# Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning

arXiv: https://arxiv.org/abs/2311.08182 · HTML: https://mlsanity.com/paper/2311.08182/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Placed in Code and instruction data synthesis, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Placed in

- Synthetic data and self-improvement for LLMs / [Code and instruction data synthesis](https://mlsanity.com/synth/code-and-instruction-data-synthesis/)

## Builds on (most cited first, 8 of 8)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://mlsanity.com/paper/2305.14387/) (2023)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [The Vendi Score: A Diversity Evaluation Metric for Machine Learning](https://mlsanity.com/paper/2210.02410/) (2022)
- [ExpertPrompting: Instructing Large Language Models to be Distinguished Experts](https://mlsanity.com/paper/2305.14688/) (2023)
- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning](https://mlsanity.com/paper/2307.03692/) (2023)

## Built on by (most cited first, 30 of 52)

- [Large Language Model Agent: A Survey on Methodology, Applications and Challenges](https://mlsanity.com/paper/2503.21460/) (2025)
- [A Survey on Self-Evolution of Large Language Models](https://mlsanity.com/paper/2404.14387/) (2024)
- [A Survey on Data Selection for LLM Instruction Tuning](https://mlsanity.com/paper/2402.05123/) (2024)
- [From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery](https://mlsanity.com/paper/2508.14111/) (2025)
- [Ming-Omni: A Unified Multimodal Model for Perception and Generation](https://mlsanity.com/paper/2506.09344/) (2025)
- [Self-Exploring Language Models: Active Preference Elicitation for Online Alignment](https://mlsanity.com/paper/2405.19332/) (2024)
- [KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents](https://mlsanity.com/paper/2403.03101/) (2024)
- [SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models](https://mlsanity.com/paper/2403.07384/) (2024)
- [Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning](https://mlsanity.com/paper/2505.20161/) (2025)
- [Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples](https://mlsanity.com/paper/2502.09650/) (2025)
- [SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection](https://mlsanity.com/paper/2402.16705/) (2024)
- [LongCat-Flash-Thinking-2601 Technical Report](https://mlsanity.com/paper/2601.16725/) (2026)
- [Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment](https://mlsanity.com/paper/2403.02738/) (2024)
- [Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models](https://mlsanity.com/paper/2408.02085/) (2024)
- [Online Merging Optimizers for Boosting Rewards and Mitigating Tax in Alignment](https://mlsanity.com/paper/2405.17931/) (2024)
- [Concept-skill Transferability-based Data Selection for Large Vision-Language Models](https://mlsanity.com/paper/2406.10995/) (2024)
- [Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric](https://mlsanity.com/paper/2502.17184/) (2025)
- [Rethinking Data Selection at Scale: Random Selection is Almost All You Need](https://mlsanity.com/paper/2410.09335/) (2024)
- [SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning](https://mlsanity.com/paper/2405.00705/) (2024)
- [Data Management For Training Large Language Models: A Survey](https://mlsanity.com/paper/2312.01700/) (2023)
- [SMART: Submodular Data Mixture Strategy for Instruction Tuning](https://mlsanity.com/paper/2403.08370/) (2024)
- [Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design](https://mlsanity.com/paper/2405.02449/) (2024)
- [Toward Effective Tool-Integrated Reasoning via Self-Evolved Preference Learning](https://mlsanity.com/paper/2509.23285/) (2025)
- [Exploring the Mystery of Influential Data for Mathematical Reasoning](https://mlsanity.com/paper/2404.01067/) (2024)
- [LEAD: Iterative Data Selection for Efficient LLM Instruction Tuning](https://mlsanity.com/paper/2505.07437/) (2025)
- [MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space](https://mlsanity.com/paper/2504.13835/) (2025)
- [LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement](https://mlsanity.com/paper/2407.00497/) (2024)
- [Federated Data-Efficient Instruction Tuning for Large Language Models](https://mlsanity.com/paper/2410.10926/) (2024)
- [Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness](https://mlsanity.com/paper/2412.06293/) (2024)
- [Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap](https://mlsanity.com/paper/2508.04149/) (2025)

## Abstract (arXiv)

Enhancing the instruction-following ability of Large Language Models (LLMs) primarily demands substantial instruction-tuning datasets. However, the sheer volume of these imposes a considerable computational burden and annotation cost. To investigate a label-efficient instruction tuning method that allows the model itself to actively sample subsets that are equally or even more effective, we introduce a self-evolving mechanism DiverseEvol. In this process, a model iteratively augments its training subset to refine its own performance, without requiring any intervention from humans or more advanced LLMs. The key to our data sampling technique lies in the enhancement of diversity in the chosen subsets, as the model selects new data points most distinct from any existing ones according to its current embedding space. Extensive experiments across three datasets and benchmarks demonstrate the effectiveness of DiverseEvol. Our models, trained on less than 8% of the original dataset, maintain or improve performance compared with finetuning on full data. We also provide empirical evidence to analyze the importance of diversity in instruction data and the iterative scheme as opposed to one-time sampling. Our code is publicly available at https://github.com/OFA-Sys/DiverseEvol.git.