Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Models

2026-03-14

AI Summary

This work addresses the need for post-training to enhance the accuracy and reasoning reliability of large language models on specific tasks, while the boundaries and synergies between supervised fine-tuning (SFT) and reinforcement learning (RL) remain unclear. The study proposes a unified analytical framework to systematically compare SFT and RL in terms of objective formulation, algorithmic structure, and data requirements, revealing their intrinsic connections. Building on this analysis, the authors design an integrated strategy to establish an efficient hybrid post-training paradigm. Through empirical evaluation across representative applications from 2023 to 2025, the research identifies a clear trend toward hybrid post-training approaches and distills key practical guidelines, offering both theoretical grounding and methodological guidance for scalable, effective, and generalizable post-training of large language models.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Pre-trained Large Language Model (LLM) exhibits broad capabilities, yet, for specific tasks or domains their attainment of higher accuracy and more reliable reasoning generally depends on post-training through Supervised Fine-Tuning (SFT) or Reinforcement Learning (RL). Although often treated as distinct methodologies, recent theoretical and empirical developments demonstrate that SFT and RL are closely connected. This study presents a comprehensive and unified perspective on LLM post-training with SFT and RL. We first provide an in-depth overview of both techniques, examining their objectives, algorithmic structures, and data requirements. We then systematically analyze their interplay, highlighting frameworks that integrate SFT and RL, hybrid training pipelines, and methods that leverage their complementary strengths. Drawing on a representative set of recent application studies from 2023 to 2025, we identify emerging trends, characterize the rapid shift toward hybrid post-training paradigms, and distill key takeaways that clarify when and why each method is most effective. By synthesizing theoretical insights, practical methodologies, and empirical evidence, this study establishes a coherent understanding of SFT and RL within a unified framework and outlines promising directions for future research in scalable, efficient, and generalizable LLM post-training.

Problem

• Supervised Fine-Tuning
• Reinforcement Learning
• Large Language Models
• Post-Training

Innovation

• Supervised Fine-Tuning
• Reinforcement Learning
• Large Language Models
• Hybrid Training
• Post-Training