From Static Context to Calibrated Interactive RL: Mitigating Distribution Shift in Multi-turn Dialogue with Aligned Simulator
The article discusses a new approach to improving dialogue agents through a method called Calibrated Interactive RL. This method aims to address the challenges posed by distribution shifts in multi-turn dialogues. The authors present experimental results showing that their approach significantly enhances dialogue quality compared to traditional methods.
- ▪The research focuses on optimizing dialogue agents using Calibrated Interactive RL to mitigate distribution shifts.
- ▪Two main sources of distribution shift are identified: policy-induced shift and simulator-induced shift.
- ▪Experiments demonstrate that Interactive RL outperforms Static Context RL, and aligning simulators with human behaviors further improves performance.
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Computer Science > Artificial Intelligence arXiv:2605.26403 (cs) [Submitted on 26 May 2026] Title:From Static Context to Calibrated Interactive RL: Mitigating Distribution Shift in Multi-turn Dialogue with Aligned Simulator Authors:Xiaohua Wang, Jiakang Yuan, Zisu Huang, Muzhao Tian, Changze Lv, Kaitao Song, Tao Chen, Xiaoqing Zheng View a PDF of the paper titled From Static Context to Calibrated Interactive RL: Mitigating Distribution Shift in Multi-turn Dialogue with Aligned Simulator, by Xiaohua Wang and 7 other authors View PDF HTML (experimental) Abstract:A long-standing goal of the research community is to develop highly interactive LLM-based dialogue agents.
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