1 citations · 1 across the 4 of their papers we have counts for
12 papers
The Red Queen Gödel Machine: Co-Evolving Agents and Their Evaluators
Alex Iacob, Andrej JovanoviÄ, William F. Shen +10
Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains. However, their search methods generally assume a…
FoMoE: Breaking the Full-Replica Barrier with a Federation of MoEs
Lorenzo Sani, Zeyu Cao, Meghdad Kurmanji +5
Pre-training Large Language Models (LLMs) typically demands large-scale infrastructure with tightly coupled hardware accelerators. Mixture-of-Experts (MoEs) architectures partially…
LoRDO: Distributed Low-Rank Optimization with Infrequent Communication
Andrej JovanoviÄ, Alex Iacob, Mher Safaryan +6
Distributed training of foundation models via is limited by interconnect bandwidth. While infrequent communication strategies reduce synchronization frequency, they…
Photon: Federated LLM Pre-Training
Lorenzo Sani, Alex Iacob, Zeyu Cao +8
Scaling large language models (LLMs) demands extensive data and computing resources, which are traditionally constrained to data centers by the high-bandwidth requirements of distr…
Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systems
Nurbek Tastan, Alex Iacob, Lorenzo Sani +4
Multi-agent systems can solve complex tasks through collaboration between multiple Large Language Model agents. Existing collaboration frameworks typically operate in either a para…
Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods
Wanru Zhao, Yihong Chen, Yuzhi Tang +6
Data curation is a critical yet under-explored area in large language model (LLM) training. Existing methods, such as data selection and mixing, operate in an offline paradigm, det…