Continuous Learning Conversational AI: A Personalized Agent Framework via A2C Reinforcement Learning
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Creating personalized and adaptable conversational AI remains a key challenge. This paper introduces a Continuous Learning Conversational AI (CLCA) approach, implemented using A2C reinforcement learning, to move beyond static Large Language Models (LLMs). We use simulated sales dialogues, generated by LLMs, to train an A2C agent. This agent learns to optimize conversation strategies for personalization, focusing on engagement and delivering value. Our system architecture integrates reinforcement learning with LLMs for both data creation and response selection. This method offers a practical way to build personalized AI companions that evolve through continuous learning, advancing beyond traditional static LLM techniques.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 3 of its 6 references that are in the map. First posted on arXiv 18 Feb 2025.
Builds on
3 of 6 references.
References · 3
- 2023GPT-4 Technical Reportcited by 18,132
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
- 2015Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Modelscited by 207
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Citing papers per year: 2025: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
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- 2025Memoria: A Scalable Agentic Memory Framework for Personalized Conversational AI
- 2024AGILE: A Novel Reinforcement Learning Framework of LLM Agents
- 2025EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle
- 2025Enabling Personalized Long-term Interactions in LLM-based Agents through Persistent Memory and User Profiles
- 2024From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models
- 2023Enhancing Pipeline-Based Conversational Agents with Large Language Models