Paper Citation Record · LEDGER

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

As of 15 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 3 inbound Pith citation observations for arXiv:2506.01260.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.01260 v3

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:55:15.447206Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T18:53:47.187437Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-28T19:42:36.073867Z

Reference resolution

63 of 63 outbound references displayed

verified exact1
verified fuzzy32
unresolved30
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 592c6e93-48c5-42ed-a329-6175452b8bf2 · outbound

This paper cites Transformers learn through gradual rank increase.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Transformers learn through gradual rank increase

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:19.678630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c64f9b57-fbda-4ae6-9d11-f765a78e9e18 · outbound

This paper cites Qsgd: Communication-efficient sgd via gradient quantization and encoding.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Qsgd: Communication-efficient sgd via gradient quantization and encoding

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:19.504881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d9f4a23d-1c97-4aff-8476-1549f87bd169 · outbound

This paper cites Dissecting adam: The sign, magnitude and variance of stochastic gradients.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Dissecting adam: The sign, magnitude and variance of stochastic gradients

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:19.330916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b0970cf0-a55e-4c8e-bd80-4d94511bc6d9 · outbound

This paper cites signsgd: Compressed optimisation for non-convex problems.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism signsgd: Compressed optimisation for non-convex problems

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:19.199270Z

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Observation f0b24b53-ce89-4eeb-8b57-0b237e83940b · outbound

This paper cites Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning

Reference 5

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Source-reported events for the cited work

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Observation 1add04da-defb-470c-90cd-02b6b5ca622c · outbound

This paper cites Does compressing activations help model parallel training? Proceedings of Machine Learning and Systems, 6: 0 239--252, 2024.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Does compressing activations help model parallel training? Proceedings of Machine Learning and Systems, 6: 0 239--252, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:19.045514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c8e9404a-916d-4c17-804c-81fdebbac5f7 · outbound

This paper cites Low-rank gradient descent.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Low-rank gradient descent

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:18.889782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 68b31344-16d3-455d-bc0d-ca08bcbba0c4 · outbound

This paper cites DeepSeek LLMs , 2023.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism DeepSeek LLMs , 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:18.694587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4b22ed7a-9bb6-4124-bbc1-0c5aaec5abd2 · outbound

This paper cites A Simple Convergence Proof of Adam and Adagrad.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism A Simple Convergence Proof of Adam and Adagrad

Reference 9

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Source-reported events for the cited work

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Observation f320d63e-c91c-46e4-9a45-535ee198c85f · outbound

This paper cites 8-bit Optimizers via Block-wise Quantization.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism 8-bit Optimizers via Block-wise Quantization

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ca8007e-5add-4001-a83b-f107719004ae · outbound

This paper cites Distributed deep learning in open collaborations.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Distributed deep learning in open collaborations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:18.526941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f036de15-21bb-48ec-810e-d27ca7cb503e · outbound

This paper cites Attention is not all you need: Pure attention loses rank doubly exponentially with depth.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Attention is not all you need: Pure attention loses rank doubly exponentially with depth

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:18.456406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6e906f3c-ff5a-495d-ab76-86472ce8777e · outbound

This paper cites DiLoCo: Distributed Low-Communication Training of Language Models.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism DiLoCo: Distributed Low-Communication Training of Language Models

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 657f8464-4b43-4ef3-a757-c4b350906cce · outbound

This paper cites The Llama 3 Herd of Models.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism The Llama 3 Herd of Models

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1517c73-b813-4383-9d2b-c86d6c3edb31 · outbound

This paper cites Openwebtext corpus.

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

Reference 15

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Source-reported events for the cited work

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Observation 4dd99cc9-a68d-4bba-b2a7-7be5f5266f9b · outbound

This paper cites Gradient Descent Happens in a Tiny Subspace.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Gradient Descent Happens in a Tiny Subspace

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af20aac7-bd93-48cd-8526-2d1f2499c4b3 · outbound

This paper cites Training compute-optimal large language models.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Training compute-optimal large language models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:18.325287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 683d281f-56b2-437a-8a1e-24729c94ee26 · outbound

This paper cites Gpipe: Efficient training of giant neural networks using pipeline parallelism.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Gpipe: Efficient training of giant neural networks using pipeline parallelism

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4c27129-cdab-49cd-99c9-b8aa62ec37ca · outbound

This paper cites Error feedback fixes signsgd and other gradient compression schemes.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Error feedback fixes signsgd and other gradient compression schemes

Reference 19

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Source-reported events for the cited work

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Observation 64d9c80c-1fe9-42e7-bb18-22149f1a0fc3 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Adam: A Method for Stochastic Optimization

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9e3c9a4-ab2e-456e-aeaa-ce89aaa02670 · outbound

This paper cites Big transfer (bit): General visual representation learning.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Big transfer (bit): General visual representation learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:18.191509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 28c35edf-fce5-4f31-9f2e-739c15297eb7 · outbound

