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Paper Citation Record · LEDGER

Efficient Data Selection at Scale via Influence Distillation

As of 8 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 1 inbound Pith citation observation for arXiv:2505.19051.

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

pith.paper-citation-record.v1
2505.19051 v1

Coverage vector

measured 73 of 73 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:25:22.360806Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T17:01:21.521025Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T17:04:56.636609Z

Reference resolution

73 of 73 outbound references displayed

  • verified exact3
  • verified fuzzy24
  • unresolved46
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8fbc55e1-4d0d-4961-b324-4f89de4aa9fe · outbound

This paper cites LESS: Selecting Influential Data for Targeted Instruction Tuning.

Efficient Data Selection at Scale via Influence Distillation LESS: Selecting Influential Data for Targeted Instruction Tuning

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:17.393085Z digest=sha256:d6929096c8b1db58cfd9b62fe4a72c419232ce6a59e86080cd63e504ecd33cad

Observation 3d6ef85e-3975-4139-be2b-80015db191cc · outbound

This paper cites Compute-Constrained Data Selection.

Efficient Data Selection at Scale via Influence Distillation Compute-Constrained Data Selection

Reference 2

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source=arxiv_source observed=2026-08-07T14:25:17.780704Z digest=sha256:2e0a400ee1d0d4440ba90e6520a88fd9a929c53c5fd4e2be045a9c94c7726a7a

Observation f876d2a2-41fd-4918-908b-e958932f7cad · outbound

This paper cites Selecting Informative Contexts Improves Language Model Finetuning.

Efficient Data Selection at Scale via Influence Distillation Selecting Informative Contexts Improves Language Model Finetuning

Reference 3

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verified exact
local_arxiv, observed 2026-08-07T14:25:23.404674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:17.896261Z digest=sha256:7a8afdb365c3d2cbcb5d9a84733634a722eb98a15e96d8dedbcc98914ba55cca

Observation d619e2ab-6886-44f8-9a45-7f500f198bcc · outbound

This paper cites When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale.

Efficient Data Selection at Scale via Influence Distillation When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 4

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T14:25:18.051571Z digest=sha256:06688a3c8f15b6b7c1c2d43b0b58e57d3b67f8d4c1da5960b8a804981f0187ee

Observation 19b78a8c-059c-4d4e-a570-b9b7eac4fa33 · outbound

This paper cites Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models.

Efficient Data Selection at Scale via Influence Distillation Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models

Reference 5

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T14:25:18.158554Z digest=sha256:cc8726e5a37090411460cdbd0d7a7fa33c669b14472c9c7c13843c383d783501

Observation 21e7f305-db1c-408e-8639-e83c44e0de5f · outbound

This paper cites From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning.

Efficient Data Selection at Scale via Influence Distillation From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:18.225316Z digest=sha256:561f0c7260a0feb396f39f6ea2397ff45d6968ed7aeebd002c731397223735f9

Observation 1842ce34-e2e5-4790-8ad3-bdc80f01ace0 · outbound

This paper cites Large-Scale Data Selection for Instruction Tuning.

Efficient Data Selection at Scale via Influence Distillation Large-Scale Data Selection for Instruction Tuning

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:18.318477Z digest=sha256:59ea5d2f9bfb3c09e7162460565dd3b2aef15e2c62dbb1208c0d7ad980f2a1f1

Observation 79ee8196-a446-4fcb-9266-7f377ae89de4 · outbound

This paper cites Woodruff, and Michael Wunder.

Efficient Data Selection at Scale via Influence Distillation Woodruff, and Michael Wunder

Reference 8

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raw_fallback, observed 2026-08-07T14:25:24.098253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:18.411049Z digest=sha256:916088dbd1f0654b75887012640bf9f20c144b4243785f344de237afff9ea920

Observation 60b8b8ae-e7a5-4c03-bf9d-c1d4aa4238b1 · outbound

This paper cites Data selection for language models via importance resampling.

