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Xu Huang, Wenhao Zhu, Hanxu Hu +4 authors
BenchMAX evaluates multilingual large language models across advanced capabilities like instruction following, reasoning, and code generation, revealing language-specific performance gaps.
NVIDIA, Alisson Azzolini, Hannah Brandon +42 authors
Cosmos-Reason1 models, using hierarchical and two-dimensional ontologies for physical common sense and embodied reasoning, generate embodied decisions through multimodal large language models trained in vision and Physical AI stages.
Quy-Anh Dang, Chris Ngo
Reinforcement learning enhances reasoning in small language models with limited resources, demonstrating significant performance improvements in mathematical reasoning tasks.
Shanghaoran Quan, Jiaxi Yang, Bowen Yu +14 authors
CodeElo is a standardized code generation benchmark based on CodeForces contests, providing comprehensive evaluation of LLMs' coding abilities with reliable Elo ratings.
Shaolei Zhang, Qingkai Fang, Zhe Yang +1 authors
LLaVA-Mini, a unified large multimodal model, significantly reduces computational cost and latency by compressing vision tokens to a single token before feeding them to the LLM backbone, outperforming previous models.
Wei He, Kai Han, Hang Zhou +4 authors
ROOT, a robust optimizer, enhances training stability and convergence for large language models by addressing dimensional fragility and outlier noise through adaptive Newton iterations and proximal optimization.
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