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Paper Citation Record · LEDGER

Efficient Data Selection at Scale via Influence Distillation

As of 7 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=sha256:01e96d7e29ed019249dfe6ddcc2a47133076fa2c19cf71934ddcd40b5c89c1f1

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:c3328200f07dcf7f354baa0c69b75cb716276ac93b0e3a7728c91d2d220758eb

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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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=sha256:4fe1e2a8dc6312bf39df35eb56bde0801a19cc642768744637eb6838d6546412

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:e728004133dd1652335a4dd8038fa545532ef682f4b1adcb76c5e5970c61d34e

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:f9bf235d142d90574044804c72aa2b6282c4d69447aa83e0cc42d6a80fee1e25

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

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source=arxiv_source observed=2026-08-07T14:25:18.225316Z digest=sha256:786e53c40650db8def0627ea29245640579a6197b4f5a1b53c8d79d413174ad4

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:e0f60703cf7fa8d9e3c5be48a90ab417da53b00d373fd04d1ef55287822acbe5

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=sha256:0b4d2d41e023ba074a262bc7f4d155ed1480a366d501aa052ddced0a6d3870fd

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=sha256:331d0932681f24d5556dcf4520638a5a7f7969981bf2df665c875ed5480a1cbb

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

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source=arxiv_source observed=2026-08-07T14:25:18.604677Z digest=sha256:10b73d1967d70c90a96b2c3a91888586dab81098bf459ce61ca92d78acc061a0

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=sha256:4e544e307888b624e430f52ce97ddb7d82b18694fd0af9a113f6cdce714aa8b6

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:ba319e0e3dd4ce26b51cc2ef40dcfcd9e8c2ef73b6a293ec979b92e4574269c1

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:18.851073Z digest=sha256:2ff499569a1573425ced266422f67d97369e600f078a3ef5aea52f2bafcabb05

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

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source=arxiv_source observed=2026-08-07T14:25:18.919852Z digest=sha256:93fb80cd1e16245720acd1ee13ddb71eb72f7a7e937b8e6e42f65f02d5841464

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=sha256:6590f27bbd6965c14afb04d45307e7bc6c3ebedc1d2a7e8d348766a97791aa8d

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:a9b704e93de16d207e211d3848c62b858360a0fb7f05ea5fe7aec9e053557082

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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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=sha256:017d68d51adfcfcf82571670465b47bc1ed0d18952a6562631b4b0f4a1fa4482

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:19.244702Z digest=sha256:369d3060131204b9663c08ba8a2b0855a26d55720cd43d0bbb8d5932bca8b0f3

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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no resolver link, observed 2026-08-07T14:25:19.312719Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:19.312719Z digest=sha256:e55b589286d3dcd8013c4af5a6c5c32a5c7ee57081583b70c136d5c5c4bc7342

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:eb42066b29b9e29dae3ff3adfa64d9ba0cbcd560adf36f5a38cf308b9cac1e59

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=sha256:6411340c83136fe6d9b41def4ff11fd922551f612c7eb72f9e4b0c98f0448213

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:19.525244Z digest=sha256:31ab0947a97aa807503e20d9e40019902aad0a1d786bf7f6c771299e2717dc71

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:d4222fefa80821c1b90bcaab02bfeed0b628bce71e5319f234828f5d95d06f63

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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no resolver link, observed 2026-08-07T14:25:19.673561Z

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source=arxiv_source observed=2026-08-07T14:25:19.673561Z digest=sha256:bcb12a7583d761eff810ec402db53769ee7785b685ea302c4502bf9f80407270

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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verified fuzzy
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=sha256:3a0bc08e63b4c40abd80e3a5cc51ec4bfee45b9302c5ccea0557f34c305bec63

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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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:dd1c9d217e0b6409965cf8877d13a722db7cf4e492aebfa432d7a2005e527beb

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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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=sha256:6e3680048bc74f594d5ca63865f4ed3acfe2337cf14e7c115862b85b7e3cac96

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=sha256:7832d2a59af91e8918cf8eb7c81fb22433e9dff4d7ff22dc507a1ca5e3dbb5e4

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:b56b024d499f804531945dc48bcfe587b20d07b3e5c03af443a8ed7e9a37f591

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=sha256:516c4db0a09cc793570c38e4282bcefd00ee0497f310a683f4e9aa691ab79fc5

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:c2a7d7ae2fbe3b0d618f96101e1ec09570ddf90eb00e82c8e220cded89bb5e42

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=sha256:13281c4fab30502942a8bf4f941badd484c3a53755a61c64a03def0c4aae352f

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:b43d2eb32f742a1e26807471d965403ebe5aabc88060947b857525e8db82ae4e

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

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source=arxiv_source observed=2026-08-07T14:25:20.521520Z digest=sha256:7bcb05e5e11c6a48af56eead3fe8be87bf6f42336bc4733210edffb782454007