Efficient Data Selection at Scale via Influence Distillation Data selection for language models via importance resampling

Reference 9

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raw_fallback, observed 2026-08-07T14:25:24.078640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:18.509368Z digest=sha256:64b0f7d98cd99b1e8cb6fa8d3a3a994d3c01bcc50f6bf7ad35d7cac72075ba1b

Observation bb7a900b-430a-47e6-9e52-c239a7a5ec99 · outbound

This paper cites DsDm: Model-Aware Dataset Selection with Datamodels.

Efficient Data Selection at Scale via Influence Distillation DsDm: Model-Aware Dataset Selection with Datamodels

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:18.604677Z digest=sha256:9dc5f61b86f8e0a34e2e7920edf1e4f8263f86836fd45a1c0968f04c2b147ffd

Observation b5eec917-9062-45a4-bfdc-781b790f61e1 · outbound

This paper cites Dynimpt: A dynamic data selection method for improving model training efficiency.

Efficient Data Selection at Scale via Influence Distillation Dynimpt: A dynamic data selection method for improving model training efficiency

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:18.700224Z digest=sha256:c57125f0884b38e6fac84a8b67e1bcbfd7b399d43cf2821ab32f4b812d452376

Observation 11e1d17c-c31b-4f87-8cb3-e328be64f6f9 · outbound

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

Efficient Data Selection at Scale via Influence Distillation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:18.771868Z digest=sha256:d51eb5a7742c28d8395fa20b626228f85b2234a541ae3e4cb539afb795471f50

Observation 0463f96f-69bc-4ecb-a1b0-d5d34f434f28 · outbound

This paper cites Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2.

Efficient Data Selection at Scale via Influence Distillation Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:18.851073Z digest=sha256:caf7d2eb5aa82d5d4464156a9a561206a8adbdd43ce4f5d38e4ee6815f463b37

Observation f4790058-ee55-4ed6-955b-52cba7aa5027 · outbound

This paper cites The Llama 3 Herd of Models.

Efficient Data Selection at Scale via Influence Distillation The Llama 3 Herd of Models

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:18.919852Z digest=sha256:9b18e9246a98f1fbe0eb4337c10096dde753ad5735127be111f13550d611eb1f

Observation eb35e51a-e430-4238-b334-dcf3b50017d4 · outbound

This paper cites Qwen2.5: A party of foundation models, September 2024.

Efficient Data Selection at Scale via Influence Distillation Qwen2.5: A party of foundation models, September 2024

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:19.036128Z digest=sha256:437413a3923f8889c04dca28072bd744830fba9dd5baa70decd853eb0a8d11f3

Observation befea17d-e1d9-4c00-a113-d58a9d298519 · outbound

This paper cites Measuring massive multitask language understanding.

Efficient Data Selection at Scale via Influence Distillation Measuring massive multitask language understanding

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:19.100480Z digest=sha256:d23331f772fc884b443ad75d3aac8f2f09d819e61bb282f1fe2fc76ddc6e8818

Observation fcdb9fb3-9c3f-40f1-b9db-e891da515fbe · outbound

This paper cites Aligning ai with shared human values.

Efficient Data Selection at Scale via Influence Distillation Aligning ai with shared human values

Reference 17

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raw_fallback, observed 2026-08-07T14:25:24.015581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:19.172914Z digest=sha256:61a22d9caceeb9b47a0ebe05d6ea056e957ad71d9cd999b06704a6af99fbe3dc

Observation bb3c6ee3-5dc6-4280-99c3-324731caf66f · outbound

This paper cites Beyond neural scaling laws: beating power law scaling via data pruning.

Efficient Data Selection at Scale via Influence Distillation Beyond neural scaling laws: beating power law scaling via data pruning

Reference 18

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T14:25:19.244702Z digest=sha256:31d355cfac3415d4e34610d683cf82b57d5b7241b28ce5cb8a151756e17b3cba

Observation d5a8c5d7-e961-4745-875f-389ced08038f · outbound

This paper cites SemDeDup: Data-efficient learning at web-scale through semantic deduplication.

