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Research questionHow can conversational reinforcement learning coordinate strategic utterance choices with token generation under sparse, delayed rewards?A conversational agent must decide both which communicative strategy to pursue and how to realize it token by token. Feedback often arrives at the utterance or conversation level, making credit assignment across long interactions difficult.
AI
AI Agents
Natural Language Processing
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.ToSCA: Leveraging Hierarchical Reinforcement Learning on Temporal and Strategic Abstractions of Conversational AgentsThe source studies a two-level hierarchical reinforcement-learning conversational agent that conditions token generation on explicit utterance-level strategies. Evidence comes from daily-life and emotional-support conversations, with outcomes reported for strategy determination and response quality.research paper · Sep 2, 2026
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