This paper cites Decentralized stochastic optimization and gossip algorithms with compressed communication.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Decentralized stochastic optimization and gossip algorithms with compressed communication

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:18.111648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4e2ef206-c2d0-4d33-bc94-9ab270d35c10 · outbound

This paper cites A unified theory of decentralized sgd with changing topology and local updates.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism A unified theory of decentralized sgd with changing topology and local updates

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:18.022141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 886cc201-60f9-4848-93b9-49a731d1a1f7 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Imagenet classification with deep convolutional neural networks

Reference 24

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Source-reported events for the cited work

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Observation 0d731396-90cc-43ae-bf02-63c8765e4443 · outbound

This paper cites Convergence of adam under relaxed assumptions.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Convergence of adam under relaxed assumptions

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:17.906167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4cc5152f-e7a0-4997-a5f0-5e9f547c6b78 · outbound

This paper cites Learning on transformers is provable low-rank and sparse: A one-layer analysis.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Learning on transformers is provable low-rank and sparse: A one-layer analysis

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:17.727061Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 19dd66f6-69f6-4bb8-a90d-8468618ce0ff · outbound

This paper cites PyTorch Distributed: Experiences on Accelerating Data Parallel Training.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Reference 27

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Source-reported events for the cited work

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Observation 732b3de9-bc91-4a0d-a6ca-47b1d0f59ec7 · outbound

This paper cites Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:17.641992Z

Source-reported events for the cited work

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Observation 0bd52271-10b8-4d50-a78c-22c4a00bb911 · outbound

This paper cites TorchTitan: One-stop PyTorch native solution for production ready LLM pre-training.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism TorchTitan: One-stop PyTorch native solution for production ready LLM pre-training

Reference 29

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Source-reported events for the cited work

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Observation e8cf2029-b266-4302-bfb0-99f8904247e0 · outbound

This paper cites Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Reference 30

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Source-reported events for the cited work

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Observation 6f8f90bb-c2ea-4452-9de8-6966bae1c399 · outbound

This paper cites Adam$^+$: A Stochastic Method with Adaptive Variance Reduction.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Adam$^+$: A Stochastic Method with Adaptive Variance Reduction

Reference 31

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9d2acdea-e1c8-4755-8b44-50e70a49009d · outbound

This paper cites Decoupled Weight Decay Regularization.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Decoupled Weight Decay Regularization

Reference 32

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Source-reported events for the cited work

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Observation 0dc712ca-9b3e-4e6c-99e1-0292bccb2473 · outbound

This paper cites Pointer sentinel mixture models, 2016.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Pointer sentinel mixture models, 2016

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c77da94-f270-4761-bd07-7264e30cae9f · outbound

This paper cites Efficient large-scale language model training on gpu clusters using megatron-lm.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Efficient large-scale language model training on gpu clusters using megatron-lm

Reference 34

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Source-reported events for the cited work

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Observation fc818491-3547-47da-94c9-deac8862c8f2 · outbound

This paper cites Decoupled momentum optimization.

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

Reference 35

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Observation 23dbdad6-0ba3-4c34-afb7-f26c30eb8305 · outbound

This paper cites Ai and compute.

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

Reference 36

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Observation 06aeac6e-0e51-469d-9d5e-ff5d00d31a4f · outbound

This paper cites an unresolved cited work.

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

Reference 37

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Source-reported events for the cited work

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Observation 084b05a6-4f5a-47db-801b-fb9d6b952adf · outbound

This paper cites PanGu-{\Sigma}: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism PanGu-{\Sigma}: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing

Reference 38

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Source-reported events for the cited work

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Observation 95d26b16-c41d-4c86-b777-8211317f30dd · outbound

This paper cites Activations and gradients compression for model-parallel training.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Activations and gradients compression for model-parallel training

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:17.445422Z

Source-reported events for the cited work

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Observation 847e1442-37ef-42f9-b5bd-b2febdfc801f · outbound

This paper cites Towards crowdsourced training of large neural networks using decentralized mixture-of-experts.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Towards crowdsourced training of large neural networks using decentralized mixture-of-experts

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:17.356432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ea379800-0903-4da6-9da1-a2b0399d6d36 · outbound

This paper cites Moshpit sgd: Communication-efficient decentralized training on heterogeneous unreliable devices.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Moshpit sgd: Communication-efficient decentralized training on heterogeneous unreliable devices

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:17.271867Z

Source-reported events for the cited work

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Observation ea093586-11fc-473b-a71d-e4a9a660c855 · outbound

This paper cites Swarm parallelism: Training large models can be surprisingly communication-efficient.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Swarm parallelism: Training large models can be surprisingly communication-efficient

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:17.187559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 01da2141-3467-4517-86e1-04d69921597a · outbound

This paper cites Inheritune: Training smaller yet more attentive language models, 2024.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Inheritune: Training smaller yet more attentive language models, 2024

Reference 43

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Source-reported events for the cited work

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Observation 28adb1f0-5240-469f-a239-f90147d83e63 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 44

