Paper Citation Record · LEDGER

A Scaling Law for Token Efficiency in LLM Fine-Tuning Under Fixed Compute Budgets

As of 24 August 2026, this Paper Citation Record lists 8 of 8 outbound references and 0 inbound Pith citation observations for arXiv:2505.06150.

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

pith.paper-citation-record.v1
2505.06150 v2

Coverage vector

measured 8 of 8 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:50:59.014613Z

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

8 of 8 outbound references displayed

verified exact0
verified fuzzy1
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 35cb107b-ae44-4577-9084-7c0059f82280 · outbound

This paper cites Open llm leaderboard.

A Scaling Law for Token Efficiency in LLM Fine-Tuning Under Fixed Compute Budgets Open llm leaderboard

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T22:50:58.978831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c614ceb5-cd32-4d8d-a375-dc721e4a355d · outbound

This paper cites Measuring Massive Multitask Language Understanding.

A Scaling Law for Token Efficiency in LLM Fine-Tuning Under Fixed Compute Budgets Measuring Massive Multitask Language Understanding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T22:50:58.984696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 447d37cd-c4b6-4e7b-9b91-351bb04d09e8 · outbound

This paper cites Scaling Laws for Transfer.

A Scaling Law for Token Efficiency in LLM Fine-Tuning Under Fixed Compute Budgets Scaling Laws for Transfer

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T22:50:58.989961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e20beeb9-581f-4604-a796-183ba7b60e3a · outbound

This paper cites Training Compute-Optimal Large Language Models.

A Scaling Law for Token Efficiency in LLM Fine-Tuning Under Fixed Compute Budgets Training Compute-Optimal Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:50:58.994850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb3d0909-7831-4010-bcce-f50cb72d6211 · outbound

This paper cites Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs.

A Scaling Law for Token Efficiency in LLM Fine-Tuning Under Fixed Compute Budgets Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T22:50:59.000095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07996391-8918-4b65-b05b-444e8d2e2c5a · outbound

This paper cites Reducing biases towards minoritized populations in medical curricular content via artificial intelligence for fairer health outcomes.

A Scaling Law for Token Efficiency in LLM Fine-Tuning Under Fixed Compute Budgets Reducing biases towards minoritized populations in medical curricular content via artificial intelligence for fairer health outcomes

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:59.127642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation eae15ada-2bba-4863-85c6-5e82788dd2bf · outbound

This paper cites When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method.

A Scaling Law for Token Efficiency in LLM Fine-Tuning Under Fixed Compute Budgets When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T22:50:59.009942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29a47e85-d849-4bff-8b45-5d97aae3fb8b · outbound

This paper cites write newline.

A Scaling Law for Token Efficiency in LLM Fine-Tuning Under Fixed Compute Budgets write newline

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:50:59.014613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.