Dynamic Fine-Tuning of Neural Language Models via Deep Reinforcement Learning on Embedding Representations

This paper introduces a novel framework that integrates Deep Reinforcement Learning (DRL) with Large Language Models (LLMs) to dynamically fine-tune text embeddings based on task- specific question-answering scenarios. Traditional embedding pipelines often lack adaptability, applying static transformations that fail to leverage feedback loops for continuous improvement. Our approach addresses this limitation by treating the fine-tuning process as a reinforcement learning problem, where a DRL agent iteratively optimizes embeddings to enhance performance in downstream tasks. The framework begins with a pre-processed document, represented as embeddings, and a predefined set of question-answer pairs. A DRL agent interacts with these embeddings, receiving rewards or penalties based on the quality of its answers, measured by alignment with the ground truth or task-specific performance metrics. Over time, this reward feedback guides the agent in applying updates effectively, enabling embeddings that better capture contextual nuances. Experimental evaluation across various datasets demonstrates the efficacy of this approach in improving question-answering accuracy, embedding coherence, and task- specific performance metrics. By enabling the LLM to iteratively adapt its embeddings based on reinforcement signals, this method bridges the gap between static vectorization and dynamic, task- driven optimization. The proposed framework highlights the potential of DRL as a fine-tuning strategy for neural language models, paving the way for more intelligent, feedback-oriented NLP systems.