Paper Citation Record · LEDGER

Online Data Selection for Instruction Tuning via Gaussian Processes

As of 5 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2606.30077.

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

pith.paper-citation-record.v1
2606.30077 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T07:01:16.854965Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

44 of 44 outbound references displayed

verified exact8
verified fuzzy0
unresolved34
parse uncertain0
malformed identifier2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation adc69494-8b91-40f8-832d-9cc6d2ac16ea · outbound

This paper cites GPT-4 Technical Report.

Online Data Selection for Instruction Tuning via Gaussian Processes GPT-4 Technical Report

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-06-30T07:04:21.067207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1ef8d5e5-5a46-45a0-9a6a-a4c112cbfc72 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Online Data Selection for Instruction Tuning via Gaussian Processes DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-06-30T07:04:21.077748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cbc2a4b9-7748-4c8d-a53b-c45daaa82e5d · outbound

This paper cites MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs.

Online Data Selection for Instruction Tuning via Gaussian Processes MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:04:21.074960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 562c105b-d024-43a0-93f3-a0ad531ba081 · outbound

This paper cites Should chatgpt be biased? challenges and risks of bias in large language models.

Online Data Selection for Instruction Tuning via Gaussian Processes Should chatgpt be biased? challenges and risks of bias in large language models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d7fc474-90c9-47db-b8f2-2ddf9c4dff52 · outbound

This paper cites Data shapley: Equitable valuation of data for machine learning.

Online Data Selection for Instruction Tuning via Gaussian Processes Data shapley: Equitable valuation of data for machine learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2f1af9e-f7d5-429a-aad6-0ebd91261d23 · outbound

This paper cites Understanding black-box predictions via influence functions.

Online Data Selection for Instruction Tuning via Gaussian Processes Understanding black-box predictions via influence functions

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 910a9e0c-8efa-4ab2-b5ac-b9455979217f · outbound

This paper cites Greats: Online selection of high-quality data for llm training in every iteration.Advances in Neural Information Processing Systems, 37:131197–131223, 2024.

Online Data Selection for Instruction Tuning via Gaussian Processes Greats: Online selection of high-quality data for llm training in every iteration.Advances in Neural Information Processing Systems, 37:131197–131223, 2024

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd1fe518-8311-4bb2-8846-a30cf81b6883 · outbound

This paper cites Lava: Data valuation without pre-specified learning algorithms.

Online Data Selection for Instruction Tuning via Gaussian Processes Lava: Data valuation without pre-specified learning algorithms

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1046bd87-bd53-44e1-99a2-5c6127f148a5 · outbound

This paper cites Sava: Scalable learning-agnostic data valuation.

Online Data Selection for Instruction Tuning via Gaussian Processes Sava: Scalable learning-agnostic data valuation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d20521ce-c9fc-4b35-9dd0-3c970a985b3f · outbound

This paper cites Kairos: Scalable model-agnostic data valuation.Advances in Neural Information Processing Systems, 2025.

Online Data Selection for Instruction Tuning via Gaussian Processes Kairos: Scalable model-agnostic data valuation.Advances in Neural Information Processing Systems, 2025

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0766dca-3b9a-4814-9f00-b1780fa3a0a6 · outbound

This paper cites Shapley-based data valuation for weighted k-nearest neighbors.

Online Data Selection for Instruction Tuning via Gaussian Processes Shapley-based data valuation for weighted k-nearest neighbors

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9209e504-eff8-4ea8-b164-1e703d27fc41 · outbound

This paper cites What is your data worth to gpt? llm-scale data valuation with influence functions.

Online Data Selection for Instruction Tuning via Gaussian Processes What is your data worth to gpt? llm-scale data valuation with influence functions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 447c67ff-d3b7-4c81-b7fa-0f226f2461e0 · outbound

This paper cites Dataset distillation.

Online Data Selection for Instruction Tuning via Gaussian Processes Dataset distillation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d3f2ed4-b0c1-4e54-b669-721eb6ba0df0 · outbound

This paper cites Coresets for data-efficient training of machine learning models.

Online Data Selection for Instruction Tuning via Gaussian Processes Coresets for data-efficient training of machine learning models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

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Observation 443623fa-8ddc-4b2b-8604-4f3677256921 · outbound

This paper cites Estimating training data influence by tracing gradient descent.Advances in Neural Information Processing Systems, 33: 19920–19930, 2020.

