一作ACL 2026Findings· Findings of ACL
RIFT : Repurposing Negative Samples via Reward-Informed Fine-Tuning
Zehua Liu · Shuqi LIU · Tao Zhong · Mingxuan Yuan
摘要
While Supervised Fine-Tuning (SFT) and Rejection Sampling Fine-Tuning (RFT) are standard for LLM alignment, they either rely on costly expert data or discard valuable negative samples, leading to data inefficiency. To address this, we propose Reward Informed Fine-Tuning (RIFT), a simple yet effec...