The Flow has not summarised this story yet — read it at arXiv Computation and Language.
A prominent paradigm in inference-time alignment employs lightweight supervisors to steer Large Language Models (LLMs). Through empirical analysis, we identify a structural mismatch in this paradigm:...
arXiv:2603. 07445v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often require fine-tuning (FT) to perform well on downstream tasks, but FT can induce safety-alignment drift even when the training dataset contains only benign data.
arXiv:2606. 11201v1 Announce Type: cross Abstract: The wide deployment of LLMs has made model alignment necessary to make newly trained models safely and effectively respond to user instructions.
Safety alignment of large language models (LLMs) typically depends on high-quality supervision data, such as safe demonstrations or preference pairs. However, in real-world deployment, emerging safety requirements are often specified as natural-language policies, while corresponding supervision data may be costly, delayed, or unavailable.
Atomic Consistency Preference Optimization (ACPO) is a self‑supervised preference‑tuning method that improves factual accuracy in large language models without external supervision. It uses atomic consistency signals—agreement of individual facts across multiple stochastic responses—to identify high‑ and low‑quality data pairs for model alignment. ACPO outperforms a strong supervised alignment baseline by 1.95 points on the LongFact and BioGen datasets for Phi‑3 and Llama‑3 models.
arXiv:2607. 20515v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is critical for aligning Large Language Models (LLMs) with human preferences.