@inproceedings{sun-etal-2026-dynamo,
title = "{DYNAMO}: Dynamic Skill-Tool Evolution for Vision-Language Agents",
author = "Sun, Yutao and
Miao, Yanting and
Ma, Hao-Xuan and
Zhou, Mengyu and
Chen, Mingshuai and
Zhao, Tiancheng and
Wang, Dexin and
Lv, Lei and
Xu, Li and
Jiang, Xiaoxi and
Jiang, Guanjun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.774/",
pages = "17994--18015",
ISBN = "979-8-89176-459-0",
abstract = "Improving vision-language models (VLMs) on visual reasoning typically requires retraining or hand-designed prompts and tools. We present Dynamo, a training-free framework that adapts a frozen VLM without any weight updates. On a small labeled training subset, the agent inspects its own correct and incorrect attempts and evolves two complementary capabilities: reusable reasoning skills for cognitive bottlenecks, and executable visual tools for perceptual ones. Each generated tool is paired with a skill that specifies when to invoke it, and both capability types accumulate in a persistent library. Across four visual reasoning benchmarks and five VLM backbones, Dynamo improves direct inference on all 20 model{--}benchmark settings (avg. +5.6 acc). When the tool set is given in advance, the framework learns when to call each tool, and per-step tool choice improves on every tested backbone. Against task-specific RL (VTool-R1, DeepEyes), Dynamo closes 65{--}99{\%} of the RL gap at a fraction of the compute, and combines additively with RL when available."
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<abstract>Improving vision-language models (VLMs) on visual reasoning typically requires retraining or hand-designed prompts and tools. We present Dynamo, a training-free framework that adapts a frozen VLM without any weight updates. On a small labeled training subset, the agent inspects its own correct and incorrect attempts and evolves two complementary capabilities: reusable reasoning skills for cognitive bottlenecks, and executable visual tools for perceptual ones. Each generated tool is paired with a skill that specifies when to invoke it, and both capability types accumulate in a persistent library. Across four visual reasoning benchmarks and five VLM backbones, Dynamo improves direct inference on all 20 model–benchmark settings (avg. +5.6 acc). When the tool set is given in advance, the framework learns when to call each tool, and per-step tool choice improves on every tested backbone. Against task-specific RL (VTool-R1, DeepEyes), Dynamo closes 65–99% of the RL gap at a fraction of the compute, and combines additively with RL when available.</abstract>
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%0 Conference Proceedings
%T DYNAMO: Dynamic Skill-Tool Evolution for Vision-Language Agents
%A Sun, Yutao
%A Miao, Yanting
%A Ma, Hao-Xuan
%A Zhou, Mengyu
%A Chen, Mingshuai
%A Zhao, Tiancheng
%A Wang, Dexin
%A Lv, Lei
%A Xu, Li
%A Jiang, Xiaoxi
%A Jiang, Guanjun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F sun-etal-2026-dynamo
%X Improving vision-language models (VLMs) on visual reasoning typically requires retraining or hand-designed prompts and tools. We present Dynamo, a training-free framework that adapts a frozen VLM without any weight updates. On a small labeled training subset, the agent inspects its own correct and incorrect attempts and evolves two complementary capabilities: reusable reasoning skills for cognitive bottlenecks, and executable visual tools for perceptual ones. Each generated tool is paired with a skill that specifies when to invoke it, and both capability types accumulate in a persistent library. Across four visual reasoning benchmarks and five VLM backbones, Dynamo improves direct inference on all 20 model–benchmark settings (avg. +5.6 acc). When the tool set is given in advance, the framework learns when to call each tool, and per-step tool choice improves on every tested backbone. Against task-specific RL (VTool-R1, DeepEyes), Dynamo closes 65–99% of the RL gap at a fraction of the compute, and combines additively with RL when available.
%U https://aclanthology.org/2026.emnlp-main.774/
%P 17994-18015
Markdown (Informal)
[DYNAMO: Dynamic Skill-Tool Evolution for Vision-Language Agents](https://aclanthology.org/2026.emnlp-main.774/) (Sun et al., EMNLP 2026)
ACL
- Yutao Sun, Yanting Miao, Hao-Xuan Ma, Mengyu Zhou, Mingshuai Chen, Tiancheng Zhao, Dexin Wang, Lei Lv, Li Xu, Xiaoxi Jiang, and Guanjun Jiang. 2026. DYNAMO: Dynamic Skill-Tool Evolution for Vision-Language Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 17994–18015, Budapest, Hungary. Association for Computational Linguistics.