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Paper Citation Record · LEDGER

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning

As of 19 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 1 inbound Pith citation observation for arXiv:2607.17043.

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

pith.paper-citation-record.v1
2607.17043 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T19:16:45.202633Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:08:25.571429Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T00:08:28.562083Z

Reference resolution

56 of 56 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved52
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7ba904ad-e4b8-4849-8c3f-7bd4a74345fa · outbound

This paper cites 2024 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2024 , eprint=

Reference 1

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no resolver link, observed 2026-08-01T19:16:37.800084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:37.800084Z digest=sha256:831e6eb279097dcadf1ba29b1c58507bfbc555313908d80cec988273f567a401

Observation bca8b6fe-7418-4163-b894-749d29777dd4 · outbound

This paper cites Forty-first International Conference on Machine Learning , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Forty-first International Conference on Machine Learning , year=

Reference 2

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no resolver link, observed 2026-08-01T19:16:37.911031Z

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source=arxiv_source observed=2026-08-01T19:16:37.911031Z digest=sha256:ba7b7db7b89671b8f6517b31997fd8fe60e6d3aa26416d06608e76f0661798fc

Observation 8a38c97e-fc3f-4c17-92f2-ed71ff1dbb50 · outbound

This paper cites First Conference on Language Modeling , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning First Conference on Language Modeling , year=

Reference 3

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no resolver link, observed 2026-08-01T19:16:37.972344Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:37.972344Z digest=sha256:12b3ecb8668bb6926ac0915d8eb86f84e593140c557a10938ff4e43ea022779e

Observation fae68403-0f18-45f8-8149-12ea99187812 · outbound

This paper cites Forty-second International Conference on Machine Learning , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Forty-second International Conference on Machine Learning , year=

Reference 4

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no resolver link, observed 2026-08-01T19:16:38.050986Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:38.050986Z digest=sha256:178f5c2994b1ea3ac345ec67dec7173d7509d78bfa8261bc819715acd08b9ae0

Observation cd7f2cbb-a295-46c6-82d6-7cba6dcb6ed7 · outbound

This paper cites The Thirteenth International Conference on Learning Representations , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning The Thirteenth International Conference on Learning Representations , year=

Reference 5

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no resolver link, observed 2026-08-01T19:16:38.149880Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:38.149880Z digest=sha256:91615669beb9c36512df372b59ba33a9777e76dd3152b7ad55d455b6255a2669

Observation 2f5966b1-a0a3-407f-bf34-1a2fe3205fdf · outbound

This paper cites Machine-generated text detection prevents language model collapse.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Machine-generated text detection prevents language model collapse

Reference 6

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no resolver link, observed 2026-08-01T19:16:38.159710Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:38.159710Z digest=sha256:311014788b85f14dbd2f18269defa4c62ae138edea854b26f7b67f14c460551a

Observation c6233ec5-a28c-473f-8d07-e2c6762789df · outbound

This paper cites The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

Reference 7

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no resolver link, observed 2026-08-01T19:16:38.163184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:38.163184Z digest=sha256:52fd9e0357b0aa9694d5a17e5c9175606a24f1ac09fd3e2dd20f09953a662f71

Observation 626a08a3-d8b1-4890-8334-dd84845bbc8d · outbound

This paper cites First Conference on Language Modeling , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning First Conference on Language Modeling , year=

Reference 8

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no resolver link, observed 2026-08-01T19:16:38.204167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:38.204167Z digest=sha256:80bf2b934da9a72ee66f0aff301a38a2373b4780eb376047703f59dc37955b4b

Observation 99094271-036a-4d80-8e07-8773c93c8608 · outbound

This paper cites Generating Datasets with Pretrained Language Models.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Generating Datasets with Pretrained Language Models

Reference 9

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no resolver link, observed 2026-08-01T19:16:38.355848Z

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source=arxiv_source observed=2026-08-01T19:16:38.355848Z digest=sha256:4a9030425704ddedd093de34e7d0e5dcb70b4b49b3482ee281e6289c363185dc

Observation f28b616d-6477-4835-820d-dfec94b9eeb0 · outbound

This paper cites Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering

Reference 10

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verified exact
doi, observed 2026-08-01T19:19:08.461907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-01T19:16:38.492939Z digest=sha256:8b9b3534b48e3eeb22c75a7c564be9e32d64b908c5a9dcc411fee7690f65e5c6

