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Paper Citation Record · LEDGER

Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.19580.

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

pith.paper-citation-record.v1
2407.19580 v4

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 2026-08-16T06:30:59.297886+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:15:34.344195Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 802fb3e0-9e66-48e9-b997-f53562b65e24 · inbound

Guiding Data Collection via Factored Scaling Curves cites this paper.

Guiding Data Collection via Factored Scaling Curves Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:34.344195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:15:34.344195Z digest=sha256:3b2539c3e22fde48aacfdbbffb0c8835ddcd31ffcb4bf2bfeec3d7216c980141

Observation 100e7885-cf8d-4f55-9842-a10b1a9e538a · inbound

Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking cites this paper.

Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-07T13:06:25.885072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:06:25.885072Z digest=sha256:872562cbae0c0abe5c4b2ec2ca3817d3cac5b05e9fbb674f09c65e649074fd6e

Observation a8c20ec4-ccf4-47d3-8a17-dc54f6c81886 · inbound

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

Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-03T22:28:48.025018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:28:48.025018Z digest=sha256:bbf52ff1654a30c9afff821b7d40f33ef563199ea9eac8648e40d13a69a00505

Observation 116cc25d-7f2a-4a84-818c-c6e78681c2d1 · inbound

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization cites this paper.

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures

Reference 61

Resolution
verified exact
arxiv_id, observed 2026-05-10T20:30:49.050178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-10T18:06:46.131725Z digest=sha256:ad84d659e1096c095132e97f374d868fc86678ec5ff7029a6580a4bdd1be9071

Observation 87315dc4-7279-43f1-8d64-f67ecb46c128 · inbound

Learning as Reasoning Unfolds: Progressive Rollout Allocation for Efficient Reinforcement Learning cites this paper.

Learning as Reasoning Unfolds: Progressive Rollout Allocation for Efficient Reinforcement Learning Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-01T06:14:27.668792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T06:14:27.668792Z digest=sha256:61d8966ad072d8987bf4e0d9ff021a5c9aa831e4aa99e998f2f40e25b094e572