlsanity.com/paper/2403.05894/) (2024)
- [Weight Distillation: Transferring the Knowledge in Neural Network Parameters](https://mlsanity.com/paper/2009.09152/) (2020)
- [Spot-adaptive Knowledge Distillation](https://mlsanity.com/paper/2205.02399/) (2022)

## Built on by (most cited first, 6 of 6)

- [AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up](https://mlsanity.com/paper/2505.24584/) (2025)
- [Order-Level Attention Similarity Across Language Models: A Latent Commonality](https://mlsanity.com/paper/2511.05064/) (2025)
- [ECR: Manifold-Guided Semantic Cues for Compact Language Models](https://mlsanity.com/paper/2601.00543/) (2026)
- [Fast Reasoning Segmentation for Images and Videos](https://mlsanity.com/paper/2511.12368/) (2025)
- [FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift](https://mlsanity.com/paper/2607.09695/) (2026)
- [APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning](https://mlsanity.com/paper/2608.05499/) (2026)

## Abstract (arXiv)

Knowledge distillation (KD) is a technique for transferring knowledge from complex teacher models to simpler student models, significantly enhancing model efficiency and accuracy. It has demonstrated substantial advancements in various applications including image classification, object detection, language modeling, text classification, and sentiment analysis. Recent innovations in KD methods, such as attention-based approaches, block-wise logit distillation, and decoupling distillation, have notably improved student model performance. These techniques focus on stimulus complexity, attention mechanisms, and global information capture to optimize knowledge transfer. In addition, KD has proven effective in compressing large language models while preserving accuracy, reducing computational overhead, and improving inference speed. This survey synthesizes the latest literature, highlighting key findings, contributions, and future directions in knowledge distillation to provide insights for researchers and practitioners on its evolving role in artificial intelligence and machine learning.