Rohan Bansal's Qwen AI model has been trained to generate query plans that run up to 81% faster than those produced by Postgres, as detailed in a September 16, 2026 report on rohanbansal.com. The model uses a 4 billion parameter architecture and reinforcement learning to optimize database queries more efficiently than traditional methods.
The training process involves four reinforcement learning rollouts per query, where Qwen generates candidate query strategies. Each candidate plan is executed on Postgres to measure performance against the default plan. Scalar rewards based on execution speed are fed back to update Qwen’s model weights, nudging it toward faster query plans. This iterative approach allows Qwen to learn and improve query optimization dynamically.
This advancement addresses the longstanding challenge of query optimization in database management systems. Previous research, such as the 2015 study by Leis et al., questioned the effectiveness of existing query optimizers. Qwen’s ability to outperform Postgres by a significant margin highlights the potential of AI-driven techniques to enhance database performance, which could impact applications relying heavily on complex queries.
The Qwen model’s training and evaluation details are publicly available on rohanbansal.com as of September 16, 2026, providing a resource for further research and development in AI-based query optimization.