Developing AI agents that use large language models requires running many test scenarios in batches. Each scenario alternates between the model's reasoning (inference) and actions in a simulated environment. The total time to finish all scenarios, batch makespan, can delay development because lengthy scenarios hold up the batch.
To reduce this delay, the researchers coordinated inference and environment execution. They adjusted memory allocation to run more scenarios concurrently while reusing computations. They also prioritized longer action sequences to avoid bottlenecks.
These changes reduced batch makespan by 10.4% to 65.0% on standard tests without hurting accuracy. This faster batch completion speeds up AI model development, allowing quicker iteration.