The authors built Weblica, a framework for creating reproducible and scalable web navigation environments by capturing real websites at the HTTP level (so interactions replay consistently) and using LLMs to synthesize thousands of diverse navigation tasks grounded in real-world sites. They trained an RL agent on these environments, producing Weblica-8B, which outperforms similar-sized open-weight baselines on multiple web navigation benchmarks while using fewer inference steps and scaling well with test-time compute.
Main takeaways:
- Existing web-agent training is limited to offline trajectories or a handful of simulated sites; Weblica scales to thousands of diverse, reproducible environments.
- HTTP-level caching captures stable visual states and interactive behavior from real websites, enabling consistent replay for RL training.
- LLM-based synthesis generates diverse tasks grounded in real-world websites and core navigation skills (form-filling, search, multi-step goals).
- Weblica-8B outperforms open-weight baselines of similar size, uses fewer inference steps, and is competitive with API models.
- Scales favorably with additional test-time compute (more search/rollouts improve performance).