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Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue
arXiv – CS AI|Ning Gao, Wei Zhang, Yuqin Dai, Ling Shi, Ziyin Wang, Yujie Wang, Wei He, Jinpeng Wang, Chaozheng Wang||5 views
🤖AI Summary
Researchers introduce InteractCS-RL, a new reinforcement learning framework that helps AI agents balance empathetic communication with cost-effective decision-making in task-oriented dialogue. The system uses a multi-granularity approach with persona-driven user interactions and cost-aware policy optimization to achieve better performance across business scenarios.
Key Takeaways
- →InteractCS-RL framework addresses the challenge of balancing empathy and budget constraints in AI dialogue systems.
- →The system uses a User-centric Interaction Framework as a training environment with persona-driven users.
- →Cost-aware Multi-turn Policy Optimization (CMPO) employs hybrid advantage estimation and PID-Lagrangian cost control.
- →Experiments show significant outperformance over baselines across three evaluation dimensions in real business scenarios.
- →The framework demonstrates robustness across diverse domains in tool-agent-user interaction benchmarks.
#reinforcement-learning#llm#dialogue-systems#ai-agents#task-oriented#optimization#cost-efficiency#research
Read Original →via arXiv – CS AI
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