Paper Citation Record · LEDGER

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

As of 11 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 100 inbound Pith citation observations for arXiv:2304.11277.

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

pith.paper-citation-record.v1
2304.11277 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-12T04:15:20.027659Z

measured 139 of 139 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 100 of 195 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:50:28.835675Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

39 of 39 outbound references displayed

verified exact13
verified fuzzy2
unresolved22
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

12
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation c858bb33-93d2-4285-9e41-9d10d3cccf40 · outbound

This paper cites torch.amp Gradient Scaling.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel torch.amp Gradient Scaling

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T04:15:20.144142Z

Source-reported events for the cited work

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Observation 99f20aa3-13f0-4aaf-8406-1d8cab85679f · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.114455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 55683cfe-58e5-415a-a57d-ca0d4e873138 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.146037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 26facca3-4f42-4bbe-92ac-8a6529cd09de · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.140258Z

Source-reported events for the cited work

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Observation d5fa683c-080d-4f90-80c6-d0c86f35f8ee · outbound

This paper cites PipeDream: Fast and Efficient Pipeline Parallel DNN Training.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel PipeDream: Fast and Efficient Pipeline Parallel DNN Training

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.068407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 80025e5d-515b-403d-b32b-77c3118d7720 · outbound

This paper cites PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.072364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a5a0a9c0-738d-4caa-8b07-d5744ad61e09 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.136516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 19519cd9-5de7-4396-a206-96633f201bf2 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.138249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2c846745-31d0-401d-82eb-b1e9bbf0b6bb · outbound

This paper cites Accurate Uncertainties for Dee p Learning Using Calibrated Regression.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Accurate Uncertainties for Dee p Learning Using Calibrated Regression

Reference 9

Resolution: verified exact; doi, observed 2026-05-12T04:15:20.052543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 57d56363-abd9-45f9-bf9f-834846974d51 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.142190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 802594f0-03fa-42fd-8360-1f2893c8fe57 · outbound

This paper cites torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.075557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e3bbfabb-1a63-4802-ac38-2f71709db5e9 · outbound

This paper cites Dynamic Tensor Rematerialization.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Dynamic Tensor Rematerialization

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.060933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e3169a5d-bf93-40d0-b0f4-2af1cd38ed7b · outbound

This paper cites Reducing Activation Recomputation in Large Transformer Models.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Reducing Activation Recomputation in Large Transformer Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.079382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0c973995-1e60-4760-86af-1b5c7d7bedf2 · outbound

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

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.083330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2daccb86-6682-45c1-a7bc-ab23caa78e08 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.152588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ed9f89a2-66a5-4cd2-9dcf-e5c462cef58a · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.104955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8d087009-fca7-4599-ac3c-f97a293091b4 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.107236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a5d5bb79-16c7-4e39-add7-b4fa1c58d15c · outbound

This paper cites Mixed Precision Training.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Mixed Precision Training

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:47:18.676998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3368299c-24dc-4a1c-becc-8a2187bd7761 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.112411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9d5bb313-75c6-4286-8ff4-27edca4f021b · outbound

This paper cites Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Reference 20

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Source-reported events for the cited work

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Observation acd1af57-cead-41b8-ba02-0ab2d37726af · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.116443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a8efa8cd-7a7c-4a77-b3f6-8e10d0755c5c · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 22

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Source-reported events for the cited work

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Observation 9991499a-50ac-4809-8f36-f5b7b2547d32 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 23

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Observation 55cdd3a3-e010-41e4-ae97-2b489fdb30f0 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 24

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Observation ef6c5eed-5240-4d78-9e35-62f78fe9bd6b · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 25

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Observation fd260cb2-3bf8-4fc7-a5ae-5bc34c989782 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 26

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Observation 9321594d-8d41-4712-8425-1159ae372d22 · outbound

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PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 27

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Observation 3f307cd8-69ce-4dfa-be83-090f8cda9b5d · outbound

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PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 28

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Source-reported events for the cited work

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Observation 5bdbc883-d190-4cdc-a035-7f1da8d8453f · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 29

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Source-reported events for the cited work

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Observation 9a86ad3d-68ba-475a-8b31-6a9eb8d66ac6 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 30

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Source-reported events for the cited work

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Observation eaed3ba3-a101-4c90-8d53-12b669390a4d · outbound

This paper cites Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.092977Z

Source-reported events for the cited work

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Observation 0ec2ff22-9726-42d1-9da9-e74451217978 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.148161Z

