# InstOptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction Operators

arXiv: https://arxiv.org/abs/2310.17630 · HTML: https://mlsanity.com/paper/2310.17630/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Poisoning Language Models During Instruction Tuning](https://mlsanity.com/paper/2305.00944/) (2023)
- [Instruction Induction: From Few Examples to Natural Language Task Descriptions](https://mlsanity.com/paper/2205.10782/) (2022)
- [Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker](https://mlsanity.com/paper/2305.13729/) (2023)
- [Evaluating the Robustness of Discrete Prompts](https://mlsanity.com/paper/2302.05619/) (2023)
- [Scalable Prompt Generation for Semi-supervised Learning with Language Models](https://mlsanity.com/paper/2302.09236/) (2023)

## Built on by (most cited first, 17 of 17)

- [LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch](https://mlsanity.com/paper/2410.13213/) (2024)
- [Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap](https://mlsanity.com/paper/2401.10034/) (2024)
- [Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers](https://mlsanity.com/paper/2402.17564/) (2024)
- [Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithms](https://mlsanity.com/paper/2410.20848/) (2024)
- [Robust Utility-Preserving Text Anonymization Based on Large Language Models](https://mlsanity.com/paper/2407.11770/) (2024)
- [Survival of the Safest: Towards Secure Prompt Optimization through Interleaved Multi-Objective Evolution](https://mlsanity.com/paper/2410.09652/) (2024)
- [Multi-Fidelity Methods for Optimization: A Survey](https://mlsanity.com/paper/2402.09638/) (2024)
- [A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm](https://mlsanity.com/paper/2502.18746/) (2025)
- [Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm](https://mlsanity.com/paper/2408.11198/) (2024)
- [Human-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2401.02160/) (2024)
- [Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits](https://mlsanity.com/paper/2605.14553/) (2026)
- [MO-CAPO: Multi-Objective Cost-Aware Prompt Optimization](https://mlsanity.com/paper/2605.18869/) (2026)
- [TP-Eval: Tap Multimodal LLMs' Potential in Evaluation by Customizing Prompts](https://mlsanity.com/paper/2410.18071/) (2024)
- [Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap](https://mlsanity.com/paper/2505.19625/) (2025)
- [OmniReflect: Discovering Transferable Constitutions for LLM agents via Neuro-Symbolic Reflections](https://mlsanity.com/paper/2506.17449/) (2025)
- [ToxSearch: Evolving Prompts for Toxicity Search in Large Language Models](https://mlsanity.com/paper/2511.12487/) (2025)
- [CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts](https://mlsanity.com/paper/2606.04661/) (2026)

## Abstract (arXiv)

Instruction-based language modeling has received significant attention in pretrained language models. However, the efficiency of instruction engineering remains low and hinders the development of instruction studies. Recent studies have focused on automating instruction generation, but they primarily aim to improve performance without considering other crucial objectives that impact instruction quality, such as instruction length and perplexity. Therefore, we propose a novel approach (i.e., InstOptima) that treats instruction generation as an evolutionary multi-objective optimization problem. In contrast to text edition-based methods, our approach utilizes a large language model (LLM) to simulate instruction operators, including mutation and crossover. Furthermore, we introduce an objective-guided mechanism for these operators, allowing the LLM to comprehend the objectives and enhance the quality of the generated instructions. Experimental results demonstrate improved fine-tuning performance and the generation of a diverse set of high-quality instructions.