From Intervention to Skill: Evolving Small–Large Language Model Collaboration

Small–large language model collaboration improves inference efficiency by assigning routine decisions to a smaller model and invoking a stronger model when additional capability is needed. Existing approaches primarily optimize this allocation for the current interaction, leaving the value of a large-model intervention largely transient. We study whether past interventions can instead enable collaboration to evolve. We introduce Intervention-to-Skill (I2S), which converts execution-supported large-model interventions into reusable skills. I2S localizes the decision opportunity at which stronger assistance is requested, grounds its effect in environment feedback, and uses trajectory context to identify a reusable procedural residual. The resulting skills persist across tasks and evolve with new evidence, allowing the effective small-model policy to improve while both acting models remain fixed. Across ALFWorld, WebShop, and ScienceWorld with Qwen3-8B paired with Qwen3-32B, DeepSeek-V4-Flash, and Gemini-2.5-Flash, I2S consistently improves task quality over strong routing and skill-based baselines. Compared with SkillGen, task-quality gains reach 27.1 score points and weighted token usage is reduced by up to 63.2%. As skills accumulate, task quality increases while large-model involvement decreases, and acquired skills are repeatedly reused in later small-model decisions. These results establish small–large collaboration as a cumulative adaptation process in which previous assistance reshapes the future division of labor between models. Code is available in the Supplementary Material.