8 papers
Experience Graphs: The Data Foundation for Self-Improving Agents
Gang Liao, Yujia He, Abdullah Ozturk +22
The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures. We argue that long-horizon agentic tasks -- c…
AIRA_2: Overcoming Bottlenecks in AI Research Agents
Karen Hambardzumyan, Nicolas Baldwin, Edan Toledo +22
Existing research has identified three structural performance bottlenecks in AI research agents: (1) synchronous single-GPU execution constrains sample throughput, limiting the ben…
To 2:4 Sparsity and Beyond: Neuron-level Activation Function to Accelerate LLM Pre-Training
Meghana Madhyastha, Daniel Haziza, Jesse Cai +3
Trainings of Large Language Models are generally bottlenecked by matrix multiplications. In the Transformer architecture, a large portion of these operations happens in the Feed Fo…
Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework
Dong Wang, Yang Li, Ansong Ni +12
Synthetic data has become increasingly important for training large language models, especially when real data is scarce, expensive, or privacy-sensitive. Many such generation task…
Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls
Feiyang Kang, Newsha Ardalani, Michael Kuchnik +7
Training data plays a crucial role in Large Language Models (LLM) scaling, yet high quality data is of limited supply. Synthetic data techniques offer a potential path toward sides…
Quagmires in SFT-RL Post-Training: When High SFT Scores Mislead and What to Use Instead
Feiyang Kang, Michael Kuchnik, Karthik Padthe +4
In post-training for reasoning Large Language Models (LLMs), the current state of practice trains LLMs in two independent stages: Supervised Fine-Tuning (SFT) and Reinforcement Lea…