Self-Generative Adversarial Fine-Tuning for Large Language Models

2026-02-01

AI Summary

This work proposes SGALM, a novel framework that addresses the high cost of human annotation in large language model alignment and mitigates bias accumulation and performance drift inherent in existing self-generated or synthetic data approaches, which often rely on heuristic assumptions or unreliable self-evaluation. SGALM uniquely establishes an internal generative adversarial game within a single model, jointly optimizing its generative and discriminative capabilities without requiring external reward models or supervised signals. Functioning simultaneously as an alignment algorithm and a synthetic data engine, SGALM achieves unsupervised alignment through adversarial training and self-adversarial fine-tuning. Experimental results demonstrate that SGALM attains state-of-the-art performance across multiple metrics, significantly improving alignment quality while consistently generating high-quality synthetic data.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

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.

Problem

• alignment
• fine-tuning
• large language models
• synthetic data
• bias accumulation

Innovation

• Self-Generative Adversarial Learning
• LLM Alignment
• Synthetic Data Generation
• Adversarial Fine-Tuning
• Reward-Free Learning