Online Data Selection for Instruction Tuning via Gaussian Processes Estimating training data influence by tracing gradient descent.Advances in Neural Information Processing Systems, 33: 19920–19930, 2020

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18d7abed-f84d-4b14-b9fb-7b814dab03ae · outbound

This paper cites Rethinking data shapley for data selection tasks: Misleads and merits.

Online Data Selection for Instruction Tuning via Gaussian Processes Rethinking data shapley for data selection tasks: Misleads and merits

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b6a4101-34db-4cf8-837c-5ea048e7ecd0 · outbound

This paper cites Doremi: Optimizing data mixtures speeds up language model pretraining.Advances in Neural Information Processing Systems, 36: 69798–69818, 2023.

Online Data Selection for Instruction Tuning via Gaussian Processes Doremi: Optimizing data mixtures speeds up language model pretraining.Advances in Neural Information Processing Systems, 36: 69798–69818, 2023

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35072153-1e3a-45ca-a8c1-cd809c77d4f7 · outbound

This paper cites Less: selecting influential data for targeted instruction tuning.

Online Data Selection for Instruction Tuning via Gaussian Processes Less: selecting influential data for targeted instruction tuning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6185285c-db87-46f9-9c11-13b81e6036aa · outbound

This paper cites Qurating: Selecting high- quality data for training language models.

Online Data Selection for Instruction Tuning via Gaussian Processes Qurating: Selecting high- quality data for training language models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f3fb830-30f6-4dbe-a5f1-77e6affd1bb2 · outbound

This paper cites An empirical study of example forgetting during deep neural network learning.

Online Data Selection for Instruction Tuning via Gaussian Processes An empirical study of example forgetting during deep neural network learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3457ef35-d5bb-48ce-86d2-b7c81c14d945 · outbound

This paper cites Deep learning on a data diet: Finding important examples early in training.Advances in neural information processing systems, 34:20596–20607, 2021.

Online Data Selection for Instruction Tuning via Gaussian Processes Deep learning on a data diet: Finding important examples early in training.Advances in neural information processing systems, 34:20596–20607, 2021

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d340364-7578-40ae-9034-1ecdcf4df77a · outbound

This paper cites Beyond neural scaling laws: beating power law scaling via data pruning.Advances in Neural Information Processing Systems, 35:19523–19536, 2022.

Online Data Selection for Instruction Tuning via Gaussian Processes Beyond neural scaling laws: beating power law scaling via data pruning.Advances in Neural Information Processing Systems, 35:19523–19536, 2022

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75a5c97b-83fa-4d03-849a-6f0b88d5f0f2 · outbound

This paper cites Tracking the best expert.Machine Learning, 32(2): 151–178, 1998.

Online Data Selection for Instruction Tuning via Gaussian Processes Tracking the best expert.Machine Learning, 32(2): 151–178, 1998

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9337d63-bd30-492b-8a5e-03bcb37d2e4a · outbound

This paper cites Cambridge University Press, 2006.

Online Data Selection for Instruction Tuning via Gaussian Processes Cambridge University Press, 2006

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82fda2b2-15ca-4205-adfe-7817b9e12cb5 · outbound

This paper cites Measuring massive multitask language understanding.Proceedings of the International Conference on Learning Representations (ICLR), 2021.

Online Data Selection for Instruction Tuning via Gaussian Processes Measuring massive multitask language understanding.Proceedings of the International Conference on Learning Representations (ICLR), 2021

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 446e3934-7a31-4485-82eb-e1f564eb2fb7 · outbound

This paper cites SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization.

Online Data Selection for Instruction Tuning via Gaussian Processes SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf2964f3-79a5-4653-a6fc-9b117ee0514a · outbound

This paper cites Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki.

Online Data Selection for Instruction Tuning via Gaussian Processes Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c6668a9-d9b5-4cfe-a63e-a0836bb987d8 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Online Data Selection for Instruction Tuning via Gaussian Processes Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-06-30T07:04:21.083490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a06183d2-8be2-4f9a-951e-50d6549ec13e · outbound

This paper cites The Llama 3 Herd of Models.