Efficient Data Selection at Scale via Influence Distillation SemDeDup: Data-efficient learning at web-scale through semantic deduplication

Reference 19

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source=arxiv_source observed=2026-08-07T14:25:19.312719Z digest=sha256:02f1b854cf176f8ad13754443323638374199e1ca82ae808b0ff9b501085ebb9

Observation 88d799a2-6e30-40e3-81b2-49fab40ddd1c · outbound

This paper cites Cross-lingual transfer learning with data selection for large-scale spoken language understanding.

Efficient Data Selection at Scale via Influence Distillation Cross-lingual transfer learning with data selection for large-scale spoken language understanding

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.983540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:19.379256Z digest=sha256:37ad1ea460c09f6a64c5854e25a54ef477cb7bc9bfc551eb117b9b830f6a4aab

Observation a41bb63b-5888-4361-84d8-82c8b50b1045 · outbound

This paper cites Smalltolarge (s2l): Scalable data selection for fine-tuning large language models by summarizing training loss trajectories of small models.

Efficient Data Selection at Scale via Influence Distillation Smalltolarge (s2l): Scalable data selection for fine-tuning large language models by summarizing training loss trajectories of small models

Reference 21

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verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.965627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:19.453517Z digest=sha256:a95e6b3d8dceec9ce76a1fd7a3b06c76fc5e61370e55398fab369da48a321fc4

Observation f361fb2b-cf34-40e7-b9cd-9725d3e3ad4a · outbound

This paper cites Language Models are Few-Shot Learners.

Efficient Data Selection at Scale via Influence Distillation Language Models are Few-Shot Learners

Reference 22

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T14:25:19.525244Z digest=sha256:3be87a2b8e079a9b1b121af23aa700f7e3ca20511e55756f6deadacac23cd6ba

Observation 00ac99af-f005-4de7-b4c8-10b652dab6c2 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Efficient Data Selection at Scale via Influence Distillation The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 23

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source=arxiv_source observed=2026-08-07T14:25:19.604099Z digest=sha256:cf2a436a4d28c7045b68009ddecda80503eed59e5b1f27720fdbc0ffd7342494

Observation a73e737e-1a7e-436f-821b-970c7579f6ed · outbound

This paper cites Palm: Scaling language modeling with pathways.

Efficient Data Selection at Scale via Influence Distillation Palm: Scaling language modeling with pathways

Reference 24

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source=arxiv_source observed=2026-08-07T14:25:19.673561Z digest=sha256:0d72e86568bfc22bf5afa2f82bf3c19a29d2217558a1182128b26e23c79a222a

Observation 82471f86-8c9e-4aea-8eba-95d0d6fb6a6a · outbound

This paper cites Glam: Efficient scaling of language models with mixture-of-experts.

Efficient Data Selection at Scale via Influence Distillation Glam: Efficient scaling of language models with mixture-of-experts

Reference 25

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raw_fallback, observed 2026-08-07T14:25:23.929603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:19.745460Z digest=sha256:9578b77e252980ae54d7223684ada92024dc6eab6921de6d47a43f08cf12f2f1

Observation 2bfb858c-ab44-4093-b67f-c1f624d15e86 · outbound

This paper cites Intelligent selection of language model training data.

Efficient Data Selection at Scale via Influence Distillation Intelligent selection of language model training data

Reference 26

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verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.911761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:19.818399Z digest=sha256:e47063b463b43f005d0afd585f4c8851aa7428276705a5bcb70bc5ef977df876

Observation c452aa61-e1e1-4d88-b0e2-234b48fdb01e · outbound

This paper cites Cynical Selection of Language Model Training Data.

