arXiv AI
Jun 4

Unlocking Proactivity in Task-Oriented Dialogue

arXiv:2605. 22240v2 Announce Type: replace Abstract: Proactive task-oriented dialogue (TOD), such as outbound sales, demands a persuasive agent that actively probes the user's concerns and steers the conversation toward acceptance within a bounded number of turns.

By Azure Zhang, Ning Gao, Yuqin Dai, Ruiyuan Wu, Jinpeng Wang, Rena Wei Gao, Bingdong Tan, Shuzheng Gao, Zongjie Li, Chaozheng Wang
arXiv AI
Sep 25

Beyond Surface Style: Aligning Multi-Turn User Simulators with Behavioral Consistency

The paper introduces TRACER, a multi‑turn user simulator that models evolving user intent and aligns simulated behavior with real interaction trajectories. TRACER is trained first with supervised fine‑tuning on real dialogues and then with reinforcement learning that uses hierarchical outcome‑ and trajectory‑level rewards to address reward sparsity and credit assignment. In real customer‑service sessions, TRACER‑7B outperforms the best baseline by 11.4 conversion F1, achieves the lowest group‑level conversion‑rate error and semantic trajectory distance, and generalizes to out‑of‑distribution scenarios, while human Turing tests show its conversations appear natural. The authors also present the Dynamic Marketing Benchmark, which evaluates both persuasion effectiveness and response quality of large language models through simulated interactions, demonstrating that higher response quality does not always lead to higher conversion rates.

By Geng Chen, Ruotong Pan, Zhirui Yang, Qiqi He, Jiawei Chen, Zhang Yunfei, Chongyuan Chen, Minxuan Lv, Zheng Yang, Win-Bin Huang, Xiangyu Wu, Wenwu Ou
arXiv Computation and Language
3d ago

Profile-Driven User Simulation via Reinforcement Learning with Hierarchical Rewards for Task-Oriented Dialogues

ProfileSim is a profile‑driven user simulation framework designed for financial marketing outbound calls. It jointly models user demographics, Big Five personality traits, and business intents reverse‑engineered from real call logs, allowing fine‑grained control over simulated behavior. The system is fine‑tuned on real voice dialogue data and further refined with Group Relative Policy Optimization using hierarchical rewards, achieving high colloquial naturalness, strong user profile adherence, and low agent scores on long‑tail intents while producing shorter user utterances.

By Juan Li, Fangshu Chang, Xinyi Jiang, Ling Huang, Yunzhang Chen
arXiv Computation and Language
Sep 4

RL-ADA: A World-Feedback Framework for Adversarially Robust Enterprise Dialogue Agents

RL-ADA introduces a co‑evolutionary training framework that replaces costly human annotations with world‑feedback rewards derived from interaction outcomes. In this system, a large Customer Support Agent and an Adversarial Customer Agent train together, guided by an automated judge that rewards successful resolution and realistic intent‑concealing utterances, respectively. Applied to a banking support proof of concept, the method eliminates routing errors and doubles the end‑to‑end PASS rate over five cycles, while also revealing a new adversarial strategy called Contextual Camouflage.

By Ram Narayanan, Harshit Rajgarhia, Abhishek Mukherji