Semantic Energy-Guided Stable Fine-Tuning for Distribution-Controlled Generation in Large Language Models

Abstract

This study proposes a stable fine-tuning method based on a semantic energy function to address common problems in large language model generation, including semantic drift, distribution instability, and insufficient long-range coherence. The method constructs a differentiable semantic energy field in the latent space and integrates an energy function, a smoothing term, and gradient constraints to guide the generation trajectory in a continuous and structured manner. This allows the model to evolve along low-energy paths during inference and maintain semantic consistency and distributional stability. The framework consists of semantic energy modeling, energy regularization, stable fine-tuning, and distribution guidance, and can be seamlessly integrated into existing pretrained language models without modifying the underlying architecture. The study further introduces evaluation metrics for semantic consistency, distribution shift, energy stability, and long-range coherence to assess the method from structural, distributional, and trajectory perspectives. Comparative experiments show that the method achieves superior performance across all metrics, with notable improvements in controllable distribution behavior and semantic trajectory smoothness. Sensitivity analyses on hyperparameters, environmental factors, and data properties confirm the robustness of the semantic energy framework and highlight its key role in semantic structure regulation and distribution management. Overall, the proposed semantic-energy-driven fine-tuning mechanism provides an interpretable, scalable, and generalizable approach for enhancing the stable generation capability of large language models.