Efficient Data Selection at Scale via Influence Distillation Cynical Selection of Language Model Training Data

Reference 27

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verified exact
local_arxiv, observed 2026-08-07T14:25:23.141447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:19.882220Z digest=sha256:361d348a331f69b7c4aa6bdf8dfff5853a4aab220fcfbd0b76b04eae25d0c49e

Observation 5a7ade20-0521-4473-bff6-3c1bb2bab8a8 · outbound

This paper cites Automatic Document Selection for Efficient Encoder Pretraining.

Efficient Data Selection at Scale via Influence Distillation Automatic Document Selection for Efficient Encoder Pretraining

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:20.012070Z digest=sha256:d775722afa49b813125713e7e5043cbeca9af61bbcfa1503fadc3c27c5eecbda

Observation 7690d485-e810-4b73-9afc-bf3a14ecad51 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Efficient Data Selection at Scale via Influence Distillation Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 29

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no resolver link, observed 2026-08-07T14:25:20.084156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:20.084156Z digest=sha256:6a9ab7cf398325bf8223f31d5d93a09e735b2321d7a052be6fb89cc53a5336c5

Observation a9e3bc3b-a137-4acd-8a29-5540a37335c8 · outbound

This paper cites Skill-it! a data-driven skills framework for understanding and training language models.

Efficient Data Selection at Scale via Influence Distillation Skill-it! a data-driven skills framework for understanding and training language models

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.883126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:20.166474Z digest=sha256:cc5ec22930a159d385c2ab1bd0d68043c0a068d049ecd765a66937f878fbb136

Observation 0f8af9a2-3458-4764-aeb1-601c3b23e6f8 · outbound

This paper cites Efficient online data mixing for language model pre-training.

Efficient Data Selection at Scale via Influence Distillation Efficient online data mixing for language model pre-training

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.858137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:20.269168Z digest=sha256:7e799de9f46ae646ad98d282365fd11fbf35ad39c7ee0cb51f4941839fcb5751

Observation 9d30b2e6-7b81-498c-8c6b-2ded60bc6076 · outbound

This paper cites DavIR: Data Selection via Implicit Reward for Large Language Models.

Efficient Data Selection at Scale via Influence Distillation DavIR: Data Selection via Implicit Reward for Large Language Models

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:20.358301Z digest=sha256:00883ef13077b876a739d01987d3ce26fd1419ecf7894f6fbcff9487213f6ae7

Observation 29860640-ff21-4f63-ab10-007a95bad28e · outbound

This paper cites Dataset cartography: Mapping and diagnosing datasets with training dynamics.

Efficient Data Selection at Scale via Influence Distillation Dataset cartography: Mapping and diagnosing datasets with training dynamics

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.834143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:20.437538Z digest=sha256:6bd918e59b0d5493bab01ad6f86eb27272445ea69cbf8214ec13e0efe3edeb8e

Observation 17ea860b-5e66-4673-a0b5-82171a40dc6a · outbound

This paper cites An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models.

Efficient Data Selection at Scale via Influence Distillation An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:20.521520Z digest=sha256:d51e02b74a85f98463c181efb60dcb29ad8bc45ea5e854fa8b4ca928168d2777

Observation e924ecee-3e7c-4541-92d8-d6c7929fd640 · outbound

This paper cites D4: improving LLM pretraining via document de-duplication and diversification.

Efficient Data Selection at Scale via Influence Distillation D4: improving LLM pretraining via document de-duplication and diversification

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.808786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:20.741486Z digest=sha256:46595ea3df48699ccd08354b7069fb1c397767ad424b8304463ca2a1b8ace9c2

Observation 474777d3-2037-4e4a-b396-c0df17e86390 · outbound

This paper cites Dsdm: Model-aware dataset selection with datamodels.

Efficient Data Selection at Scale via Influence Distillation Dsdm: Model-aware dataset selection with datamodels

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.788444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:20.901391Z digest=sha256:ece131f4fa51c4c9bc86acb5608fe160e7c416532a3177b6883363976297df17

Observation d87a0e1b-bd04-4182-a1aa-25df599736f9 · outbound

This paper cites Learning from less data: A unified data subset selection and active learning framework for computer vision.