Online Data Selection for Instruction Tuning via Gaussian Processes The Llama 3 Herd of Models

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-06-30T07:04:21.086103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b7271dc0-330c-4f02-a888-a8082bc0aac9 · outbound

This paper cites Qwen3 Technical Report.

Online Data Selection for Instruction Tuning via Gaussian Processes Qwen3 Technical Report

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-06-30T07:04:21.080706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1d038b87-1b60-49ee-b226-d091b22bf4fa · outbound

This paper cites Mistral 7B.

Online Data Selection for Instruction Tuning via Gaussian Processes Mistral 7B

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-06-30T07:04:21.083735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e159c18e-7968-49ec-926e-886141fdf96a · outbound

This paper cites Mistral 7B.

Online Data Selection for Instruction Tuning via Gaussian Processes Mistral 7B

Reference 32

Resolution: malformed identifier; local_arxiv, observed 2026-06-30T07:04:20.452059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4840421f-712c-4ba7-b142-1129061d92d5 · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert- networks.

Online Data Selection for Instruction Tuning via Gaussian Processes Sentence-bert: Sentence embeddings using siamese bert- networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73ba7e50-4965-41bd-bc34-5f9def389d85 · outbound

This paper cites MIT press Cambridge, MA, 2006.

Online Data Selection for Instruction Tuning via Gaussian Processes MIT press Cambridge, MA, 2006

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76870e90-13cc-4a1a-bd3f-cc4df6cb9f4a · outbound

This paper cites Tighter bounds on the log marginal likelihood of gaussian process regression using conjugate gradients.

Online Data Selection for Instruction Tuning via Gaussian Processes Tighter bounds on the log marginal likelihood of gaussian process regression using conjugate gradients

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c29d1279-5a79-48a3-98d5-8b0b4ff7eaa4 · outbound

This paper cites Hashimoto.

Online Data Selection for Instruction Tuning via Gaussian Processes Hashimoto

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96333040-97d0-4e07-904f-5c4b83dabddc · outbound

This paper cites The flan collection: Designing data and methods for effective instruction tuning.

Online Data Selection for Instruction Tuning via Gaussian Processes The flan collection: Designing data and methods for effective instruction tuning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 094e2160-f50d-46cf-96e8-a961d8434a75 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Online Data Selection for Instruction Tuning via Gaussian Processes Chain-of-thought prompting elicits reasoning in large language models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76cc5da7-7924-492c-8f9f-959493280346 · outbound

This paper cites Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023.

Online Data Selection for Instruction Tuning via Gaussian Processes Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a14c9e9-9874-4baa-adaa-09192aac4bca · outbound

This paper cites Openassistant conversations-democratizing large language model alignment.Advances in neural information processing systems, 36:47669–47681, 2023.

Online Data Selection for Instruction Tuning via Gaussian Processes Openassistant conversations-democratizing large language model alignment.Advances in neural information processing systems, 36:47669–47681, 2023

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee466375-d50b-49b6-bcf6-813e78639072 · outbound

This paper cites Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration.Advances in neural information processing systems, 31, 2018.

Online Data Selection for Instruction Tuning via Gaussian Processes Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration.Advances in neural information processing systems, 31, 2018

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a10ba5e5-1f04-4aee-b33e-265315203182 · outbound

This paper cites Not all samples are created equal: Deep learning with importance sampling.

Online Data Selection for Instruction Tuning via Gaussian Processes Not all samples are created equal: Deep learning with importance sampling

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-30T07:01:16.854965Z

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Unavailable: canonical work link unavailable.

Observation 1d20716b-6e4f-4581-8d9e-6434a78952b1 · outbound

This paper cites Online Batch Selection for Faster Training of Neural Networks.

Online Data Selection for Instruction Tuning via Gaussian Processes Online Batch Selection for Faster Training of Neural Networks

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-06-30T07:04:21.089014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a08977e8-3998-41c7-b7a6-c9e01debcdd7 · outbound

This paper cites Gomez, Adrien Morisot, Sebastian Farquhar, and Yarin Gal.

Online Data Selection for Instruction Tuning via Gaussian Processes Gomez, Adrien Morisot, Sebastian Farquhar, and Yarin Gal

Reference 44

Resolution: malformed identifier; arxiv_id, observed 2026-06-30T07:04:21.091847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

No inbound Pith citation observations are available.