mlsanityData to 14 Oct 2026

Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue

arXiv 2406.14457 ↗First posted 20 Jun 2024cs.AIcited by 3

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (arXiv)

Reinforcement learning (RL) is a powerful approach to enhance task-oriented dialogue (TOD) systems. However, existing RL methods tend to mainly focus on generation tasks, such as dialogue policy learning (DPL) or response generation (RG), while neglecting dialogue state tracking (DST) for understanding. This narrow focus limits the systems to achieve globally optimal performance by overlooking the interdependence between understanding and generation. Additionally, RL methods face challenges with sparse and delayed rewards, which complicates training and optimization. To address these issues, we extend RL into both understanding and generation tasks by introducing step-by-step rewards throughout the token generation. The understanding reward increases as more slots are correctly filled in DST, while the generation reward grows with the accurate inclusion of user requests. Our approach provides a balanced optimization aligned with task completion. Experimental results demonstrate that our approach effectively enhances the performance of TOD systems and achieves new state-of-the-art results on three widely used datasets, including MultiWOZ2.0, MultiWOZ2.1, and In-Car. Our approach also shows superior few-shot ability in low-resource settings compared to current models.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 53 of its 76 references that are in the map. First posted on arXiv 20 Jun 2024.

Builds on

53 of 76 references.

References · 53

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Benchmarking and Learning Real-World Customer Service Dialoguecited by 1
  2. 2026StagePilot: Stage-Level Planning for Long-Horizon Dialogue Simulation in Cybergroomingcited by 0
  3. 2026Controllable Spoken Dialogue Generation: An LLM-Driven Grading System for K-12 Non-Native English Learnerscited by 0

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