Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data

Supervised fine-tuning (SFT) has become a crucial step for aligning pretrained large language models (LLMs) using supervised datasets of input-output pairs. However, despite being supervised, SFT is inherently limited by its generative training objective. To address its limitations, the existing common strategy is to follow SFT with a separate phase of preference optimization (PO), which relies on either human-labeled preference data or a strong reward model to guide the learning process. In this paper, we address the limitations of SFT by exploring one of the most successful techniques in conventional supervised learning: discriminative learning. We introduce Discriminative Fine-Tuning (DFT), an improved variant of SFT, which mitigates the burden of collecting human-labeled preference data or training strong reward models. Unlike SFT that employs a generative approach and overlooks negative data, DFT adopts a discriminative paradigm that increases the probability of positive answers while suppressing potentially negative ones, aiming for data prediction instead of token prediction. Our contributions include: (i) a discriminative probabilistic framework for fine-tuning LLMs by explicitly modeling the discriminative likelihood of an answer among all possible outputs given an input; (ii) efficient algorithms to optimize this discriminative likelihood; and (iii) extensive experiments demonstrating DFT's effectiveness, achieving performance better than SFT and comparable to if not better than SFT$\rightarrow$PO. The code can be found atthis https URL.

@article{guo2025_2502.18679,
  title={ Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data },
  author={ Siqi Guo and Ilgee Hong and Vicente Balmaseda and Changlong Yu and Liang Qiu and Xin Liu and Haoming Jiang and Tuo Zhao and Tianbao Yang },
  journal={arXiv preprint arXiv:2502.18679},
  year={ 2025 }
}