# Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence

arXiv: https://arxiv.org/abs/2410.11163 · HTML: https://mlsanity.com/paper/2410.11163/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 100 of its 121 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 30 of 100)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [Editing Models with Task Arithmetic](https://mlsanity.com/paper/2212.04089/) (2022)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://mlsanity.com/paper/2112.06905/) (2021)
- [TIES-Merging: Resolving Interference When Merging Models](https://mlsanity.com/paper/2306.01708/) (2023)
- [Robust fine-tuning of zero-shot models](https://mlsanity.com/paper/2109.01903/) (2021)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://mlsanity.com/paper/2305.14387/) (2023)
- [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://mlsanity.com/paper/2308.07201/) (2023)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch](https://mlsanity.com/paper/2311.03099/) (2023)
- [Mixture-of-Experts with Expert Choice Routing](https://mlsanity.com/paper/2202.09368/) (2022)

## Built on by (most cited first, 27 of 27)

- [The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants](https://mlsanity.com/paper/2505.19797/) (2025)
- [Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systems](https://mlsanity.com/paper/2502.04510/) (2025)
- [Decouple and Orthogonalize: A Data-Free Framework for LoRA Merging](https://mlsanity.com/paper/2505.15875/) (2025)
- [SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence](https://mlsanity.com/paper/2506.15672/) (2025)
- [Nature-Inspired Population-Based Evolution of Large Language Models](https://mlsanity.com/paper/2503.01155/) (2025)
- [FlexOlmo: Open Language Models for Flexible Data Use](https://mlsanity.com/paper/2507.07024/) (2025)
- [Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence](https://mlsanity.com/paper/2503.03800/) (2025)
- [Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say](https://mlsanity.com/paper/2509.21164/) (2025)
- [Don't Throw Away Your Pretrained Model](https://mlsanity.com/paper/2510.09913/) (2025)
- [SPARTA ALIGNMENT: Collectively Aligning Multiple Language Models through Combat](https://mlsanity.com/paper/2506.04721/) (2025)
- [Among Us: Measuring and Mitigating Malicious Contributions in Model Collaboration Systems](https://mlsanity.com/paper/2602.05176/) (2026)
- [Why Do More Experts Fail? A Theoretical Analysis of Model Merging](https://mlsanity.com/paper/2505.21226/) (2025)
- [MoCo: A One-Stop Shop for Model Collaboration Research](https://mlsanity.com/paper/2601.21257/) (2026)
- [DiffGraph: An Automated Agent-driven Model Merging Framework for In-the-Wild Text-to-Image Generation](https://mlsanity.com/paper/2603.20470/) (2026)
- [Data Swarms: Optimizable Generation of Synthetic Evaluation Data](https://mlsanity.com/paper/2506.00741/) (2025)
- [The Single-Multi Evolution Loop for Self-Improving Model Collaboration Systems](https://mlsanity.com/paper/2602.05182/) (2026)
- [How Many Visual Tokens Do Multimodal Language Models Need? Scaling Visual Token Pruning with F^3A](https://mlsanity.com/paper/2605.16359/) (2026)
- [Scaling Participation in Modular AI Systems](https://mlsanity.com/paper/2606.07812/) (2026)
- [A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments](https://mlsanity.com/paper/2502.10419/) (2025)
- [Weak-Link Optimization for Multi-Agent Reasoning and Collaboration](https://mlsanity.com/paper/2604.15972/) (2026)
- [Beyond Scaling: Agents Are Heading to the Edge](https://mlsanity.com/paper/2605.18535/) (2026)
- [AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions](https://mlsanity.com/paper/2608.03283/) (2026)
- [PSO-Merging: Merging Models Based on Particle Swarm Optimization](https://mlsanity.com/paper/2508.19839/) (2025)
- [Delta Activations: A Representation for Finetuned Large Language Models](https://mlsanity.com/paper/2509.04442/) (2025)
- [Model Merging to Evolution: Parameter Space Exploration for Expert Models](https://mlsanity.com/paper/2606.28373/) (2026)
- [From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMs](https://mlsanity.com/paper/2609.38516/) (2026)
- [Learning to Predict Distributions over Weight Updates for Test-Time Adaptation](https://mlsanity.com/paper/2610.01934/) (2026)

## Abstract (arXiv)

We propose Model Swarms, a collaborative search algorithm to adapt LLMs via swarm intelligence, the collective behavior guiding individual systems. Specifically, Model Swarms starts with a pool of LLM experts and a utility function. Guided by the best-found checkpoints across models, diverse LLM experts collaboratively move in the weight space and optimize a utility function representing model adaptation objectives. Compared to existing model composition approaches, Model Swarms offers tuning-free model adaptation, works in low-data regimes with as few as 200 examples, and does not require assumptions about specific experts in the swarm or how they should be composed. Extensive experiments demonstrate that Model Swarms could flexibly adapt LLM experts to a single task, multi-task domains, reward models, as well as diverse human interests, improving over 12 model composition baselines by up to 21.0% across tasks and contexts. Further analysis reveals that LLM experts discover previously unseen capabilities in initial checkpoints and that Model Swarms enable the weak-to-strong transition of experts through the collaborative search process.