Efficient Data Selection at Scale via Influence Distillation Learning from less data: A unified data subset selection and active learning framework for computer vision

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.766973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:21.067871Z digest=sha256:5ab8c285fe20df3bdf862b34a9e73a26c10df60f5dd9de9c727e6d5fba50a474

Observation 8c036948-b5b7-4b01-80f2-b7226ffdb001 · outbound

This paper cites Retrieve: Coreset selection for efficient and robust semi-supervised learning.

Efficient Data Selection at Scale via Influence Distillation Retrieve: Coreset selection for efficient and robust semi-supervised learning

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.735817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:21.234618Z digest=sha256:4421963e08cfba0cb25571cf23dbbf2660c5f6b4bb5d353e0de8b5e0d4a9bd1f

Observation 5533b5e1-4487-4a27-967c-91930106f009 · outbound

This paper cites Submodularity in data subset selection and active learning.

Efficient Data Selection at Scale via Influence Distillation Submodularity in data subset selection and active learning

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.709866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:21.361747Z digest=sha256:06b4693877f486c6d6ad99ad6d5b375eba275e19ac48bc3501eb564301654cd8

Observation 99c9d33d-1de1-48fa-897f-013f850ee489 · outbound

This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

Efficient Data Selection at Scale via Influence Distillation AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 40

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unresolved
no resolver link, observed 2026-08-07T14:25:21.398721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:21.398721Z digest=sha256:6767d3dbb0dea41382008ea6233e47ccd38445633caddbe754fda4d52039e1b7

Observation 4553c525-29a0-4827-8eaf-0364a95e7b86 · outbound

This paper cites Instruction Mining: Instruction Data Selection for Tuning Large Language Models.

Efficient Data Selection at Scale via Influence Distillation Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 41

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no resolver link, observed 2026-08-07T14:25:21.464252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:21.464252Z digest=sha256:8cfa6a95b42dc597861d17899ed017cee9c947e2cc0269f1adbd09d58dfd99b5

Observation 9427bd11-8fc2-4a97-a465-12efbb58d1fd · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Efficient Data Selection at Scale via Influence Distillation Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 42

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unresolved
no resolver link, observed 2026-08-07T14:25:21.747454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:21.747454Z digest=sha256:92aa1a05fd7eaa677914be9a46f9e00e2c906d8939287f317b5c81257eadb881

Observation 065bb8c1-8daf-4f54-84ed-cbfac5366862 · outbound

This paper cites Learning multiple layers of features from tiny images.

Efficient Data Selection at Scale via Influence Distillation Learning multiple layers of features from tiny images

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:21.789613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:21.789613Z digest=sha256:8175698236072ebf56efcd93009326cc315bde6da3a96fd8b6adeb3931e39056

Observation 9df2c883-9405-4900-8f21-f08980d7e081 · outbound

This paper cites A software package for sequential quadratic programming.

Efficient Data Selection at Scale via Influence Distillation A software package for sequential quadratic programming

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.666379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:21.870108Z digest=sha256:c5e8d1a65e2beb2954bcd1548cc91ee4321ca0ee05c307926f021a16011e75e6

Observation c5fb7ade-f1d3-47db-8a18-55d965c6d369 · outbound

This paper cites Fundamental algorithms for scientific computing in python and scipy 1.0 contributors.

