Researchers have proposed a new method called FACA (Feedback-Aware Credit Assignment) to improve the performance of multi-turn user interaction agents. These agents must coordinate dialogue and tool use as user goals unfold over multiple turns, but current methods often assign the same credit to effective elicitation, errors, and later repair. FACA aligns each reaction with a specific segment of the conversation, derives a locally normalized advantage, and adds it to verified terminal outcome advantage without requiring additional critics or rollouts. --- Why it matters: This matters because multi-turn user interaction agents are increasingly used in real-world applications such as customer service chatbots and virtual assistants. Improving their performance can lead to better user experiences and more efficient interactions. Source: https://arxiv.org/abs/2608.17499