7 papers
AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models
Faqiang Qian, Kang An, Weikun Zhang +6
Post-training alignment of large language models often combines supervised fine-tuning (SFT) on expert demonstrations with reinforcement learning (RL) from preference or verifiable…
NH-CROP: Robust Pricing for Governed Language Data Assets under Cost Uncertainty
Xu Zheng, Feiyu Wu, Zhuocheng Wang +2
Language data are increasingly acquired and governed as assets, yet platforms often price candidate resources before knowing their true privacy or access costs. We study online pri…
SCARV: Structure-Constrained Aggregation for Stable Sample Ranking in Redundant NLP Datasets
Xu Zheng, Feiyu Wu, Linhong Wu +2
Sample-level rankings are increasingly used in data-centric NLP for analysis, filtering, debugging, and curation, yet existing pipelines typically score training examples pointwise…
RHyVE: Competence-Aware Verification and Phase-Aware Deployment for LLM-Generated Reward Hypotheses
Feiyu Wu, Xu Zheng, Zhuocheng Wang +2
Large language models (LLMs) make reward design in reinforcement learning substantially more scalable, but generated rewards are not automatically reliable training objectives. Exi…
Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs
Ziliang Wang, Kang An, Xuhui Zheng +6
While search-augmented large language models (LLMs) exhibit impressive capabilities, their reliability in complex multi-hop reasoning remains limited. This limitation arises from t…
MMRPT: MultiModal Reinforcement Pre-Training via Masked Vision-Dependent Reasoning
Xuhui Zheng, Kang An, Ziliang Wang +3
Multimodal pre-training remains constrained by the descriptive bias of image-caption pairs, leading models to favor surface linguistic cues over grounded visual understanding. We i…