Observation f0bac9a9-b2de-43f7-a1a7-caf472d095ec · outbound

This paper cites WildChat: 1M Chat.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning WildChat: 1M Chat

Reference 11

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:38.666561Z digest=sha256:87c9200f6ef643684cef8f0e1c6806eba25868f1719d8b40ea0fd133cb95669e

Observation fa8bb71e-d67c-4b24-9f7f-1e07f7239b00 · outbound

This paper cites Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned

Reference 12

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source=arxiv_source observed=2026-08-01T19:16:38.836667Z digest=sha256:da5c9901c53be0e6ab9f6ae5cab188720ace1bb5043764c40c93ffc935251660

Observation ca4285d4-ef78-4987-a84f-aee48a6b3c44 · outbound

This paper cites 2025 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2025 , eprint=

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:39.005431Z digest=sha256:02768e25e8a628687d6f6799eff27c853f586315793f67a50e35b67a87462fc5

Observation a217a4a5-b4a8-436a-8400-6bbb0365ecb5 · outbound

This paper cites 2025 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2025 , eprint=

Reference 14

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no resolver link, observed 2026-08-01T19:16:39.206081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:39.206081Z digest=sha256:3e2c1c304087292640f96d3357ea7c4c08fd0159a4bb660337f2e591b18ed54b

Observation ca64a94e-e342-4ac6-bf38-32cfeb644658 · outbound

This paper cites 2024 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2024 , eprint=

Reference 15

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no resolver link, observed 2026-08-01T19:16:39.424723Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:39.424723Z digest=sha256:6b9e069842af7f6b0e9027867ee662c9c1aa1821e00be3bfcc6eb0ef3a580d41

Observation b3ac5142-2f97-4ffb-848c-35e99c597ef0 · outbound

This paper cites ChemOrch: Empowering.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning ChemOrch: Empowering

Reference 16

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no resolver link, observed 2026-08-01T19:16:39.593123Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:39.593123Z digest=sha256:875d8b875a9a28776b466dd595ed31f7bec45f924bb910c0e469d3ba17e556a3

Observation a997aa47-cd08-4956-a287-f810908393f2 · outbound

This paper cites AugGPT: Leveraging ChatGPT for Text Data Augmentation , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning AugGPT: Leveraging ChatGPT for Text Data Augmentation , year=

Reference 17

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no resolver link, observed 2026-08-01T19:16:39.858154Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:39.858154Z digest=sha256:5436db5b1c9988d98575db35ee56e31aa89c06d64d992ef643cd24609d7f038f

Observation 6670701a-8289-4d16-8c1d-8f111c21251b · outbound

This paper cites MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation , url=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation , url=

Reference 18

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no resolver link, observed 2026-08-01T19:16:40.069628Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:40.069628Z digest=sha256:d9f70d70681885c9a0e10e45a88dd9fa1c852208be9b258f8418584989fe57c7

Observation 7056f156-31f4-439b-be41-cdede75f3a93 · outbound

This paper cites The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

Reference 19

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no resolver link, observed 2026-08-01T19:16:40.231652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:40.231652Z digest=sha256:122389738c4b8c0257f2f08bcdec23d075419df5a7938286b1435664f0904458

Observation d7416383-9d9b-40b3-980b-72286b84c52a · outbound

This paper cites The Thirteenth International Conference on Learning Representations , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning The Thirteenth International Conference on Learning Representations , year=

Reference 20

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no resolver link, observed 2026-08-01T19:16:40.407982Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:40.407982Z digest=sha256:1eda856bd45eb081d440701dcf104def34dc021f48b523436bc1396a0be00529

Observation c95b31ad-066e-48cd-9a93-8ef3e4c7d046 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Training Verifiers to Solve Math Word Problems

Reference 21

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no resolver link, observed 2026-08-01T19:16:40.583129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:40.583129Z digest=sha256:bb8ee00d7bf9d8f004aa5389ea2f5859b4c346920fe7760fd8ba7aba21e7199a

