5 accepted papers
The paradigm shift in large language models (LLMs) from instinctive responses to chain-of-thought (CoT) reasoning has fueled two prevailing assumptions: (1) reasoning capabilities only emerge in sufficiently large models, and (2) such capabilities require training on massive datasets. While the firs…
Achieving robust vision-based humanoid locomotion remains challenging due to two fundamental issues: the sim-toreal gap introduces significant perception noise that degrades performance on fine-grained tasks, and training a unified policy across diverse terrains is hindered by conflicting learning o…
Fine-grained sparsity promises higher parametric capacity without proportional per-token compute, but often suffers from training instability, load balancing, and communication overhead. We introduce \textbf{STEM} (\emph{Scaling Transformers with Embedding Modules}), a static, token-indexed approach…
The post-training phase of large language models is essential for enhancing capabilities such as instruction-following, reasoning, and alignment with human preferences. However, it demands extensive high-quality data and poses risks like overfitting, alongside significant computational costs due to…
Cardiovascular and cerebrovascular diseases are significant health issues that threaten human life. They typically develop insidiously and progress gradually, but when an event occurs, the consequences can be severe. These conditions often manifest suddenly and acutely, necessitating prompt treatmen