11 papers
The Strongest Teacher Is Not Always the Best Teacher: Student-Centric Answer Selection
Zhengyu Hu, Zheyuan Xiao, Linxin Song +10
LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations. Current practice often chooses the high…
Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders
Zhongzhi Li, Xuansheng Wu, Yijiang Li +2
The diversity of post-training data is critical for effective downstream performance in large language models (LLMs). Many existing approaches to constructing post-training data qu…
Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoring
Guanxu Chen, Jing Shao, Tao Luo +3
Large language models (LLMs) are becoming increasingly capable, but the mechanisms of their thinking and decision-making processes remain unclear. Chain-of-thoughts (CoTs) have bee…
From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning
Haodong Wu, Jiahao Zhang, Lijie Hu +1
Supervised fine-tuning (SFT) data selection is commonly formulated as instance ranking: score each example and retain a top- subset. However, effective SFT training subsets are…
Adaptive Multi-Subspace Representation Steering for Attribute Alignment in Large Language Models
Xinyan Jiang, Lin Zhang, Jiayi Zhang +4
Activation steering offers a promising approach to controlling the behavior of Large Language Models by directly manipulating their internal activations. However, most existing met…
FaithSteer-BENCH: A Deployment-Aligned Stress-Testing Benchmark for Inference-Time Steering
Zikang Ding, Qiying Hu, Yi Zhang +4
Inference-time steering is widely regarded as a lightweight and parameter-free mechanism for controlling large language model (LLM) behavior, and prior work has often suggested tha…