Observation 8631f0b4-67bb-41b9-b9c8-8e59b581e471 · outbound

This paper cites Journal of Educational and Behavioral Statistics , volume =.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Journal of Educational and Behavioral Statistics , volume =

Reference 22

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no resolver link, observed 2026-08-01T19:16:40.811463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:40.811463Z digest=sha256:fbaba5d0de667d7b33413b0385886ed313e2c4afd6e96393e2c7a6be2c1319b9

Observation 4028f733-04c8-42a6-8bb2-8c6cca0480c0 · outbound

This paper cites 2024 , url=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2024 , url=

Reference 23

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:40.957900Z digest=sha256:e78c90e464f144d99d88197e5a403504b73f37dceef57dbbc2f0388aa3f7401c

Observation 93907abb-ec6c-4519-9154-dca22124392e · outbound

This paper cites 2021 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2021 , eprint=

Reference 24

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:41.123251Z digest=sha256:fba63c47bef11df2d79b2b49cb9443780ec6b07823d7924da5b9e6c4b9c69c21

Observation 4e24c473-e491-48da-a4f8-a39898762a44 · outbound

This paper cites 2021 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2021 , eprint=

Reference 25

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Observation e1ed80a8-a2ec-4f8a-ac0e-ffb73a4d88aa · outbound

This paper cites Bowman , booktitle=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Bowman , booktitle=

Reference 26

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no resolver link, observed 2026-08-01T19:16:41.408086Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:41.408086Z digest=sha256:6b16ca60e43d62da74b440f1719296ca1962c89591d59e9a934581ae0d5bca30

Observation 30446ef8-0586-46d2-9368-1b2237f3022f · outbound

This paper cites 2023 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2023 , eprint=

Reference 27

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no resolver link, observed 2026-08-01T19:16:41.500661Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:41.500661Z digest=sha256:44a40c74c8fe63f04be25e028ecbe10acd57ff658083a32fb2082d34120c7446

Observation f04fb078-a386-4b4b-a5c9-e810ef619d08 · outbound

This paper cites Kernel Language Entropy: Fine-grained Uncertainty Quantification for.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Kernel Language Entropy: Fine-grained Uncertainty Quantification for

Reference 28

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source=arxiv_source observed=2026-08-01T19:16:41.628791Z digest=sha256:3f52357af20fe7227939cb4503672e21fd27578ebbdd3c282c4aeb4797093649

Observation e6d7c491-2be7-4089-8431-8beeb49b1b43 · outbound

This paper cites an unresolved cited work.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Unresolved cited work

Reference 29

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parse uncertain
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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:41.748959Z digest=sha256:ae11c4bb52109948e16c8ea1940c0684522a8a4f31fa28bc5f9022d02723da2b

Observation 3e284aa7-81da-4a4e-aa8a-40df3e75cde0 · outbound

This paper cites an unresolved cited work.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Unresolved cited work

Reference 30

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:41.909186Z digest=sha256:0c88569b2d315f90c7faebd08bfa4b4d4b4046e46652b6123e82359c0ff9875b

Observation c8d58358-543d-4532-82e0-c300eca4537c · outbound

This paper cites 2025 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2025 , eprint=

Reference 31

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unresolved
no resolver link, observed 2026-08-01T19:16:42.069626Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:42.069626Z digest=sha256:7311f5cb1ce17a15728019c0e044558941c10560734c0c4f8104cf568ac50da8

Observation 45d0a908-5848-4497-b1a0-4a57dd1ad673 · outbound

This paper cites 2024 , url =.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2024 , url =

Reference 32

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:42.241620Z digest=sha256:a73c2b23523859a2d65372db8d91abbf92ca0618e6c57f6f118f6a9306de791a

Observation 3d3d9696-fd1d-4f5f-9888-2c9749005dd4 · outbound

This paper cites Gemma 3 , url=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Gemma 3 , url=

Reference 33

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no resolver link, observed 2026-08-01T19:16:42.358076Z

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source=arxiv_source observed=2026-08-01T19:16:42.358076Z digest=sha256:42259884e0b87befa4444d992efe2b165fa729e86b7f5e25f0911cabea6d9a21

Observation e3d53f09-c6ad-4b3d-963f-df3b0e562185 · outbound

This paper cites Forty-second International Conference on Machine Learning , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Forty-second International Conference on Machine Learning , year=

Reference 34

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no resolver link, observed 2026-08-01T19:16:42.474509Z

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Observation 29367883-9448-4fbe-85e2-c27807c7cde3 · outbound

This paper cites The Thirteenth International Conference on Learning Representations , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning The Thirteenth International Conference on Learning Representations , year=

Reference 35

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Unavailable: canonical work link unavailable.

