The Single-Multi Evolution Loop for Self-Improving Model Collaboration Systems

• URL: http://arxiv.org/abs/2602.05182v1
• Date: Thu, 05 Feb 2026 01:20:32 GMT
• Title: The Single-Multi Evolution Loop for Self-Improving Model Collaboration Systems
• Authors: Shangbin Feng, Kishan Panaganti, Yulia Tsvetkov, Wenhao Yu,
• Abstract summary: We improve efficiency while preserving the strengths of collaboration by distilling collaborative patterns into a single model.<n>We propose the single-multi evolution loop: multiple LMs collaborate, each distills from the collaborative outputs, and these post-distillation improved LMs collaborate again.
• Score: 55.28554025674495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model collaboration -- systems where multiple language models (LMs) collaborate -- combines the strengths of diverse models with cost in loading multiple LMs. We improve efficiency while preserving the strengths of collaboration by distilling collaborative patterns into a single model, where the model is trained on the outputs of the model collaboration system. At inference time, only the distilled model is employed: it imitates the collaboration while only incurring the cost of a single model. Furthermore, we propose the single-multi evolution loop: multiple LMs collaborate, each distills from the collaborative outputs, and these post-distillation improved LMs collaborate again, forming a collective evolution ecosystem where models evolve and self-improve by interacting with an environment of other models. Extensive experiments with 7 collaboration strategies and 15 tasks (QA, reasoning, factuality, etc.) demonstrate that: 1) individual models improve by 8.0% on average, absorbing the strengths of collaboration while reducing the cost to a single model; 2) the collaboration also benefits from the stronger and more synergistic LMs after distillation, improving over initial systems without evolution by 14.9% on average. Analysis reveals that the single-multi evolution loop outperforms various existing evolutionary AI methods, is compatible with diverse model/collaboration/distillation settings, and helps solve problems where the initial model/system struggles to.

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