ServeLearnBench: How Well Can Agents Self-Improve from Serving Experience? (infini-ai-lab.github.io)

A recent study introduces ServeLearnBench, a framework for evaluating how well AI agents can improve their performance based on serving experiences. The research highlights that agents employing more diverse exploration strategies tend to perform better than those locked into repetitive behaviors. Notably, the Prime and CH models achieved higher Hidden reward scores (61.7 and 57.6) than their counterparts, which scored between 38.3 and 43.4. However, even these top performers lag significantly behind the Oracle model, which boasts a score of 94.5, revealing a substantial gap in learning effectiveness. Significantly, the study also found that the cost of achieving higher rewards can be considerable; for instance, Prime’s Hidden reward came at a cost of $0.195 per task, compared to CH’s $0.083 for similar rewards. The learning speed varied notably among different harnesses, with Prime and CH displaying the most rapid adaptation to new policies, particularly in Banking tasks. The research indicates that while learning can enhance agent performance, it may also inadvertently degrade previously correct behaviors, particularly in Fully Specified Retail tasks. These findings emphasize the importance of balancing exploration and exploitation in AI learning strategies, with implications for improving agent design and performance across multiple sectors.