Source-reported events for the cited work

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Observation ce53eb44-d4f5-417e-a2ec-171fc38f833f · outbound

This paper cites GSPMD: General and Scalable Parallelization for ML Computation Graphs.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel GSPMD: General and Scalable Parallelization for ML Computation Graphs

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T12:36:36.562059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c20ebb53-9d1b-4637-8d2d-186e107e4aec · outbound

This paper cites OneFlow: Redesign the Distributed Deep Learning Framework from Scratch.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel OneFlow: Redesign the Distributed Deep Learning Framework from Scratch

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.099914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dc522653-2819-45b2-9554-edf99829462f · outbound

This paper cites DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.057051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ace95df2-273a-4228-8001-907658cae18c · outbound

This paper cites MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.102935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 777bbed9-7a19-42e8-88a5-022fb0da44a4 · outbound

This paper cites an unresolved cited work.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-05-12T04:15:20.150762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b535e0f9-74ae-4dc4-aade-f56698679fd6 · outbound

This paper cites In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22).

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T04:15:20.110389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ce8daacb-daca-4d89-9570-49e168886371 · outbound

This paper cites DeepViT: Towards Deeper Vision Transformer.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel DeepViT: Towards Deeper Vision Transformer

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.064603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation b5d7c391-6a4e-4b27-9d4f-5e4f789ece4f · inbound

OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models cites this paper.

OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-05-14T01:52:01.372311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation da2b49d5-1dd8-4c5f-ac93-510092fe9813 · inbound

YaRN: Efficient Context Window Extension of Large Language Models cites this paper.

YaRN: Efficient Context Window Extension of Large Language Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:46:51.302758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e0036ecd-cc11-4c26-9552-dc9b0b9316a7 · inbound

DoRA: Weight-Decomposed Low-Rank Adaptation cites this paper.

DoRA: Weight-Decomposed Low-Rank Adaptation PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 117

Resolution: verified exact; local_arxiv, observed 2026-05-15T22:26:21.578949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 68b7e74b-f73b-48cc-b83b-c40a0854b682 · inbound

ORPO: Monolithic Preference Optimization without Reference Model cites this paper.

ORPO: Monolithic Preference Optimization without Reference Model PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-05-16T09:34:04.722924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ac7acae5-897e-4548-b60e-6160e5c2a5f5 · inbound

OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework cites this paper.

OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-15T03:28:57.198747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4556994b-460e-4176-87fb-cc83996da08a · inbound

Scaling and evaluating sparse autoencoders cites this paper.

Scaling and evaluating sparse autoencoders PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-05-12T17:47:23.336609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b2b399e8-7da7-4e3a-87cd-eccd0d2e7cbe · inbound

Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation cites this paper.

Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.153254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a85e440b-ea3f-4fca-b118-431cb8691d8e · inbound

ProTrain: Efficient LLM Training via Memory-Aware Techniques cites this paper.

ProTrain: Efficient LLM Training via Memory-Aware Techniques PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-23T23:53:39.373647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0548510b-2da9-4a7f-a398-8612ed8eed49 · inbound

OpenVLA: An Open-Source Vision-Language-Action Model cites this paper.

OpenVLA: An Open-Source Vision-Language-Action Model PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.153254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2ab3bbfd-16b3-4d44-bbac-79476d2ab9ea · inbound

Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs cites this paper.

Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 150

Resolution: verified exact; local_arxiv, observed 2026-05-17T00:05:03.871857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c1d1b286-c181-4047-9ad0-95940478d3ed · inbound

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models cites this paper.

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 135

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Source-reported events for the cited work

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Movie Gen: A Cast of Media Foundation Models cites this paper.

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Reference 85

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Observation 572ed9ce-d888-4211-a562-39aa1b12c217 · inbound

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training cites this paper.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 17

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Open-Sora Plan: Open-Source Large Video Generation Model cites this paper.

Open-Sora Plan: Open-Source Large Video Generation Model PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 28

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MetaMorph: Multimodal Understanding and Generation via Instruction Tuning cites this paper.

MetaMorph: Multimodal Understanding and Generation via Instruction Tuning PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 196

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Source-reported events for the cited work

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Observation 4f123731-f06b-4490-ad4e-0be9ed4e9d74 · inbound

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Reference 72

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Observation b940b398-90de-4f1e-8780-c9364d5ff600 · inbound

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Adjoint sharding for very long context training of state space models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T22:50:28.835675Z

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Observation 83b24d57-cbc2-43b2-9953-7ed3c9ce7f7a · inbound

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Reference 74

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Observation 354e0a3f-1189-409a-8e77-fc09bf6937e8 · inbound

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators cites this paper.