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c5ed812-b146-4583-acd0-2f391285cda7 · outbound

This paper cites 1-bit adam: Communication efficient large-scale training with adam’s convergence speed.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism 1-bit adam: Communication efficient large-scale training with adam’s convergence speed

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:17.120045Z

Source-reported events for the cited work

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Observation d3f47848-25af-49fb-8b0c-f25666683362 · outbound

This paper cites Scaling the summit: deploying the world’s fastest supercomputer.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Scaling the summit: deploying the world’s fastest supercomputer

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:17.055192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3670813a-87e0-4f51-ae8f-e6a7fff0a382 · outbound

This paper cites Powersgd: Practical low-rank gradient compression for distributed optimization.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Powersgd: Practical low-rank gradient compression for distributed optimization

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:14.772989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcaaf8b2-61dc-427c-bae4-f0c9bf3f473a · outbound

This paper cites Kalman Gradient Descent: Adaptive Variance Reduction in Stochastic Optimization.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Kalman Gradient Descent: Adaptive Variance Reduction in Stochastic Optimization

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:14.838055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6874d0b4-192d-415b-96a5-3531593fc3fa · outbound

This paper cites Variance reduction for stochastic gradient optimization.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Variance reduction for stochastic gradient optimization

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:16.949401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7f84e35e-cba5-42b3-8109-1a64f96e43ab · outbound

This paper cites Atomo: Communication-efficient learning via atomic sparsification.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Atomo: Communication-efficient learning via atomic sparsification

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:16.890495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cc829027-62a7-422f-915c-8e12a8b7961c · outbound

This paper cites Pufferfish: Communication-efficient models at no extra cost.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Pufferfish: Communication-efficient models at no extra cost

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:16.804411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2193dd3a-4213-465e-b126-d8717960a4fe · outbound

This paper cites Efficient distributed learning with sparsity.

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

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:16.716469Z

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Observation d4b889cc-8acb-4882-82c0-a01bebc72cff · outbound

This paper cites Cocktailsgd: Fine-tuning foundation models over 500mbps networks.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Cocktailsgd: Fine-tuning foundation models over 500mbps networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:16.633501Z

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Observation bc257cec-5b28-4b92-8d2c-e30af183a949 · outbound

This paper cites Gradient sparsification for communication-efficient distributed optimization.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Gradient sparsification for communication-efficient distributed optimization

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:16.537448Z

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Observation 74a6b4c1-4926-4340-bc05-6b093c11153f · outbound

This paper cites Error compensated quantized sgd and its applications to large-scale distributed optimization.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Error compensated quantized sgd and its applications to large-scale distributed optimization

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:16.430575Z

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Observation d054a76c-f635-49b6-b8ac-0faffc2dc425 · outbound

This paper cites A Spectral Condition for Feature Learning.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism A Spectral Condition for Feature Learning

Reference 56

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Observation fc8965be-0e92-426b-80f8-55b176dddbf8 · outbound

This paper cites Stochastic Gradient Variance Reduction by Solving a Filtering Problem.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Stochastic Gradient Variance Reduction by Solving a Filtering Problem

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:15.205516Z

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Observation e20f2efd-df6b-429e-8765-f24f65a631e5 · outbound

This paper cites Decentralized training of foundation models in heterogeneous environments.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Decentralized training of foundation models in heterogeneous environments

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:16.241381Z

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Observation 62d9db7f-7614-4cbd-9382-b7706f10294e · outbound

This paper cites Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance

Reference 59

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Observation 54ddbd28-fa93-40ef-a94b-65c092a6f745 · outbound

This paper cites ZerO Initialization: Initializing Neural Networks with only Zeros and Ones.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism ZerO Initialization: Initializing Neural Networks with only Zeros and Ones

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:15.314868Z

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Observation 529c2762-7a6b-4c2d-b08d-0186b91e35ff · outbound

This paper cites GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Reference 61

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This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 62

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Observation 84ae9fa8-ad4c-46f6-8881-7f811796074d · outbound

This paper cites Aligning books and movies: Towards story-like visual explanations by watching movies and reading books.

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Aligning books and movies: Towards story-like visual explanations by watching movies and reading books

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:16.124732Z

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Pith citing papers

Observation 9a3fc158-0571-435a-9652-263a421bd0b8 · inbound

On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning cites this paper.

On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-07-09T00:19:35.965961Z

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Observation 5ab30c17-9f1e-48dc-b734-49a5b9295181 · inbound

ResBM: Residual Bottleneck Models for Low-Bandwidth Pipeline Parallelism cites this paper.

ResBM: Residual Bottleneck Models for Low-Bandwidth Pipeline Parallelism Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-09T00:19:35.965961Z

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Observation d3d7d422-f931-43f2-85ba-457aed58aacd · inbound

GNMR: Runtime Stability Control for Low-Precision Large Language Model Training cites this paper.

GNMR: Runtime Stability Control for Low-Precision Large Language Model Training Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism

Reference 45

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