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cs.LG2026

Agora: Collective and Permissionless Internet-Scale Pretraining of Large Language Models

Gil Avraham, Violetta Shevchenko, Hadi Mohaghegh Dolatabadi +9

Agora is a system that enables large language models to be trained collectively across many heterogeneous, internet‑connected GPUs, using pipeline‑parallel sharding and fault‑toler…

cs.LG2026

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism

Sameera Ramasinghe, Thalaiyasingam Ajanthan, Gil Avraham +2

Scaling models has led to significant advancements in deep learning, but training these models in decentralized settings remains challenging due to communication bottlenecks. While…

cs.LG2026

Factored Gossip DiLoCo: Reducing Blocking Communication in DiLoCo

Chamin Hewa Koneputugodage, Thalaiyasingam Ajanthan, Sameera Ramasinghe +7

To make large-scale distributed training practical outside high-bandwidth datacenters, we must reduce blocking, high-volume synchronization. While DiLoCo communicates infrequently,…

cs.LG2026

Taming Curvature: Architecture Warm-Up for Stable Transformer Training

Sameera Ramasinghe, Ajanthan Thalaiyasingam, Hadi Mohaghegh Dolatabadi +6

Training billion-parameter Transformers is often brittle, with transient loss spikes and divergence that waste compute. Even though the recently developed Edge of Stability (EoS) t…

cs.LG2026

Mixtures of Subspaces for Bandwidth Efficient Context Parallel Training

Sameera Ramasinghe, Ajanthan Thalaiyasingam, Hadi Mohaghegh Dolatabadi +5

Pretraining language models with extended context windows enhances their ability to leverage rich information during generation. Existing methods split input sequences into chunks,…

cs.LG2026

Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization

Alexander Long, Chamin Hewa Koneputugodage, Thalaiyasingam Ajanthan +5

We consider a decentralized setup in which the participants collaboratively train and serve a large neural network, and where each participant only processes a subset of the model.…