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Reference 90

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Observation 2b629f5e-1470-412e-a753-13dd96880d73 · inbound

Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioning cites this paper.

Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioning PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 30

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Observation c6644dc4-0911-45b3-af0e-0d4be94538c3 · inbound

Decentralized Diffusion Models cites this paper.

Decentralized Diffusion Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 53

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Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation cites this paper.

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LLMic: Romanian Foundation Language Model PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 40

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Observation d47e6977-df76-49ce-a46d-39e24005b275 · inbound

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training cites this paper.

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Reference 35

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Observation ead83c85-b29d-4a54-8b74-e380f9d34488 · inbound

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Glinthawk: A Two-Tiered Architecture for Offline LLM Inference PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 57

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Observation 7f83f168-eb42-40e9-99b0-7b3cde396051 · inbound

iServe: An Intent-based Serving System for LLMs cites this paper.

iServe: An Intent-based Serving System for LLMs PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 129

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Observation 2937bea6-2252-4bee-9bc3-21c5a240be25 · inbound

Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch cites this paper.

Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 56

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Observation d624f376-44a0-4e63-a64b-555cc136f2e6 · inbound

Mixture of neural operator experts for learning boundary conditions and model selection cites this paper.

Mixture of neural operator experts for learning boundary conditions and model selection PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 27

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Observation afc1e85c-e3b7-454d-a9e5-ed011e8d30ce · inbound

Steel-LLM:From Scratch to Open Source -- A Personal Journey in Building a Chinese-Centric LLM cites this paper.

Steel-LLM:From Scratch to Open Source -- A Personal Journey in Building a Chinese-Centric LLM PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T14:50:58.981760Z

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Observation d086f6f5-ea3f-4c28-9a85-b5e8e452f58d · inbound

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MoETuner: Optimized Mixture of Expert Serving with Balanced Expert Placement and Token Routing PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-08T14:52:07.603608Z

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Observation 0d6ba78c-503e-4bca-9d2a-5947c2a03589 · inbound

Lumina-Video: Efficient and Flexible Video Generation with Multi-scale Next-DiT cites this paper.

Lumina-Video: Efficient and Flexible Video Generation with Multi-scale Next-DiT PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 85

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Observation 63a8ebcd-779d-4711-b25d-16f3495823ca · inbound

LASP-2: Rethinking Sequence Parallelism for Linear Attention and Its Hybrid cites this paper.

LASP-2: Rethinking Sequence Parallelism for Linear Attention and Its Hybrid PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-08T12:25:42.771337Z

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Observation cff3e95f-3b22-4765-bdd7-5b9ae15d0571 · inbound

Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model cites this paper.

Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-05-17T08:27:36.278797Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c4530406-ad43-4014-96c3-6cb0079e9acc · inbound

MAGI-1: Autoregressive Video Generation at Scale cites this paper.

MAGI-1: Autoregressive Video Generation at Scale PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-13T20:31:15.750971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4bd090f3-fcb5-40dd-b93b-034c8917c9d8 · inbound

ChemMLLM: Chemical Multimodal Large Language Model cites this paper.

ChemMLLM: Chemical Multimodal Large Language Model PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T15:06:08.308487Z

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Observation e783c794-8549-43fc-91f5-ba780525bd61 · inbound

AdamS: Momentum Itself Can Be A Normalizer for LLM Pretraining and Post-training cites this paper.

AdamS: Momentum Itself Can Be A Normalizer for LLM Pretraining and Post-training PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:16.870871Z

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Observation 65a0e00b-9d5f-44d8-9679-92277947ce43 · inbound

VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning cites this paper.

VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 90

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bd81e855-636f-4768-bbaf-40464cf7f24e · inbound

Small Language Models: Architectures, Techniques, Evaluation, Problems and Future Adaptation cites this paper.

Small Language Models: Architectures, Techniques, Evaluation, Problems and Future Adaptation PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 50

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Observation f1dca64a-039e-41c8-b879-848cd520fce3 · inbound

Zero-Shot Vision Encoder Grafting via LLM Surrogates cites this paper.

Zero-Shot Vision Encoder Grafting via LLM Surrogates PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:11.740399Z

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Observation 5d4bbd67-4c41-4ccf-be99-f739761dd62b · inbound

Speeding up Model Loading with fastsafetensors cites this paper.

