Unified Pipelines for Multi-Dimensional LLM Optimization Through SFT, RLHF, and DPO

Abstract

The rapid advancements in large language models (LLMs) have sparked a significant focus on optimizing their performance for diverse applications, encompassing reasoning, domain-specific tasks, and complex coding workflows. This paper investigates the integration of three foundational techniques—Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO)—to develop unified optimization pipelines for multi-dimensional improvements in LLMs. By leveraging these methodologies collectively, this work aims to enhance LLM capabilities across dimensions such as contextual reasoning, domain-specific expertise, and syntactic precision in coding.

The study explores the use of heterogeneous datasets, including legal statutes, medical protocols, and source code repositories, to create robust models capable of adapting to diverse real-world applications. Supervised Fine-Tuning serves as a foundational layer, aligning models with task-specific objectives using curated datasets. This phase emphasizes selecting high-quality, domain-relevant training data and balancing generalization with specialization. Building upon this foundation, RLHF incorporates human evaluators to guide models toward preferred outputs by leveraging reward models tailored to task-specific benchmarks. RLHF's integration focuses on addressing challenges such as reward hacking and data sparsity, with solutions involving scalable feedback systems and adversarial testing. Complementary to these techniques, DPO streamlines optimization by directly leveraging user preference rankings to align outputs with desired outcomes, offering computational efficiency and enhanced task alignment.

The paper also delves into the architectural frameworks underpinning these optimization strategies, such as OpenAI's fine-tuning APIs and scalable distributed training infrastructures. The study provides a detailed analysis of how multi-phase pipelines ensure incremental and synergistic improvements in LLM capabilities. Case studies on the use of these pipelines in domains such as legal reasoning, medical diagnostics, and automated software generation highlight the practical benefits and challenges of this approach. Evaluation metrics such as BLEU scores for coding tasks, F1 scores for domain-specific tasks, and human preference alignment percentages for reasoning tasks are used to rigorously benchmark performance gains.

Furthermore, the research addresses critical challenges in implementing unified pipelines, including computational resource constraints, data annotation bottlenecks, and the potential for model overfitting. Strategies for mitigating these issues, such as active learning, adaptive sampling, and modular pipeline architectures, are explored in depth. The study concludes by discussing the broader implications of unified optimization pipelines for advancing LLMs as general-purpose agents, emphasizing the importance of ethical considerations, data diversity, and cross-disciplinary collaboration.