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Observation d21b5bec-a08b-4ec4-99ee-e16b7882f558 · outbound

This paper cites 2023 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2023 , eprint=

Reference 36

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Unavailable: canonical work link unavailable.

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Observation 315b6c2f-43c8-46b1-b6aa-bbbf882473d4 · outbound

This paper cites and Le, Quoc V and Firat, Orhan.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning and Le, Quoc V and Firat, Orhan

Reference 37

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Unavailable: canonical work link unavailable.

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Observation e19134b3-4931-4d58-872e-0e2db43445c5 · outbound

This paper cites an unresolved cited work.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Unresolved cited work

Reference 38

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source=arxiv_source observed=2026-08-01T19:16:42.956431Z digest=sha256:2a0b508b5f1f43af0cce177600ca5f28ba441a110ecd9c5f2b20764f89af9a5d

Observation 43913c31-1487-4054-9d3c-fad1e071fa57 · outbound

This paper cites 2025 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2025 , eprint=

Reference 39

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:43.101368Z digest=sha256:8cfd12773172d2cdb0cd2de8ab136e743f7bf5899c6d8f14e6cedf3553e0bf0d

Observation fc40a455-6f63-40f5-950e-6f1f3cf030a9 · outbound

This paper cites L lama F actory: Unified Efficient Fine-Tuning of 100+ Language Models.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning L lama F actory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 40

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no resolver link, observed 2026-08-01T19:16:43.211634Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T19:16:43.211634Z digest=sha256:179a33c2f11c30f8f8b0c057d5d0249e6e3883729d84c9543c41b7ddb4af87f6

Observation fcac6cd5-998f-41e1-a8ac-62e56686453b · outbound

This paper cites 2016 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2016 , eprint=

Reference 41

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unresolved
no resolver link, observed 2026-08-01T19:16:43.354458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:43.354458Z digest=sha256:8510b84cc179ddf4225ac3db13f93ff13e42c410137e45c9a0f68a6173b555a4

Observation 9fd482c3-8e75-4c60-a3ab-8fe8a7d6a207 · outbound

This paper cites The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

Reference 42

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no resolver link, observed 2026-08-01T19:16:43.608554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:43.608554Z digest=sha256:ae60cd7ed1f90627770cacd0dc8b6ab636d28b592a6c124c24281fdbe1225508

Observation 498673f2-4e28-449d-a2fd-f3ba3e9aa386 · outbound

This paper cites CDS : Data Synthesis Method Guided by Cognitive Diagnosis Theory.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning CDS : Data Synthesis Method Guided by Cognitive Diagnosis Theory

Reference 43

Resolution
verified exact
doi, observed 2026-08-01T19:19:08.279888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-01T19:16:43.776350Z digest=sha256:51d42e0c0bdc0fd6e9b3db91331f5013f7e3bca73d01d4a9018c55428569cb41

Observation a252d8d6-25ec-43a2-9293-c9d5f5eaf16c · outbound

This paper cites 2024 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2024 , eprint=

Reference 44

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no resolver link, observed 2026-08-01T19:16:43.951194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:43.951194Z digest=sha256:258fa7bc4bf9fa810f4d89e28c7925548608ca8b5543f96410dda89c1093a486

Observation dcd0e12c-fc1f-4bae-8045-50d9a3fbf6ca · outbound

This paper cites 2022 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2022 , eprint=

Reference 45

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unresolved
no resolver link, observed 2026-08-01T19:16:44.125647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:44.125647Z digest=sha256:fa89369abaac1a1e09d1a765be2b2d4f0b456464d253b4e41ef29436746b46d2

Observation 926a7241-20c8-4328-94d9-048cd20c85d9 · outbound

This paper cites 2025 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2025 , eprint=

Reference 46

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unresolved
no resolver link, observed 2026-08-01T19:16:44.300798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:44.300798Z digest=sha256:92d75f0137233158450266479f8a938680473ff063eebd0a9fea8e8421257c5a