Speeding up Model Loading with fastsafetensors PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 2023

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Observation 03303144-9887-4265-b371-af3dcdde92c0 · inbound

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism cites this paper.

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

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:15.412077Z

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Observation 6b8bbcb3-4434-4ac0-a050-371f282b13aa · inbound

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods cites this paper.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 47

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Observation af96af09-a3de-4845-a58d-785ada5276b4 · inbound

Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts cites this paper.

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Reference 30

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Unavailable: canonical work link unavailable.

Observation 16d7f9f4-8772-4e38-ae88-1376079cd462 · inbound

Rethinking Dynamic Networks and Heterogeneous Computing with Automatic Parallelization cites this paper.

Rethinking Dynamic Networks and Heterogeneous Computing with Automatic Parallelization PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 41

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Unavailable: canonical work link unavailable.

Observation 08cf2c8d-73bb-46a5-aa27-40810d94b887 · inbound

ContentV: Efficient Training of Video Generation Models with Limited Compute cites this paper.

ContentV: Efficient Training of Video Generation Models with Limited Compute PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 56

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Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library cites this paper.

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Reference 58

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Observation e0e74a71-5549-4498-b90b-3a0dd6444d4f · inbound

Congestion-Aware Path Selection for Load Balancing in AI Clusters cites this paper.

Congestion-Aware Path Selection for Load Balancing in AI Clusters PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7afd82b-89f1-4642-9c27-61a1bdf32373 · inbound

A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation cites this paper.

A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 66

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Seedance 1.0: Exploring the Boundaries of Video Generation Models cites this paper.

Seedance 1.0: Exploring the Boundaries of Video Generation Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

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Source-reported events for the cited work

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Observation 3de90bba-28ee-4bcb-af29-0399a6d7efb1 · inbound

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models cites this paper.

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

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Unavailable: canonical work link unavailable.

Observation 757b2521-884d-4004-a072-b40518b8c712 · inbound

TopK Language Models cites this paper.

TopK Language Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 49

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Source-reported events for the cited work

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Observation 79ec7187-4f1e-43aa-989f-c5c797e7a15e · inbound

Characterizing Compute-Communication Overlap in GPU-Accelerated Distributed Deep Learning: Performance and Power Implications cites this paper.

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Reference 2023

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Source-reported events for the cited work

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Observation 5ee06caa-f5a1-45f7-a2e0-9373b41413d8 · inbound

Photonic Rails in ML Datacenters cites this paper.

Photonic Rails in ML Datacenters PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 2023

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Source-reported events for the cited work

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Observation 8e3887bc-3eb4-4155-85eb-603d11156688 · inbound

Lizard: An Efficient Linearization Framework for Large Language Models cites this paper.

Lizard: An Efficient Linearization Framework for Large Language Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b9c5ddec-5c58-4f7b-aaec-d6143cd8e4f1 · inbound

Model Parallelism With Subnetwork Data Parallelism cites this paper.

Model Parallelism With Subnetwork Data Parallelism PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 49

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Observation 5dc1ac43-38ad-440f-8dcf-e4a9f8245472 · inbound

Compute Requirements for Algorithmic Innovation in Frontier AI Models cites this paper.

Compute Requirements for Algorithmic Innovation in Frontier AI Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 61

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Observation c04afad8-82fe-4773-8d67-97a4c6c89e56 · inbound

The Safety Gap Toolkit: Evaluating Hidden Dangers of Open-Source Models cites this paper.

The Safety Gap Toolkit: Evaluating Hidden Dangers of Open-Source Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T19:08:27.545460Z

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Unavailable: canonical work link unavailable.

Observation 6bb105cb-675b-4ebd-9ad6-694882815760 · inbound

Technical Report of TeleChat2, TeleChat2.5 and T1 cites this paper.

Technical Report of TeleChat2, TeleChat2.5 and T1 PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 68

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MegatronApp: Efficient and Comprehensive Management on Distributed LLM Training cites this paper.

MegatronApp: Efficient and Comprehensive Management on Distributed LLM Training PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

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Resolution: unresolved; no resolver link, observed 2026-08-06T14:01:41.855087Z

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Observation d962fbbe-3828-4961-9347-cf0e52fb0db0 · inbound

TTS-1 Technical Report cites this paper.

TTS-1 Technical Report PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T15:02:03.011140Z

Source-reported events for the cited work

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Observation 4a0f15e7-753a-4a95-8f51-f6470d7277c7 · inbound

Qwen-Image Technical Report cites this paper.

