# TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models

arXiv: https://arxiv.org/abs/2501.16937 · HTML: https://mlsanity.com/paper/2501.16937/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge distillation for LLMs, one of 12 directions in Knowledge distillation.
- Builds on 48 of its 60 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Placed in

- Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/)

## Builds on (most cited first, 30 of 48)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://mlsanity.com/paper/1606.06031/) (2016)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes](https://mlsanity.com/paper/2306.13649/) (2023)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [Sequence-Level Knowledge Distillation](https://mlsanity.com/paper/1606.07947/) (2016)
- [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (2024)
- [TinyLlama: An Open-Source Small Language Model](https://mlsanity.com/paper/2401.02385/) (2024)
- [Improved Knowledge Distillation via Teacher Assistant](https://mlsanity.com/paper/1902.03393/) (2019)
- [Textbooks Are All You Need II: phi-1.5 technical report](https://mlsanity.com/paper/2309.05463/) (2023)
- [Do Deep Nets Really Need to be Deep?](https://mlsanity.com/paper/1312.6184/) (2013)
- [Decoupled Knowledge Distillation](https://mlsanity.com/paper/2203.08679/) (2022)
- [On the Efficacy of Knowledge Distillation](https://mlsanity.com/paper/1910.01348/) (2019)
- [SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification](https://mlsanity.com/paper/2305.09781/) (2023)
- [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases](https://mlsanity.com/paper/2402.14905/) (2024)

## Built on by (most cited first, 22 of 22)

- [Entropy-Aware On-Policy Distillation of Language Models](https://mlsanity.com/paper/2603.07079/) (2026)
- [SelecTKD: Selective Token-Weighted Knowledge Distillation for LLMs](https://mlsanity.com/paper/2510.24021/) (2025)
- [A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone](https://mlsanity.com/paper/2505.12781/) (2025)
- [Ensemble Learning for Large Language Models in Text and Code Generation: A Survey](https://mlsanity.com/paper/2503.13505/) (2025)
- [RAD: Redundancy-Aware Distillation for Hybrid Models via Self-Speculative Decoding](https://mlsanity.com/paper/2505.22135/) (2025)
- [Distillation of Large Language Models via Concrete Score Matching](https://mlsanity.com/paper/2509.25837/) (2025)
- [Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards](https://mlsanity.com/paper/2606.18810/) (2026)
- [Can Compact Language Models Search Like Agents? Distillation-Guided Policy Optimization for Preserving Agentic RAG Capabilities](https://mlsanity.com/paper/2508.20324/) (2025)
- [AMiD: Knowledge Distillation for LLMs with α-mixture Assistant Distribution](https://mlsanity.com/paper/2510.15982/) (2025)
- [Comprehensive Evaluation on Lexical Normalization: Boundary-Aware Approaches for Unsegmented Languages](https://mlsanity.com/paper/2505.22273/) (2025)
- [Beyond Dark Knowledge: Mixup-Based Distillation for Reliable Predictions](https://mlsanity.com/paper/2606.12171/) (2026)
- [Performance Evaluation of Emotion Classification in Japanese Using RoBERTa and DeBERTa](https://mlsanity.com/paper/2505.00013/) (2025)
- [Following the Teacher's Footsteps: Scheduled Checkpoint Distillation for Domain-Specific LLMs](https://mlsanity.com/paper/2601.10114/) (2026)
- [Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models](https://mlsanity.com/paper/2604.26951/) (2026)
- [Logit Distillation on Manifolds: Mapping by Learning](https://mlsanity.com/paper/2606.00771/) (2026)
- [Let the Data Decide: Supervision Analysis, Capability Trade-offs, and Adaptive Objective Routing in Continued Pre-Training via Off-Policy Distillation](https://mlsanity.com/paper/2607.16246/) (2026)
- [iSDFT: Information-Proximal Self-Distillation for Continual Learning in LLMs](https://mlsanity.com/paper/2609.24646/) (2026)
- [Interactive-Policy Distillation with Bidirectional Propose-and-Verify](https://mlsanity.com/paper/2609.36546/) (2026)
- [Better Supervision Is Nearby: Neighborhood On-Policy Self-Distillation](https://mlsanity.com/paper/2609.39687/) (2026)
- [How Should Teachers Be Prepared? RL on Student-Induced States for On-Policy Distillation](https://mlsanity.com/paper/2610.04950/) (2026)
- [E2-OPSD: Taming Entropy Overshoot in On-Policy Self-Distillation](https://mlsanity.com/paper/2610.05048/) (2026)
- [Teacher-Guided Tool Learning for Small Language Models via Bidirectional KL Constraints](https://mlsanity.com/paper/2610.18459/) (2026)

## Abstract (arXiv)

Causal language models have demonstrated remarkable capabilities, but their size poses significant challenges for deployment in resource-constrained environments. Knowledge distillation, a widely-used technique for transferring knowledge from a large teacher model to a small student model, presents a promising approach for model compression. A significant remaining issue lies in the major differences between teacher and student models, namely the substantial capacity gap, mode averaging, and mode collapse, which pose barriers during distillation. To address these issues, we introduce textitTemporally Adaptive Interpolated Distillation (TAID), a novel knowledge distillation approach that dynamically interpolates student and teacher distributions through an adaptive intermediate distribution, gradually shifting from the student's initial distribution towards the teacher's distribution. We provide a theoretical analysis demonstrating TAID's ability to prevent mode collapse and empirically show its effectiveness in addressing the capacity gap while balancing mode averaging and mode collapse. Our comprehensive experiments demonstrate TAID's superior performance across various model sizes and architectures in both instruction tuning and pre-training scenarios. Furthermore, we showcase TAID's practical impact by developing two state-of-the-art compact foundation models: textttTAID-LLM-1.5B for language tasks and textttTAID-VLM-2B for vision-language tasks. These results demonstrate TAID's effectiveness in creating high-performing and efficient models, advancing the development of more accessible AI technologies.