8 papers
PCL-Reasoner-V1.5: Advancing Math Reasoning with Offline Reinforcement Learning
Yao Lu, Dengdong Fan, Jianzheng Nie +4
We present PCL-Reasoner-V1.5, a 32-billion-parameter large language model (LLM) for mathematical reasoning. The model is built upon Qwen2.5-32B and refined via supervised fine-tuni…
ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization
Jamison Meindl, Yunsheng Tian, Tony Cui +6
Global optimization of expensive, derivative-free black-box functions requires extreme sample efficiency. While Bayesian optimization (BO) is the current state-of-the-art, its perf…
Neighborhood Sampling Does Not Learn the Same Graph Neural Network
Zehao Niu, Mihai Anitescu, Jie Chen
Neighborhood sampling is an important ingredient in the training of large-scale graph neural networks. It suppresses the exponential growth of the neighborhood size across network…
Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks
Ziyuan Tang, Jie Chen
A foundation model like GPT elicits many emergent abilities, owing to the pre-training with broad inclusion of data and the use of the powerful Transformer architecture. While foun…
Stop Summation: Min-Form Credit Assignment Is All Process Reward Model Needs for Reasoning
Jie Cheng, Gang Xiong, Ruixi Qiao +5
Process reward models (PRMs) have proven effective for test-time scaling of Large Language Models (LLMs) on challenging reasoning tasks. However, reward hacking issues with PRMs li…
Model Risk Management for Generative AI In Financial Institutions
Anwesha Bhattacharyya, Ye Yu, Hanyu Yang +4
The success of OpenAI's ChatGPT in 2023 has spurred financial enterprises into exploring Generative AI applications to reduce costs or drive revenue within different lines of busin…