Paper Citation Record · LEDGER

A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2410.18779.

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

pith.paper-citation-record.v1
2410.18779 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:54:39.359172Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T08:31:26.433266Z

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 26b2ce15-01b2-4115-8fea-05981e86e29f · inbound

LLM Pretraining with Continuous Concepts cites this paper.

LLM Pretraining with Continuous Concepts A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:39.359172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaed84e0-cde7-4712-b087-ad4f596e14e9 · inbound

Distillation Scaling Laws cites this paper.

Distillation Scaling Laws A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-08T04:33:08.876380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cdb3e0a-689f-445c-abc7-09694b6b1845 · inbound

Universal Model Routing for Efficient LLM Inference cites this paper.

Universal Model Routing for Efficient LLM Inference A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-07T23:48:01.163720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70e14c5c-ba69-43c5-9b61-afb4f22427b7 · inbound

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining cites this paper.

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T14:09:49.458727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ffcb539-a804-4f74-8f51-b90df1c1d30c · inbound

AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up cites this paper.

AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:29.600966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbd54a30-b13d-4d47-bf25-271412d41ba7 · inbound

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges cites this paper.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs

Reference 107

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:48.239449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 986e5630-acd8-489d-88ee-9c0e0a79ee7b · inbound

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training cites this paper.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:30.658443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf0351fe-dbd6-4e8d-8dd8-f2489427ada3 · inbound

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs cites this paper.

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-10T02:53:29.794153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2aa829e3-e744-462d-bc19-4e80ef4a98c9 · inbound

CoDistill-GRPO: A Co-Distillation Recipe for Efficient Group Relative Policy Optimization cites this paper.

CoDistill-GRPO: A Co-Distillation Recipe for Efficient Group Relative Policy Optimization A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:31:26.435545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.