Qwen-Image Technical Report PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.153254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7c155d67-d838-4042-89da-c5a4e5893ef5 · inbound

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency cites this paper.

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 183

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.153254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b5d7a50d-8486-46ac-bcce-8160e888fd94 · inbound

Improving Large Vision and Language Models by Learning from a Panel of Peers cites this paper.

Improving Large Vision and Language Models by Learning from a Panel of Peers PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.708238Z

Source-reported events for the cited work

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Observation 5cc7efa1-388f-4400-a4b5-7d5d29c8381c · inbound

Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training cites this paper.

Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-05T11:18:08.578239Z

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Observation 858f84ff-2871-450d-a905-2e601dc4232f · inbound

Transition Models: Rethinking the Generative Learning Objective cites this paper.

Transition Models: Rethinking the Generative Learning Objective PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-05T10:19:54.532964Z

Source-reported events for the cited work

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Observation ab6be7d6-cc9f-485c-a37c-9de611f152b1 · inbound

veScale: Consistent and Efficient Tensor Programming with Eager-Mode SPMD cites this paper.

veScale: Consistent and Efficient Tensor Programming with Eager-Mode SPMD PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 70

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Observation 054f5e2c-50dd-4cac-a66d-f0ebde3eeb98 · inbound

OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling cites this paper.

OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 65

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Observation ac072154-eb76-4864-982d-1a4f08a0a88e · inbound

Training Agents Inside of Scalable World Models cites this paper.

Training Agents Inside of Scalable World Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-05-15T02:05:52.608605Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 699fd5e6-174c-4f1e-910e-447e35070f8e · inbound

TetriServe: Efficiently Serving Mixed DiT Workloads cites this paper.

TetriServe: Efficiently Serving Mixed DiT Workloads PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 39

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Observation 3691cc5f-78f8-4795-93a2-ea3a5d51e1a5 · inbound

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights cites this paper.

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-05-18T10:21:15.065547Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d58667d6-4ecc-41f7-af3a-df470bcb4ac0 · inbound

Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency cites this paper.

Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-18T08:51:09.131171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3b12ba06-9a83-4d12-9abb-cb60191caeb4 · inbound

PRISM: Probabilistic Runtime Insights and Scalable Performance Modeling for Large-Scale Distributed Training cites this paper.

PRISM: Probabilistic Runtime Insights and Scalable Performance Modeling for Large-Scale Distributed Training PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 46

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T06:30:59.737230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 265200c5-91dc-4e14-860d-2ff68633e242 · inbound

CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting cites this paper.

CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 24

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Observation 5c627fe8-8df7-40ea-ab1b-7ea9a2723bff · inbound

DMA-Latte: Expanding the Reach of DMA Offloads to Latency-bound ML Communication cites this paper.

DMA-Latte: Expanding the Reach of DMA Offloads to Latency-bound ML Communication PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-18T00:30:33.088064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 75589c0f-deb6-4f6d-8b3e-bafb4a1a9745 · inbound

Lit Silicon: A Case Where Thermal Imbalance Couples Concurrent Execution in Multiple GPUs cites this paper.

Lit Silicon: A Case Where Thermal Imbalance Couples Concurrent Execution in Multiple GPUs PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-05-17T23:12:11.892986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8490d60b-4e3c-4736-b4a7-b5ef0a4d4939 · inbound

Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs cites this paper.

Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-03T22:28:52.826267Z

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Observation 44ad16c6-e06b-4ae9-b5c7-5c67c966a172 · inbound

Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation cites this paper.

Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-03T21:34:17.759091Z

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Observation e35c2efa-d354-441a-aafa-f31c8c9a6e6d · inbound

Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer cites this paper.

Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 96

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.153254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e5b84ebc-1b0e-4bc5-bdc0-b8b55e55a319 · inbound

Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer cites this paper.

Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-03T19:47:32.963177Z

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Observation fa9d6c83-3baa-4219-896c-a82c8b9635fc · inbound

Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization cites this paper.

Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-05-16T22:53:38.251173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 99545e23-bf70-4d1b-b965-54e6cd390aff · inbound

BOOST: BOttleneck-Optimized Scalable Training Framework for Low-Rank Large Language Models cites this paper.

BOOST: BOttleneck-Optimized Scalable Training Framework for Low-Rank Large Language Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-16T23:21:21.589289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ca2ccf9a-d1f0-49bd-abe5-d3f1dac014b3 · inbound

Transition Matching Distillation for Fast Video Generation cites this paper.

