Published on May 8, 2026
Recent advancements in large language models (LLMs) have made remarkable strides in performance, yet they come with high deployment costs. Many researchers are now shifting towards leveraging teams of smaller LLMs to achieve comparable, if not better, results without the intensive resource demands.
This change introduces challenges in managing multiple models simultaneously. Coordinating updates among these agents often leads to instability during training due to distribution shifts. In response, a new approach called Sequential Agent Tuning (SAT) has been developed to facilitate decentralized training without requiring a central controller.
SAT operates team as a factorized policy and applying block-coordinate updates to each agent. This allows for scalability while maintaining performance, with empirical results indicating that a team of three smaller 4B agents trained under SAT outperformed a significantly larger 32B model, Qwen3-32B, of 3.9% on established benchmarks.
The implications of this new method are substantial. Not only does SAT promise monotonic improvement in performance during training, but it also allows teams to incorporate stronger agents seamlessly without retraining the entire model, enhancing overall system efficiency while minimizing downtime.
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