Search Results for author:
Found 7 papers, 3 papers with code
FullStack Bench: Evaluating LLMs as Full Stack Coders
As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing.
Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models
A large number of work has been conducted to improve the model's performance on code-related benchmarks with either modifications to the algorithm or refinement of the dataset.
BAMBOO: a predictive and transferable machine learning force field framework for liquid electrolyte development
Despite the widespread applications of machine learning force field (MLFF) on solids and small molecules, there is a notable gap in applying MLFF to complex liquid electrolytes.
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
Training LLMs at this scale brings unprecedented challenges to training efficiency and stability.
Supervised Pretraining for Molecular Force Fields and Properties Prediction
Experiments show that, compared to training from scratch, fine-tuning the pretrained model can significantly improve the performance for seven molecular property prediction tasks and two force field tasks.
Learning Regularized Positional Encoding for Molecular Prediction
To model the complex nonlinearity in predicting molecular properties in an more end-to-end approach, we propose to encode the positional quantities with a learnable embedding that is continuous and differentiable.
Random walk on the Bloch sphere realized by a simultaneous feedback and feed-forward control in a superconducting Xmon qubit system
Our experiment provides a conceptually simple and intuitive benchmark for the feedback control in a multi-qubit system.
Quantum Physics Superconductivity
