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FACA Improves Multi-Turn AI Agent Interaction with Local Feedback

Yiwen Zhao, Zhihao Wen, Yuchen Mao, Mingxuan Jiang, Yihao Hu, Pan Wang, Xin Zhang, Wei Wu· August 19, 2026 View original

Key takeaways

  • FACA uses the next user turn as local feedback for multi-turn agents.
  • It improves credit assignment in interactive reinforcement learning.
  • The method significantly boosts agent performance in dialogue and tool use.
  • Gains are concentrated in domains like Telecom, showing practical impact.

Who benefits

Customer ServiceTelecommunicationsE-commerceHealthcareEducation

Summary

This paper introduces FACA (Feedback-Aware Credit Assignment), a new method that uses the next user turn as local feedback to improve multi-turn user-interacting agents, leading to significant performance gains in dialogue and tool use.

Developing effective user-facing AI agents that can manage multi-turn interactions and tool use is challenging, especially when assigning credit for actions within a long dialogue. Traditional reinforcement learning often simplifies each interaction to a single terminal reward, failing to differentiate between effective elicitation, errors, or subsequent repairs made by the agent. The proposed framework, FACA (Feedback-Aware Credit Assignment), redefines the "next user turn" not just as context, but as a source of noisy, localized evidence about the preceding agent-user interaction segment. FACA aligns each user reaction with its corresponding segment, calculates a locally normalized reaction advantage, and integrates this with the verified terminal outcome advantage. Without requiring additional critics or rollouts, FACA significantly improves the performance of multi-turn agents, demonstrating gains of 5.91 to 10.22 percentage points across various domains. These improvements highlight the critical role of immediate user feedback in refining agent behavior and enhancing the overall quality of interactive AI systems.

Why it matters

Improving how AI agents learn from user interactions directly translates to more effective, user-friendly, and robust conversational AI systems, crucial for customer service, virtual assistants, and complex task automation.

How to implement this in your domain

  1. 1Integrate FACA's local feedback mechanism into existing interactive reinforcement learning pipelines for dialogue agents.
  2. 2Design user interaction logs to capture granular feedback signals for each agent turn.
  3. 3Experiment with different methods of quantifying "reaction advantage" from user responses.
  4. 4Apply FACA to improve agent performance in specific high-stakes multi-turn interaction scenarios.

Original post by Yiwen Zhao, Zhihao Wen, Yuchen Mao, Mingxuan Jiang, Yihao Hu, Pan Wang, Xin Zhang, Wei Wu

"arXiv:2608.17499v1 Announce Type: new Abstract: User-facing tool agents must coordinate dialogue and tool use as user goals unfold over multiple turns. Yet interactive reinforcement learning typically reduces each rollout to a terminal reward, assigning the same credit to effecti…"

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Originally posted by Yiwen Zhao, Zhihao Wen, Yuchen Mao, Mingxuan Jiang, Yihao Hu, Pan Wang, Xin Zhang, Wei Wu on X · view source

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