Observation f01c923f-3201-4a7e-a2fd-97556fee14fe · outbound

This paper cites 2023 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2023 , eprint=

Reference 47

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unresolved
no resolver link, observed 2026-08-01T19:16:44.503636Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T19:16:44.503636Z digest=sha256:3e827a1513218687a96237467db11e53343ba29fddbb22ffe046959e628e96d0

Observation 95634d21-f569-4bb9-a16b-e79839860ab8 · outbound

This paper cites 2026 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2026 , eprint=

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-01T19:16:44.623575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:44.623575Z digest=sha256:a4013bf57526e1f5f0733497f66efff0f6b92f8de9af7c4941ccb4424e54511c

Observation 9be0d15b-38dc-4cfb-948f-282b0db9836a · outbound

This paper cites 2026 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2026 , eprint=

Reference 49

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unresolved
no resolver link, observed 2026-08-01T19:16:44.693087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:44.693087Z digest=sha256:f8d8ce6f611329e9b3ffdd733fc6687370665100954657dce943ba3e00c2f1c0

Observation 23299422-824f-45a5-9c98-cce71f437d31 · outbound

This paper cites Language Models can Categorize System Inputs for Performance Analysis.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Language Models can Categorize System Inputs for Performance Analysis

Reference 50

Resolution
verified exact
doi, observed 2026-08-01T19:19:08.176100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-01T19:16:44.775223Z digest=sha256:c951d09ce8e6bab25bfe50d849f5b65af299f487a01164fb383329b13d81e3de

Observation bf555187-8dad-468f-a47f-1d41298f8c52 · outbound

This paper cites Second Conference on Language Modeling , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Second Conference on Language Modeling , year=

Reference 51

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no resolver link, observed 2026-08-01T19:16:44.858916Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:44.858916Z digest=sha256:183537137239bdf0cb70d60eee03e87093fb81b37f100dfcaa614bc3132c7ff2

Observation d540051b-933f-4a00-8edc-86995baa9d94 · outbound

This paper cites Forty-second International Conference on Machine Learning , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Forty-second International Conference on Machine Learning , year=

Reference 52

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unresolved
no resolver link, observed 2026-08-01T19:16:44.941979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:44.941979Z digest=sha256:450f27b67f51997d4059eda5b31b4861e2d8e60173f46c772685776ba84bf2a9

Observation e55852d6-264d-4ce1-8575-5e0dc82ce93f · outbound

This paper cites 2024 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2024 , eprint=

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-01T19:16:45.002164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:45.002164Z digest=sha256:5c48a9e4cad08ecec2b2efdfab0275f27465c157a8e679890cc844a646d18238

Observation 5f9a9295-8fd1-4cc2-9c0c-88087406b018 · outbound

This paper cites Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning

Reference 54

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unresolved
no resolver link, observed 2026-08-01T19:16:45.022515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:45.022515Z digest=sha256:97d9fce2d5bd6315ded09a550c4a31c9e64a83915cf290bccaaeb795bb9c3082

Observation 95a6c0ba-16a4-47b6-9057-920769197bbf · outbound

This paper cites The Fourteenth International Conference on Learning Representations , year=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning The Fourteenth International Conference on Learning Representations , year=

Reference 55

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unresolved
no resolver link, observed 2026-08-01T19:16:45.044690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:45.044690Z digest=sha256:8396d26fd5296f46669af73866876065d8ce43e1e805eae3bdf44dcd74ee83f2

Observation d2a905b5-e227-464e-a956-4651fb5eddbd · outbound

This paper cites 2026 , eprint=.

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning 2026 , eprint=

Reference 56

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unresolved
no resolver link, observed 2026-08-01T19:16:45.202633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T19:16:45.202633Z digest=sha256:732062eea2ed3c3e611ce62db05957b5000b4ddd8b7087dac84e091f23a99cc6

Pith citing papers

Observation fdb61e68-0c05-42de-8a15-7b65e0b3a022 · inbound

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics cites this paper.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-06T00:08:28.703155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T00:08:25.571429Z digest=sha256:d52db635fb18b2c335badd5183c2fbce357179ef27f34a9c38d1e1d8379aa27c