Data-Driven Optimization of User-AI Agent Interaction: Technical Logic, Academic Support, and User Participation

In an era of rapid AI advancement, AI Agents, as intelligent entities capable of perceiving environments, making decisions, and executing tasks, are increasingly integrated into various aspects of our lives. The interaction between users and AI Agents is not merely a simple exchange of information; it is a core driving force for Agents to continuously learn, evolve, and provide personalized services. This document aims to systematically explore the technical logic behind user-AI Agent interaction, its profound impact on enhancing model and Agent capabilities, and, supported by academic theories, propose a series of actionable user engagements to build a more intelligent, efficient, and user-centric AI Agent ecosystem.

We will delve into how interaction data empowers the fine-tuning of Large Language Models (LLMs), the optimization of Reinforcement Learning (RL) strategies, the knowledge base updates of Retrieval Augmented Generation (RAG), and Agent trajectory learning and personalized services. Concurrently, this document will pay special attention to the value of interaction data in specific domains, such as investment trading. Ultimately, we will elaborate on the specific actions users can take to actively participate in the Agent’s optimization process, thereby achieving an intelligent leap through human-machine collaboration.

7.1 Enhancing Model Capabilities with Interaction Data

User-AI Agent interaction data serves as a critical resource for the continuous evolution of underlying models, particularly Large Language Models (LLMs). This data encapsulates rich contextual information, user intentions, task execution processes, and feedback on Agent outputs, providing valuable training signals for model fine-tuning, comprehension enhancement, and generation quality optimization.

7.1.1 Data-Driven Model Fine-Tuning

Interaction data, as high-quality training material, can significantly improve the performance of LLMs in specific tasks and domains. By collecting and analyzing user-Agent dialogue data, we can perform Supervised Fine-Tuning (SFT) to better adapt models to practical application scenarios. For instance, combining user-Agent dialogue data (including context, intent, and task execution records) with general instruction data can train LLMs that possess both general capabilities and proficiency in specific tasks. This approach is akin to Transfer Learning, where knowledge learned by a pre-trained model on large-scale general data is transferred to a specific task, thereby avoiding the immense cost and data requirements of training from scratch.

Enhanced Contextual Understanding: Multi-turn dialogues, coreference resolution, and elliptical expressions within interaction data are crucial for improving a model’s contextual understanding. Users often omit known information or use pronouns in continuous conversations; these complex linguistic phenomena are reflected in interaction data. By learning from this data, models can establish stronger contextual dependencies, leading to more accurate understanding of user intentions and reduced ambiguity.

Optimized Generation Quality: User feedback on Agent responses, such as corrections, ratings, or likes/dislikes, can be treated as important reward signals. Utilizing these signals, Reinforcement Learning from Human Feedback (RLHF) can be employed to adjust the model’s generation strategy. For example, the Proximal Policy Optimization (PPO) algorithm is frequently used to optimize LLM outputs based on human preferences, making the generated content more aligned with user expectations and improving accuracy and user satisfaction.

7.1.2 Addressing Long-Tail Problems and Domain Adaptation

Real-world user needs are diverse and dynamic, and LLMs trained on general data may not cover all long-tail problems or specific domain knowledge. Interaction data can effectively bridge this gap.

Domain-Specific Data Augmentation: Interaction data often contains domain knowledge not encountered by models during pre-training, such as industry-specific terminology, specialized processes, or emerging concepts. By annotating and utilizing this data, the model’s knowledge boundaries can be expanded. Combined with data synthesis techniques, such as LLM-generated dialogues, domain-specific datasets can be further enriched, thereby improving model performance in specific vertical domains.

Dynamic Vocabulary Updates: Users may introduce new words, popular terms, or rare expressions during interaction. By continuously monitoring and analyzing interaction data, these dynamic vocabulary changes can be captured and incorporated into the model’s vocabulary or knowledge base, reducing the “Out-of-Vocabulary” (OOV) problem and enhancing the model’s ability to understand and generate new information.