Transition Matching Distillation for Fast Video Generation PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-03T10:35:06.660201Z

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Observation c9fff4ad-fcd4-4eb4-9ad9-b9b5b25e9a38 · inbound

Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding cites this paper.

Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 185

Resolution: verified exact; local_arxiv, observed 2026-05-16T04:21:29.906567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 92d54919-66cd-4825-a8e5-b7a6181c66e8 · inbound

DataStates-LLM: Scalable Checkpointing for Transformer Models Using Composable State Providers cites this paper.

DataStates-LLM: Scalable Checkpointing for Transformer Models Using Composable State Providers PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T08:30:55.350781Z

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Observation cbf4f22c-298e-4d17-b16a-2d9f5ea205ee · inbound

Advancing Open-source World Models cites this paper.

Advancing Open-source World Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 91

Resolution: verified exact; local_arxiv, observed 2026-05-16T09:07:01.078103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation be1acf60-4fa2-4e23-b65b-843ccb443109 · inbound

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation cites this paper.

ChatUMM: Robust Context Tracking for Conversational Interleaved Generation PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T03:57:32.396743Z

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Observation 5ea45fbc-a51e-4908-b928-8900acf4ef29 · inbound

DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reduce cites this paper.

DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reduce PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-03T03:13:27.566126Z

Source-reported events for the cited work

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Observation 83550848-7977-4011-b1f8-6b5ac8771e2b · inbound

Opus: Photonic Rail-Optimized Fabric in ML Datacenters cites this paper.

Opus: Photonic Rail-Optimized Fabric in ML Datacenters PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-02T23:51:02.492413Z

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Observation f4cfafaf-795e-49b0-bafe-c0b38fc668cf · inbound

Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking cites this paper.

Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-02T21:11:51.292724Z

Source-reported events for the cited work

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Observation e8b2ffe0-b410-4f20-a2d0-d32ae53856af · inbound

veScale-FSDP: Flexible and High-Performance FSDP at Scale cites this paper.

veScale-FSDP: Flexible and High-Performance FSDP at Scale PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:06:30.833896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a9f1a70e-3ada-4f53-85b9-c40cc55358d4 · inbound

Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously cites this paper.

Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-02T18:23:40.250047Z

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Observation 6a235eae-aa42-4139-88e0-e031fd9bf549 · inbound

ChopGrad: Pixel-Wise Losses for Latent Video Diffusion via Truncated Backpropagation cites this paper.

ChopGrad: Pixel-Wise Losses for Latent Video Diffusion via Truncated Backpropagation PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 79

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T09:45:23.298668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d4f75705-77e0-43b2-8ef7-5051ebe1f6b5 · inbound

Predict, Don't React: Value-Based Safety Forecasting for LLM Streaming cites this paper.

Predict, Don't React: Value-Based Safety Forecasting for LLM Streaming PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-13T11:43:31.089245Z

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Unavailable: canonical work link unavailable.

Observation 66fb65d3-2492-4557-bf24-a29cd3d8c709 · inbound

OP-GRPO: Efficient Off-Policy GRPO for Flow-Matching Models cites this paper.

OP-GRPO: Efficient Off-Policy GRPO for Flow-Matching Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 47

Resolution: metadata mismatch; local_arxiv, observed 2026-05-13T16:48:02.835106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 72d40269-a202-4f87-badb-5786d89ad808 · inbound

Sampling Parallelism for Fast and Efficient Bayesian Learning cites this paper.

Sampling Parallelism for Fast and Efficient Bayesian Learning PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.153254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8fc9e5ad-047f-4f6c-a1e8-dd21007c7af6 · inbound

DeepStack: Scalable and Accurate Design Space Exploration for Distributed 3D-Stacked AI Accelerators cites this paper.

DeepStack: Scalable and Accurate Design Space Exploration for Distributed 3D-Stacked AI Accelerators PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 122

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.153254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 104f6be9-8249-4981-bc77-317912439ab0 · inbound

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU cites this paper.

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 15

Resolution: malformed identifier; arxiv_id, observed 2026-05-12T04:15:20.153254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9ec0c8af-f80d-441a-9056-e60530b3732f · inbound

ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads cites this paper.

ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.153254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 39de9997-d347-4041-8aac-0eea2c8ab661 · inbound

Continuous Adversarial Flow Models cites this paper.

Continuous Adversarial Flow Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 82

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T04:15:20.153254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b2f940ef-fad4-45de-a12e-be7f400cce3d · inbound

Relax: An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scale cites this paper.

Relax: An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scale PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.153254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.