Paper Citation Record · LEDGER

torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2004.09910.

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

pith.paper-citation-record.v1
2004.09910 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:06:02.296553Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T02:39:24.645515Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

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

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

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 2026-08-07T06:34:17.273281+00:00.

Observation 6d41002f-63ff-4917-bd41-0c90d622bc63 · 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 torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:18:20.810027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a1ef491-8ecf-4fb7-a7a6-e8b39ef54638 · inbound

FlashDP: Private Training Large Language Models with Efficient DP-SGD cites this paper.

FlashDP: Private Training Large Language Models with Efficient DP-SGD torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:02.296553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 004d58b6-965b-43dd-95ff-bb682bec1673 · inbound

Distributed Deep Learning using Stochastic Gradient Staleness cites this paper.

Distributed Deep Learning using Stochastic Gradient Staleness torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T05:21:33.401623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2915599b-5094-481d-9edb-1be8b4c07436 · inbound

Pulse: Training Acceleration for Large Diffusion Models with Automatic Pipeline Parallelism cites this paper.

Pulse: Training Acceleration for Large Diffusion Models with Automatic Pipeline Parallelism torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-04T02:39:24.647250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.