A Self-Reflection and Reinforcement Learning–Focused Strategy for Sustainable Large Language Model Development: A Theoretical and Economic Analysis
Abstract
Recent advancements in large language models (LLMs) have led to impressive capabilities in reasoning, text generation, and contextual understanding. However, these gains come at a significant computational and financial cost, particularly when frequent retrainings and large-scale supervised fine-tuning (SFT) are required. In this paper, we propose a more sustainable path for LLM evolution by emphasizing:
Highly scalable base models trained once with minimal supervised guidance.
Reinforcement learning (RL)–based strategies for continuous improvement without repeated full retrainings.
Self-reflection techniques for semantic tuning (i.e., in-model optimization) that reduce the need for new large-scale data collection and supervised labeling.
We illustrate this concept using the Iron Horse series of models. Our analysis employs a cost model showing the long-term savings of investing in a robust RL pipeline and highlights the advantages of a minimal SFT approach.