Efficient Data Selection at Scale via Influence Distillation Fundamental algorithms for scientific computing in python and scipy 1.0 contributors

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.646276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:21.961637Z digest=sha256:631a27bf3b723e170eb9ea824a7d1a82fd7b9863f7b18d5f878be0046501931d

Observation ac74536e-43cd-47ae-a685-e1c856b6fd0b · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Efficient Data Selection at Scale via Influence Distillation Adam: A Method for Stochastic Optimization

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.030590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.030590Z digest=sha256:1c841a0e1af775313840ebe63cccb9c190b57ea15b70d5198ff2b7db872fe5d4

Observation 0cb541b8-9d92-4626-a4e5-89ebb8074ab1 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Efficient Data Selection at Scale via Influence Distillation Training Verifiers to Solve Math Word Problems

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.093676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.093676Z digest=sha256:d084e3afc9d229cbd5e6645e0e54452fe9299fbbcc6295eca1ba5a56463075ac

Observation 6fe5a871-a5ab-4ff0-94f1-476304d1d5d2 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Efficient Data Selection at Scale via Influence Distillation Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.179177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.179177Z digest=sha256:2fbb32db602a0cdbcff01fe64e2c3a462a66e8e97a7b7ee60ede26c5d4b1edc4

Observation 32472503-e5aa-4826-985e-446ce7b0347a · outbound

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

Efficient Data Selection at Scale via Influence Distillation Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.626327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:22.229023Z digest=sha256:1992266b1ee916364687f041de9d33629cba1fcb92432ff1b46f82a6ac99a67c

Observation afb0ea76-2f23-4533-8c7d-5f4df29d6fcc · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Efficient Data Selection at Scale via Influence Distillation Evaluating Large Language Models Trained on Code

Reference 50

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unresolved
no resolver link, observed 2026-08-07T14:25:22.234458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.234458Z digest=sha256:5ca4d6a8aab069861f8b5eb485043e311340cfb2787875a58cfb1948ffbae935

Observation a31d4afe-dcec-4ff7-a1db-d94e91c7d41f · outbound

This paper cites SQ u AD : 100,000+ questions for machine comprehension of text.

Efficient Data Selection at Scale via Influence Distillation SQ u AD : 100,000+ questions for machine comprehension of text

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.239197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.239197Z digest=sha256:ae8aa8392bb83bf0be1ffc5e731a75552ee576bc113b150f4830f34007ba90e7

Observation 9245a6b5-e785-4207-b157-b69a084dad85 · outbound

This paper cites Alpacaeval: An automatic evaluator of instruction-following models, 2023 b.

Efficient Data Selection at Scale via Influence Distillation Alpacaeval: An automatic evaluator of instruction-following models, 2023 b

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.245094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.245094Z digest=sha256:aba6e11ec68cc2265d843009837bee9349491092047189af3f662b25784046a2

Observation ebb0f529-46d0-4e5c-93d0-4a61e2cd81dc · outbound

This paper cites Scaling Laws for Neural Language Models.

Efficient Data Selection at Scale via Influence Distillation Scaling Laws for Neural Language Models

Reference 53

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unresolved
no resolver link, observed 2026-08-07T14:25:22.249844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.249844Z digest=sha256:61607daaf209f074a621a3bb5a6e2fdc4e33326e32050553feccb13db4606f0d

Observation 02a068f5-cbb0-4130-847b-dd55170a42e4 · outbound

This paper cites Second-Order Forward-Mode Automatic Differentiation for Optimization.

Efficient Data Selection at Scale via Influence Distillation Second-Order Forward-Mode Automatic Differentiation for Optimization

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.255032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.255032Z digest=sha256:a3201ba2fffefe0718c8942a4ffbecd06484e932af8f80ae20e50e04e8d3d2db

Observation 6677dad7-ed6b-4286-9978-3af6fbc2b58e · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Efficient Data Selection at Scale via Influence Distillation Lora: Low-rank adaptation of large language models

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.260229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.260229Z digest=sha256:423cf51d23980bb9cbd805d8e883519b6de2eb2b8295347800f23cc6fcd7eeae

Observation 8e7dcbd0-9d4b-4b92-a234-6347c225cabd · outbound

This paper cites TRAK: Attributing Model Behavior at Scale.

