4 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
Learning Ordinal Probabilistic Reward from Preferences
Longze Chen, Lu Wang, Qi Deng +7
Reward models are crucial for aligning large language models (LLMs) with human values and intentions. Existing approaches follow either Generative (GRMs) or Discriminative (DRMs) p…
Computer-Using World Model
Yiming Guan, Rui Yu, John Zhang +15
Agents operating in complex software environments benefit from reasoning about the consequences of their actions, as even a single incorrect user interface (UI) operation can derai…
Mitigating the Safety-utility Trade-off in LLM Alignment via Adaptive Safe Context Learning
Yanbo Wang, Minzheng Wang, Jian Liang +3
While reasoning models have achieved remarkable success in complex reasoning tasks, their increasing power necessitates stringent safety measures. For safety alignment, the core ch…
Skill-Aware Data Selection and Fine-Tuning for Data-Efficient Reasoning Distillation
Lechen Zhang, Yunxiang Zhang, Wei Hu +1
Large reasoning models such as DeepSeek-R1 and their distilled variants achieve strong performance on complex reasoning tasks. Yet, distilling these models often demands large-scal…