Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue
arXiv:2602.22697v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has accelerated the transition from conversational chatbots to general agents. However, effectively balancing empathetic communication with budget-aware decision-making remains an open challenge. Since existing methods fail to capture these complex strategic trade-offs, we propose InteractCS-RL, a framework that reframes task-oriented dialogue as a multi-granularity reinforcement learning process. Specifically, we first establish a User-centric Interaction Framework to provide a high-fidelity training gym, enabling agents to dynamically explore diverse strategies with persona-driven users. Then, we introduce Cost-aware Multi-turn Policy Optimization (CMPO) with a hybrid advantage estimation strategy. By integrating generative process credits and employing a PID-Lagrangian cost controller, CMPO effectively guides the policy to explore Pareto boundary between user reward and global cost c
arXiv:2602.22697v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has accelerated the transition from conversational chatbots to general agents. However, effectively balancing empathetic communication with budget-aware decision-making remains an open challenge. Since existing methods fail to capture these complex strategic trade-offs, we propose InteractCS-RL, a framework that reframes task-oriented dialogue as a multi-granularity reinforcement learning process. Specifically, we first establish a User-centric Interaction Framework to provide a high-fidelity training gym, enabling agents to dynamically explore diverse strategies with persona-driven users. Then, we introduce Cost-aware Multi-turn Policy Optimization (CMPO) with a hybrid advantage estimation strategy. By integrating generative process credits and employing a PID-Lagrangian cost controller, CMPO effectively guides the policy to explore Pareto boundary between user reward and global cost constraints. Extensive experiments on customized real business scenarios demonstrate that InteractCS-RL significantly outperform other baselines across three evaluation dimensions. Further evaluation on tool-agent-user interaction benchmarks verify InteractCS-RL robustness across diverse domains.
Executive Summary
This article introduces InteractCS-RL, a novel framework for task-oriented dialogue, incorporating a User-centric Interaction Framework and Cost-aware Multi-turn Policy Optimization (CMPO). InteractCS-RL balances empathetic communication with budget-aware decision-making by guiding the policy to explore the Pareto boundary between user reward and global cost constraints. Experimental results demonstrate significant performance improvements across three evaluation dimensions and robustness across diverse domains. The framework's potential to address real-world service agents' limitations makes it a valuable contribution to the field of conversational AI.
Key Points
- ▸ InteractCS-RL integrates a User-centric Interaction Framework and CMPO to balance empathetic communication and budget-aware decision-making.
- ▸ The framework guides the policy to explore the Pareto boundary between user reward and global cost constraints.
- ▸ Experimental results show significant performance improvements across three evaluation dimensions and robustness across diverse domains.
Merits
Strength in Addressing Real-world Service Agents' Limitations
The proposed framework effectively addresses the limitations of existing methods, which fail to capture complex strategic trade-offs in task-oriented dialogue.
Improved Performance in Customized Business Scenarios
Extensive experiments demonstrate that InteractCS-RL significantly outperforms other baselines across three evaluation dimensions.
Robustness Across Diverse Domains
Further evaluation on tool-agent-user interaction benchmarks verifies InteractCS-RL's robustness across diverse domains.
Demerits
Potential Overreliance on Customized Real Business Scenarios
The framework's performance may be heavily reliant on the specific customization of real business scenarios, which may not generalize well to other domains.
Complexity of Hybrid Advantage Estimation Strategy
The proposed hybrid advantage estimation strategy may be overly complex, potentially leading to computational challenges and difficulties in implementation.
Expert Commentary
The article makes a significant contribution to the field of conversational AI by introducing a novel framework that effectively balances empathetic communication and budget-aware decision-making. The proposed framework, InteractCS-RL, has the potential to address real-world service agents' limitations and improve performance in customized business scenarios. However, the framework's potential overreliance on customized real business scenarios and the complexity of the hybrid advantage estimation strategy are notable limitations that require further investigation. Overall, the article is well-written, and the experimental results are convincing, but the framework's scalability and generalizability across diverse domains remain to be seen.
Recommendations
- ✓ Future research should focus on evaluating the framework's performance in a wider range of domains and scenarios.
- ✓ The development of more intuitive and user-friendly interfaces for the hybrid advantage estimation strategy is recommended to mitigate potential computational challenges.
