Paper Citation Record · LEDGER

What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 inbound Pith citation observations for arXiv:2312.15685.

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

pith.paper-citation-record.v1
2312.15685 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 24 of 24 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 24 of 24 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T23:02:23.356377Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T08:17:45.507249Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 39b7a24c-ea35-4a4c-b8cb-0115e76f93b3 · inbound

Yi: Open Foundation Models by 01.AI cites this paper.

Yi: Open Foundation Models by 01.AI What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:47:27.882957Z

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.

Observation 64884a92-70ce-47b1-a952-bef4b85cef9f · inbound

R.I.P.: Better Models by Survival of the Fittest Prompts cites this paper.

R.I.P.: Better Models by Survival of the Fittest Prompts What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T23:02:23.356377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d92228bd-9519-474b-a53d-5786dc40b4b8 · inbound

Muon is Scalable for LLM Training cites this paper.

Muon is Scalable for LLM Training What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 74

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T23:02:52.146183Z

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.

Observation 20cc2fb0-d477-4ab9-afcf-0459c1f8816b · inbound

A Survey of LLM $\times$ DATA cites this paper.

A Survey of LLM $\times$ DATA What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 264

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:13.435228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa12ca12-3eca-4e3d-a83c-2302705ae140 · inbound

Tag-Evol: Achieving Efficient Instruction Evolving via Tag Injection cites this paper.

Tag-Evol: Achieving Efficient Instruction Evolving via Tag Injection What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:39:05.594594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b0fda6a-c7cb-4060-b6c8-0f1741d7eeed · inbound

SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis cites this paper.

SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:45.608563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acb3b8f2-44d4-479b-81ce-0e829631ad69 · inbound

MINT: Multimodal Instruction Tuning with Multimodal Interaction Grouping cites this paper.

MINT: Multimodal Instruction Tuning with Multimodal Interaction Grouping What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:10.270829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb34d5ef-469a-4247-82e9-09d0c5df819d · inbound

What makes Reasoning Models Different? Follow the Reasoning Leader for Efficient Decoding cites this paper.

What makes Reasoning Models Different? Follow the Reasoning Leader for Efficient Decoding What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:18.489295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8389a8e9-62f0-48e8-9fc9-9919c7706c0c · inbound

Optimising Language Models for Downstream Tasks: A Post-Training Perspective cites this paper.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 139

Resolution: unresolved; no resolver link, observed 2026-08-06T22:44:44.253887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e02f8e1a-08f2-4517-b09a-dca55508671e · inbound

Data Diversification Methods In Alignment Enhance Math Performance In LLMs cites this paper.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:27.467789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23fadae7-84a9-4a52-8794-70094e0efc36 · inbound

Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains cites this paper.

Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T17:02:40.430996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 337e20e7-b511-4e8e-ac19-f5d43692ef89 · inbound

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning cites this paper.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-14T22:18:56.115559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0645f4b3-b488-4658-9294-c7930258a1b4 · inbound

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts cites this paper.

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:15:58.976457Z

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.

Observation 46bd1203-066b-4b8e-a38d-e135bde5bd6d · inbound

IRIS: Interpolative R\'enyi Iterative Self-play for Large Language Model Fine-Tuning cites this paper.

IRIS: Interpolative R\'enyi Iterative Self-play for Large Language Model Fine-Tuning What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:41:04.992626Z

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.

Observation bfff7bf6-0ae4-46d9-8cc9-b308f78bc4ee · inbound

Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions cites this paper.

Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:31:29.360515Z

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.

Observation 7ad0634e-8a79-49ec-b1c8-55b57a8c6381 · inbound

Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods cites this paper.

Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:11:53.316146Z

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.

Observation e225f5e0-8613-40dc-a674-4f995b00abd7 · inbound

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence cites this paper.

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:06:18.476018Z

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.

Observation 8c86ffa5-a81c-4b48-aa3b-f7cdb6926a57 · inbound

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence cites this paper.

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:00:54.734554Z

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.

Observation 66172598-723c-4f45-ba58-87e427c25558 · inbound

Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation cites this paper.

Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:25:53.233875Z

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.

Observation 7c00a638-c554-4ed2-ab2d-0166f0acf9bb · inbound

Let the Target Select for Itself: Data Selection via Target-Aligned Paths cites this paper.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.975709Z

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.

Observation 71f9f6b1-f6ed-44ad-a7f9-f3ae47d0c86d · inbound

HARP: Efficient Data Selection for Finetuning Large Language Models cites this paper.

HARP: Efficient Data Selection for Finetuning Large Language Models What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T16:17:09.372924Z

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.

Observation 92d5d652-c060-4e02-ae84-4b6097eb2834 · inbound

CODEBLOCK: Learning to Supervise Code at the Right Granularity cites this paper.

CODEBLOCK: Learning to Supervise Code at the Right Granularity What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T08:17:45.508782Z

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.

Observation fee19c79-4d2d-4247-867a-1eb3d023baaf · inbound

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement cites this paper.

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T13:54:03.899298Z

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Unavailable: canonical work link unavailable.

Observation af3d5e9e-20a2-4122-978d-d4b48483e4af · inbound

Training Large Language Models for Self-Explanation Faithfulness cites this paper.

Training Large Language Models for Self-Explanation Faithfulness What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:23.534271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.