1 citations · 2 across the 16 of their papers we have counts for
8 papers · 1 filter
TraceML: What Auto-Research Agents Miss in Long-Horizon ML Development
Jiarui Yan, Weiwei Sun, Sijie Li +2
Auto-research agents now run machine-learning development unattended for hours, revising data pipelines, models, and validation from their own feedback, yet on most competitions th…
Reinforcing Human Behavior Simulation via Verbal Feedback
Weiwei Sun, Xuhui Zhou, Jiarui Liu +13
Humans learn social norms and behaviors from verbal feedback (e.g., a parent saying "that was rude" or a friend explaining "here's why that hurt"). Yet, learning from feedback for…
Spend Less, Fit Better: Budget-Efficient Scaling Law Fitting via Active Experiment Selection
Sijie Li, Shanda Li, Haowei Lin +3
Scaling laws are used to plan multi-million-dollar training runs, but fitting those laws can itself cost millions. In modern large-scale workflows, assembling a sufficiently inform…
GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learning
Ningyuan Yang, Weihua Du, Weiwei Sun +2
Reinforcement learning (RL) has become a central post-training paradigm for large language models (LLMs), but its performance is highly sensitive to the quality of training problem…
Enhancing Training Data Attribution with Representational Optimization
Weiwei Sun, Haokun Liu, Nikhil Kandpal +2
Training data attribution (TDA) methods aim to measure how training data impacts a model's predictions. While gradient-based attribution methods, such as influence functions, offer…
FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization
Shengyu Feng, Weiwei Sun, Shanda Li +2
Machine learning (ML) has shown promise for tackling combinatorial optimization (CO), but much of the reported progress relies on small-scale, synthetic benchmarks that fail to cap…
CodePDE: An Inference Framework for LLM-driven PDE Solver Generation
Shanda Li, Tanya Marwah, Junhong Shen +4
Partial differential equations (PDEs) are fundamental to modeling physical systems, yet solving them remains a complex challenge. Traditional numerical solvers rely on expert knowl…
Leader Reward for POMO-Based Neural Combinatorial Optimization
Chaoyang Wang, Pengzhi Cheng, Jingze Li +1
Deep neural networks based on reinforcement learning (RL) for solving combinatorial optimization (CO) problems are developing rapidly and have shown a tendency to approach or even…