10 papers · 1 filter
Perception-Aware Policy Optimization for Multimodal Reasoning
Zhenhailong Wang, Xuehang Guo, Sofia Stoica +8
Reinforcement Learning with Verifiable Rewards (RLVR) has proven to be a highly effective strategy for endowing Large Language Models (LLMs) with robust multi-step reasoning abilit…
RM-R1: Reward Modeling as Reasoning
Xiusi Chen, Gaotang Li, Ziqi Wang +9
Reward modeling is essential for aligning large language models with human preferences through reinforcement learning. To provide accurate reward signals, a reward model (RM) shoul…
Beyond Sample-Level Feedback: Using Reference-Level Feedback to Guide Data Synthesis
Shuhaib Mehri, Xiusi Chen, Heng Ji +1
High-quality instruction-tuning data is crucial for developing Large Language Models (LLMs) that can effectively navigate real-world tasks and follow human instructions. While synt…
DecisionFlow: Advancing Large Language Model as Principled Decision Maker
Xiusi Chen, Shanyong Wang, Cheng Qian +3
In high-stakes domains such as healthcare and finance, effective decision-making demands not just accurate outcomes but transparent and explainable reasoning. However, current lang…
Self-Updatable Large Language Models by Integrating Context into Model Parameters
Yu Wang, Xinshuang Liu, Xiusi Chen +3
Despite significant advancements in large language models (LLMs), the rapid and frequent integration of small-scale experiences, such as interactions with surrounding objects, rema…
Semi-supervised Fine-tuning for Large Language Models
Junyu Luo, Xiao Luo, Xiusi Chen +3
Supervised fine-tuning (SFT) is crucial in adapting large language model (LLMs) to a specific domain or task. However, only a limited amount of labeled data is available in practic…