Efficient Data Selection at Scale via Influence Distillation TRAK: Attributing Model Behavior at Scale

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.265773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.265773Z digest=sha256:b804edb92fe5f9bfacd21a65f7d69a831526c89e5e8ce21b82b0bcf8bacc8d38

Observation 36afd5ad-b3d3-4fa0-8887-ea30f67f55c6 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Efficient Data Selection at Scale via Influence Distillation Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.270445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.270445Z digest=sha256:089e4f6379806470b594712682a0beade588108c8cc6e4ee686e77a2cb3281c3

Observation 6e3305e8-fbf8-45f9-bbb6-941bef4d9fb2 · outbound

This paper cites CrAM: A Compression-Aware Minimizer.

Efficient Data Selection at Scale via Influence Distillation CrAM: A Compression-Aware Minimizer

Reference 58

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:25:22.759076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:22.275388Z digest=sha256:4a6f0db52003f14e785631f9f20b99dd0e10e1379530006bbb98ffb6fc262f21

Observation 47a69f1c-e9f3-4c64-bc1b-069c5604184e · outbound

This paper cites Scaling instruction-finetuned language models.

Efficient Data Selection at Scale via Influence Distillation Scaling instruction-finetuned language models

Reference 59

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unresolved
no resolver link, observed 2026-08-07T14:25:22.280856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.280856Z digest=sha256:1ee26f6c46b4069f4441ed5c97c8e7526e94fbd520652008d91962c483c96307

Observation 47599f09-4443-44ff-b2e8-12eb9cd12737 · outbound

This paper cites o pf, Yannic Kilcher, Dimitri Von R \.

Efficient Data Selection at Scale via Influence Distillation o pf, Yannic Kilcher, Dimitri Von R \

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.570719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:22.286354Z digest=sha256:922a4b0641cd5fc69c9a9f44c84401fbd2ec4dd80d78781b35d184433ff5991e

Observation e7186ae3-75ed-441f-8451-c12f5d1b4c9e · outbound

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

Efficient Data Selection at Scale via Influence Distillation Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023

Reference 61

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no resolver link, observed 2026-08-07T14:25:22.291632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.291632Z digest=sha256:e1ef75066123a6fea3a47c83512e8f1211f1c6b030bcb0a6b28d1462ac8ee83e

Observation f75019a2-5318-439a-88c0-baa0a93723a8 · outbound

This paper cites Instruction Tuning with GPT-4.

Efficient Data Selection at Scale via Influence Distillation Instruction Tuning with GPT-4

Reference 62

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unresolved
no resolver link, observed 2026-08-07T14:25:22.297208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.297208Z digest=sha256:5aa23ee46f3d33c20e06552e17c5c27acb07330e43cb3c2aad369f599286e992

Observation c8474305-6044-4d0e-9fb3-7910ac924ebb · outbound

This paper cites Code alpaca: An instruction-following llama model for code generation, 2023.

Efficient Data Selection at Scale via Influence Distillation Code alpaca: An instruction-following llama model for code generation, 2023

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.536533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:22.302484Z digest=sha256:cd2b7b7fdd19959f5cbfbc9bea691b26efa0ecf635731ea67970ba984185e20f

Observation d7bd0df5-54eb-484d-98e4-91e61d49214c · outbound

This paper cites Lima: Less is more for alignment.

Efficient Data Selection at Scale via Influence Distillation Lima: Less is more for alignment

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.518864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:22.309116Z digest=sha256:fe0e821ce35961288ffe3e4ecfeb24be1e1c586e521958438f9a4befcedd10cf

Observation 7068f832-bd6c-4fc5-86ef-328f9326f2f6 · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions.

Efficient Data Selection at Scale via Influence Distillation WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.315333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.315333Z digest=sha256:21ad0ababa5376b358678366adcdf249aa36102483d73c10afc8e2e74eb2ba05

Observation 04a7edd7-6903-46dc-a012-cb0049da96f7 · outbound

This paper cites Openorca: An open dataset of gpt augmented flan reasoning traces, 2023.

