Local Qwen Models Master Browser Automation Through Iterative Replanning

A practitioner demonstrated a significant breakthrough in local LLM agent reliability by changing how Qwen models approach browser automation tasks. Instead of asking models to plan entire sequences upfront before seeing real page state, the iterative approach has models complete one step, observe the actual result, then replan—achieving dramatically higher success rates.

This finding is directly applicable to anyone building local AI agents for web automation, scraping, or interactive tasks. The upfront-planning approach fails catastrophically when encountering unexpected page layouts or dynamic content, while single-step replanning allows models to adapt to reality. Using relatively modest local models (8B and 4B parameter sizes) with this technique outperforms more naive approaches with larger models, suggesting technique optimisation matters as much as model scale.

The approach is broadly applicable beyond browser automation to any task requiring interaction with unpredictable environments. This pattern of "observe → decide → act → observe" appears particularly well-suited to smaller models running locally, where latency for additional inference passes is acceptable compared to the gains in reliability.

Source: r/LocalLLaMA · Relevance: 7/10