12 papers
PLoRA: Efficient Concurrent LoRA Training for Large Language Models
Minghao Yan, Zhuang Wang, Zhen Jia +2
Low-Rank Adaptation (LoRA) has gained popularity as a fine-tuning approach for Large Language Models (LLMs) due to its low resource requirements and good performance. While numerou…
Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs
Song Bian, Tao Yu, Shivaram Venkataraman +1
Scaling the number of parameters and the size of training data has proven to be an effective strategy for improving large language model (LLM) performance. Yet, as these models gro…
PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents
Minghao Yan, Bo Peng, Benjamin Coleman +11
Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. This limits adaptation in prac…
AI Benchmark Democratization and Carpentry
Gregor von Laszewski, Wesley Brewer, Jeyan Thiyagalingam +28
Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring d…
An MLCommons Scientific Benchmarks Ontology
Ben Hawks, Gregor von Laszewski, Matthew D. Sinclair +6
Scientific machine learning research spans diverse domains and data modalities, yet existing benchmark efforts remain siloed and lack standardization. This makes novel and transfor…
What Limits Agentic Systems Efficiency?
Song Bian, Minghao Yan, Anand Jayarajan +2
Large Language Models (LLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated strong reasoning capabilities. To further enhance LLM capabilities, recent agentic systems, such…