Efficient Data Selection at Scale via Influence Distillation Openorca: An open dataset of gpt augmented flan reasoning traces, 2023

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.498394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:22.321452Z digest=sha256:7174c1d40aeaf5a119e6825e7cc287708936f03672e51f80b59e1461f539d4ad

Observation 01ff910e-d46c-4c31-9199-9d7d391b3146 · outbound

This paper cites Sciriff: A resource to enhance language model instruction-following over scientific literature.

Efficient Data Selection at Scale via Influence Distillation Sciriff: A resource to enhance language model instruction-following over scientific literature

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.327276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.327276Z digest=sha256:d7e85052f2d33a586fcb943dae42c587295d178687e6926738643489d9c90029

Observation de3062b4-e1da-4ee2-b833-0b457111a35e · outbound

This paper cites PaLM 2 Technical Report.

Efficient Data Selection at Scale via Influence Distillation PaLM 2 Technical Report

Reference 68

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unresolved
no resolver link, observed 2026-08-07T14:25:22.332639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.332639Z digest=sha256:65cdd727939bcb93e315748a65a114459f82ed1590183cb1efb14a308ffefd1d

Observation e984cdef-35ff-4ca2-abb9-a64fe1702d18 · outbound

This paper cites NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.

Efficient Data Selection at Scale via Influence Distillation NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 69

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unresolved
no resolver link, observed 2026-08-07T14:25:22.337440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.337440Z digest=sha256:8f7abc437156bff3fc1a8e3f965bb76037781135e0d1b2cf6a71f1f49b0a1886

Observation 261a37da-89c6-4cf4-83e0-86e9bdbd9157 · outbound

This paper cites Large Dual Encoders Are Generalizable Retrievers.

Efficient Data Selection at Scale via Influence Distillation Large Dual Encoders Are Generalizable Retrievers

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.342650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.342650Z digest=sha256:1c135899f4d9bbaf1809cab4c436dea81f573b3268e6fd1ee5139f0aefda6f6a

Observation 4d899f91-98c7-411e-a87d-069ad122b8f8 · outbound

This paper cites GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.

Efficient Data Selection at Scale via Influence Distillation GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Reference 71

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unresolved
no resolver link, observed 2026-08-07T14:25:22.348275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.348275Z digest=sha256:b9510464be0130c138affb6575ee23139eaca81990855df29d6d4a09631ace17

Observation f5039b9f-99d5-4e32-955f-13a37e0dc572 · outbound

This paper cites HadaCore: Tensor Core Accelerated Hadamard Transform Kernel.

Efficient Data Selection at Scale via Influence Distillation HadaCore: Tensor Core Accelerated Hadamard Transform Kernel

Reference 72

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unresolved
no resolver link, observed 2026-08-07T14:25:22.354721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:22.354721Z digest=sha256:85bfae05b4ef4f2d1b6c4b45023d8f04a4976a25669a9acbd3564a1c30a95289

Observation 206e9a1f-4905-400d-a309-729e1907d5fb · outbound

This paper cites Fast hadamard transform in cuda, with a pytorch interface, 2023.

Efficient Data Selection at Scale via Influence Distillation Fast hadamard transform in cuda, with a pytorch interface, 2023

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:25:23.471206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:25:22.360806Z digest=sha256:eafffb121f9471909bec2327530eff2d560332da7593fcd5c2a634461b16e0f7

Pith citing papers

Observation ec243924-b070-4a3b-9b1b-9e31b51376e3 · inbound

PRISM: Preference-Aware Influence Function Based Data Selection Method for Efficient Fine-Tuning cites this paper.

PRISM: Preference-Aware Influence Function Based Data Selection Method for Efficient Fine-Tuning Efficient Data Selection at Scale via Influence Distillation

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-06-30T17:04:56.638213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-30T17:01:21.521025Z digest=sha256:4b9efbe25fc497e29ed7831ba23fdba18b53356f04397fde8356334ac21abd94