6 papers
Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization
Fei Bai, Zhipeng Chen, Chuan Hao +6
Recently, reinforcement learning~(RL) has become an important approach for improving the capabilities of large language models~(LLMs). In particular, reinforcement learning from ve…
CGSPG: Confidence-calibrated Group Sequence Policy Gradient towards Self-aware Reasoning
Haotian Liu, Shuo Wang, Hongteng Xu
Reinforcement Learning (RL) methods, exemplified by Group Relative Policy Optimization (GRPO) and its variants, play a central role in developing reasoning models. However, these m…
MoORE: SVD-based Model MoE-ization for Conflict- and Oblivion-Resistant Multi-Task Adaptation
Shen Yuan, Yin Zheng, Taifeng Wang +2
Adapting large-scale foundation models in multi-task scenarios often suffers from task conflict and oblivion. To mitigate such issues, we propose a novel ''model MoE-ization'' stra…
Learning an Effective Premise Retrieval Model for Efficient Mathematical Formalization
Yicheng Tao, Haotian Liu, Shanwen Wang +1
Formalized mathematics has recently garnered significant attention for its ability to assist mathematicians across various fields. Premise retrieval, as a common step in mathematic…
Towards Better Multi-head Attention via Channel-wise Sample Permutation
Shen Yuan, Hongteng Xu
Transformer plays a central role in many fundamental deep learning models, e.g., the ViT in computer vision and the BERT and GPT in natural language processing, whose effectiveness…
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction
Fanmeng Wang, Minjie Cheng, Hongteng Xu
Predicting molecular ground-state conformation (i.e., energy-minimized conformation) is crucial for many chemical applications such as molecular docking and property prediction. Cl…