# Continuous Learning Conversational AI: A Personalized Agent Framework via A2C Reinforcement Learning

arXiv: https://arxiv.org/abs/2502.12876 · HTML: https://mlsanity.com/paper/2502.12876/

## Key facts

- Cited by 1 papers in the map (data through 14 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.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)
- Deep reinforcement learning methods / [RL training of language and agent models](https://mlsanity.com/deeprl/rl-training-of-language-and-agent-models/)

## Builds on (most cited first, 3 of 3)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)

## Built on by (most cited first, 1 of 1)

- [Systematizing LLM Persona Design: A Four-Quadrant Technical Taxonomy for AI Companion Applications](https://mlsanity.com/paper/2511.02979/) (2025)

## 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.