Self-Generative Adversarial Fine-Tuning for Large Language Models

Shiguang Wu; Yaqing Wang; Quanming Yao

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arXiv · 2602.01137

Self-Generative Adversarial Fine-Tuning for Large Language Models

Shiguang Wu·Yaqing Wang·Quanming Yao

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Abstract

Fine-tuning large language models (LLMs) for alignment typically relies on supervised fine-tuning or reinforcement learning from human feedback, both limited by the cost and scarcity of high-quality annotations. Recent self-play and synthetic data approaches reduce this dependence but often rely on heuristic assumptions or ungrounded self-evaluation, which can cause bias accumulation and performance drift. In this paper, we propose Self-Generative Adversarial LLM (SGALM), a unified fine-tuning framework that formulates alignment as a generative adversarial game within a single LLM. SGALM jointly evolves generation and discrimination capabilities without external reward models. Theoretical and empirical results demonstrate that SGALM achieves state-of-the-art performance, serves as an effective alignment algorithm and a robust synthetic data engine.

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Shiguang Wu, Yaqing Wang, Quanming Yao. 2026-02-01. Self-Generative Adversarial Fine-Tuning for Large Language Models. https://arxiv.org/abs/2602.01137

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