DEFT: Distribution-guided Efficient Fine-Tuning for Human Alignment
Reinforcement Learning from Human Feedback (RLHF), using algorithms like Proximal Policy Optimization (PPO), aligns Large Language Models (LLMs) with human values but is costly and unstable. Alternatives have been proposed to replace PPO or integrate Supervised Fine-Tuning (SFT) and contrastive learning for direct fine-tuning and value alignment. However, these methods still require voluminous data to learn preferences and may weaken the generalization ability of LLMs. To further enhance alignment efficiency and performance while mitigating the loss of generalization ability, this paper introduces Distribution-guided Efficient Fine-Tuning (DEFT), an efficient alignment framework incorpora...
| Authors | Liang Zhu, Feiteng Fang, Yuelin Bai et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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