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:bc0c6a3d56ed509d3171d1f56c470c3c742d16b8968607811f06d03feeed4361

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

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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:d051e9047d4ba1303652effa29dc253231876cd2ef3cf3438961a12b618ed2e0

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

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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=sha256:6968865c5e1d34771fee8a67dcc12adef4080e6f71eab7156429af61c3c99fa8

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=sha256:54ffa5a5e33fa1955ca6f28cea5b0fdf44ce39409857db4995f899de3fe1a464

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=sha256:42346d9dc9195521b24d019fea4e38b89099650d0fd565edb6e9df031afe8782

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:d037483e775037a9869d9b27df8c77e3c288ef3c5031504d1f942d4af28106ae

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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unresolved
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:a2a811387dad1d655aac9bfb1404a8de667a11b3801945f41466af3ecd9c1493

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

Resolution
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=sha256:8e1bd7639609609eb9b0fa65dbddeae6cab1a91272f085fcb31f9c7d0efa65a7

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:c6096ca5f8449f7005a16d7f303f667b0a8916507cd57dfff43ba4bf05ddb655

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=sha256:0eb75ed4e18da759efe818e0ed6e1c5fe43e936b1a809b4a4839c3a89daa2ce0

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=sha256:9e19dded4c068b1cb8d2b9e28a4d588b5e4ed0339e86af87e605dfd575dd6dd6

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

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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=sha256:2460ed24cd14fd165a85d93da947b76f46c915f9618a9d91b31b887d22acf0f5

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:a111af7c11ed0283448a428b5afb7651ff67b24a3edb1bfa0bd2a9e5bd629ed2

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=sha256:7d2920894bddd5cdbaa81078bcdb425d2921de318c14cbba15f05333e24b20c1

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=sha256:7c95fde8587e8088d111d2b18ab9c499b0c2597362106c433c335e3e9c0cb6de

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=sha256:9b55bcaa54334e2424f1ddbc475bae681d8f8839c2bbb5ba7f38e41e66a46d65

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=sha256:40150faa070c21cb5556ead941c9821b837501cc4fa4be7dcb6161120f9aa1db

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

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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=sha256:831bd905d6369299ef9376e89c76a1e3c70b9a4dcd0828932e722f6edf4b6f87

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=sha256:3ee0717668a61e2cc83004b54f3145ce4e496e196db581e3a5cc7285fe0aaea0

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

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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:f7167c87a9ef64f071e232f10cffe297e6d3453afffeb3463ef9900923ac4929

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

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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=sha256:2bc55650c217c05aedbe3aac604155939d6d40e2ca26abee04fd1035f97e9827

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:ddfcf0ab40224806b289bbb4c483902fda8100f1b8556707172e0ed457819487

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=sha256:47f3fb0c1172007aa346ebed44ed7e17618dfcba83a548130b9b5049a7389a11

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=sha256:701f0b9937d1b4629c889cedb8092896400399f7f95f68c777000b2529069d73

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=sha256:7b7504b625fae6bb002b9d682c519fff9c25252c08e53eec516939873f6e1b25

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:be5838e84be71454155ed82e4e27a7bfd1ada3371839b627f0cd90a723ae7e9f

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:eea4aa309b60b78bbaab4bd8ef63a05302b2ddef356b99f8c689db39493a14ac

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:e616c4d42d8a593f6c131e364030409f406259afa2c70183110819afc6639490

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:b448bc722cbd7c151b918420a168d603e708f8a5cef909650913d15f054fa596

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:c838db395df3b9d109d97cd6de44d94a8d26be4291d47a5e803d3f4cc113bfe7

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:ca790cdf78b7413c6ebf3cc4d0e901c9e64b66770df99ab090809ff0bcc19774

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=sha256:9827e0ba9a7104e363891555cbd58e0ed199db495d4c180d84b3581916d56d8b

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:a32e95b8801460b187e79ae3ce4bb00de3a05acad715898edb5553aa988ac3ec

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:de33c652497b6503cb626d29d29551be9884dfd5f72f6638c08e3408115ab946

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:e15c98b3e2f094358e54f0cc1a63786674effe03caebf158353f2b294e31a83f

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=sha256:4481f3a5f059805a76523796d23fc24c6ca04bed5b346f899217e9a7730fd353

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=sha256:2fdbcb505097cf0158791e9d3942803954976773fbbe3ae7a3e763bb68a4526f

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=sha256:6f190939d15cab035c31ff2fe5794befd9a3728bbb9d09399ab4e1e5cbf05d3b

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:ab2cb0645f90dedb97086c4f02d05538f45a6652e0a1c232b8d3e0c250d1b854

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=sha256:113befdb45c09c5639a1a026b89f682fc280878299568bf58cce8d95eac59710