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Paper Citation Record · LEDGER

Early Weight Averaging meets High Learning Rates for LLM Pre-training

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2306.03241.

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

pith.paper-citation-record.v1
2306.03241 v2

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 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:01:02.219772Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:48:39.767920Z

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 d3ca1c16-70d3-4647-af9a-d84a40b59156 · inbound

Task Vector Bases: A Unified and Scalable Framework for Compressed Task Arithmetic cites this paper.

Task Vector Bases: A Unified and Scalable Framework for Compressed Task Arithmetic Early Weight Averaging meets High Learning Rates for LLM Pre-training

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-09T17:01:02.219772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T17:01:02.219772Z digest=sha256:0ddd8b3d37d63e97bdd723cfebd781b9a0029a8b0df32ba9caea51c018826ec9

Observation 73da079d-8e71-47cf-b9bd-b020e6655d68 · inbound

WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training cites this paper.

WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training Early Weight Averaging meets High Learning Rates for LLM Pre-training

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-06T14:49:40.325724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:49:40.325724Z digest=sha256:40a4f0792994ab07f3f168fc2bfb3770df5405946c0c59a0be536ed242a7313d

Observation fab7f62e-802b-40b3-8ed6-e0ed55b58e9d · inbound

Low-rank Optimization Trajectories Modeling for LLM RLVR Acceleration cites this paper.

Low-rank Optimization Trajectories Modeling for LLM RLVR Acceleration Early Weight Averaging meets High Learning Rates for LLM Pre-training

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:16:04.466907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T16:07:20.180349Z digest=sha256:e8211d17d12ed67dddfa740f550df325cc1ff22e3209787a6b60b03ccfebb189

Observation c0daee11-efb6-48ef-a293-c870e4fc41dc · inbound

ORBIT: Preserving Foundational Language Capabilities in GenRetrieval via Origin-Regulated Merging cites this paper.

ORBIT: Preserving Foundational Language Capabilities in GenRetrieval via Origin-Regulated Merging Early Weight Averaging meets High Learning Rates for LLM Pre-training

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-13T05:12:18.084240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-13T05:07:59.947540Z digest=sha256:db40bdca3f2c96c613cdb1d744b143086544585051c9e022625df90b9def05a6

Observation 7b74ba05-f433-45dd-a8a7-74e99bc5d5d4 · inbound

Optimizing Visual Generative Models via Distribution-wise Rewards cites this paper.

Optimizing Visual Generative Models via Distribution-wise Rewards Early Weight Averaging meets High Learning Rates for LLM Pre-training

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-07-03T16:48:39.769256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-07-03T16:39:12.711424Z digest=sha256:8b5286f21e183e5e9d20d4fd49bce89e096e4a99e74dc90a0a606dae1d269b86