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Paper Citation Record · LEDGER

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.29601.

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

pith.paper-citation-record.v1
2607.29601 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:51:00.045896Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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  • verified fuzzy0
  • unresolved36
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 45018918-94ac-444e-9349-22f791115ca0 · outbound

This paper cites Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=

Reference 1

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source=arxiv_source observed=2026-08-03T03:50:56.222718Z digest=sha256:f9aab854d7ebdb500c0641a3cb197cb19d8241b58f8774fa219928f8be89e789

Observation 07253755-4749-4709-88e8-ff7947f56764 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 2

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source=arxiv_source observed=2026-08-03T03:50:56.289017Z digest=sha256:9e12b9840bcce66a52837a5f93b0cd424e07c215ac5cc67e5fffdd8970ab1e55

Observation 30d0ee45-2c49-4357-91fb-e5f4c5b2c754 · outbound

This paper cites Nature Machine Intelligence , volume=.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Nature Machine Intelligence , volume=

Reference 3

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source=arxiv_source observed=2026-08-03T03:50:56.448901Z digest=sha256:e01ca597b2e4e5263a382eee782b485d37df69176cab9ff7f96eafafb7426c8e

Observation 38350aaa-759f-403c-8b85-056547181446 · outbound

This paper cites Findings of the Association for Computational Linguistics: EACL 2023 , pages =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Findings of the Association for Computational Linguistics: EACL 2023 , pages =

Reference 4

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source=arxiv_source observed=2026-08-03T03:50:56.486806Z digest=sha256:82a30b6a9803f90d4e5c6ecea65553d45bb272d10b0dbca625de42450feb66ea

Observation c148fccc-aa2c-413d-957b-d0228746feeb · outbound

This paper cites Advances in Neural Information Processing Systems , volume =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Advances in Neural Information Processing Systems , volume =

Reference 5

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source=arxiv_source observed=2026-08-03T03:50:56.549755Z digest=sha256:e60246e82580f8540461cfa25e2dfedec7f7aff6aaa74b715b22a86456b0a88e

Observation 5c992202-3e17-4482-a5c7-0f0b443ccd15 · outbound

This paper cites Proceedings of the 36th International Conference on Machine Learning , series =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Proceedings of the 36th International Conference on Machine Learning , series =

Reference 6

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source=arxiv_source observed=2026-08-03T03:50:56.636729Z digest=sha256:8261f5fbc235ae9239acbb12adde50fadf6b8c2163d47624ed6edf3c4ea24003

Observation 0f21f527-486a-4f15-96fe-2483a0aba867 · outbound

This paper cites and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu , title =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu , title =

Reference 7

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source=arxiv_source observed=2026-08-03T03:50:56.701311Z digest=sha256:a75fe791a75b42da78e42e059c1b807df1e159e016a1f0baabe89a9d3f4feb78

Observation 5910cea5-3385-4e28-9812-a20c1a716522 · outbound

This paper cites Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages =

Reference 8

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source=arxiv_source observed=2026-08-03T03:50:56.810156Z digest=sha256:7246c95620bf4dc450a6347dc5d9215be5c616407b74c76f614890cd5273bde0

Observation df347900-eff2-4ac3-af10-49b45fbcb77e · outbound

This paper cites an unresolved cited work.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Unresolved cited work

Reference 9

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source=arxiv_source observed=2026-08-03T03:50:56.893299Z digest=sha256:7a8e65ba8f3378834de4a7253ff08b8748287bc878a9c39cd839ac1f821af576

Observation 4bf36c49-7ab3-4517-8fed-f870f6bbc495 · outbound

This paper cites , title =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs , title =

Reference 10

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source=arxiv_source observed=2026-08-03T03:50:56.999647Z digest=sha256:2bd7ea6a413696325d0211c3eb5e15e48b8e18bdb747a1734f0c933f4a983364

Observation 785ed27c-2d85-4e2f-b1bd-ee521acbbf25 · outbound

This paper cites Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , pages =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , pages =

Reference 11

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source=arxiv_source observed=2026-08-03T03:50:57.175377Z digest=sha256:06c77090543581e5398772358fff409eac3bf6912b9e2793c6972fe95d1ce162

Observation 0f6c7e5e-4678-4ee9-b7ed-44551e6c5f66 · outbound

This paper cites Advances in Neural Information Processing Systems , volume =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Advances in Neural Information Processing Systems , volume =

Reference 12

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source=arxiv_source observed=2026-08-03T03:50:57.428385Z digest=sha256:641dc225b30db6a8e77f49899cdc247ef595b51f324e2c3d9c500f62122f0b5d

Observation cfdb1259-b35c-4799-8dc7-1c9afce076fd · outbound

This paper cites an unresolved cited work.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Unresolved cited work

Reference 13

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source=arxiv_source observed=2026-08-03T03:50:57.537502Z digest=sha256:6842c98fe2874ad678549eea046295aff4b7500dacc8c32eea21d692ea47fded

Observation 7ec228e4-347b-48e9-b065-3355ecd60998 · outbound

This paper cites UniPELT: A unified framework for parameter-efficient language model tuning , booktitle =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs UniPELT: A unified framework for parameter-efficient language model tuning , booktitle =

Reference 14

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source=arxiv_source observed=2026-08-03T03:50:57.679357Z digest=sha256:2f06c190857891629ac1efb4fea1e79d159a962cf6eb01cfd2f03f026e74070a

Observation 481c99d3-7b4e-46fa-a895-6ef23bcc7997 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =

Reference 15

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source=arxiv_source observed=2026-08-03T03:50:57.819061Z digest=sha256:3b8bb8185a07fe137eb6f66eb958b95c3a9566a8ebadc8e36dbf328c450ef61d

Observation 3410ecbc-9c14-46f3-afd3-b9e0c0a10827 · outbound

This paper cites Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages =

Reference 16

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source=arxiv_source observed=2026-08-03T03:50:57.982595Z digest=sha256:393def42fb890bdd7de21085f8c0c028b058bb24a281c8e53794338a8d864810

Observation 221b310a-cbcb-4dae-a670-8ff326de5077 · outbound

This paper cites AdapterFusion: Non-destructive task composition for transfer learning , booktitle =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs AdapterFusion: Non-destructive task composition for transfer learning , booktitle =

Reference 17

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source=arxiv_source observed=2026-08-03T03:50:58.119928Z digest=sha256:29b8f3668b3493792f12d34b72f609da6352faef49e7419ca113a4be4f647d9c

Observation 3808e523-d0f6-4f8f-83cd-f03c4d67e9fc · outbound

This paper cites Progressive Neural Networks.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Progressive Neural Networks

Reference 18

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source=arxiv_source observed=2026-08-03T03:50:58.175667Z digest=sha256:b99f6910a644f92f550054475f067f1c15ebc7e3b200b1456e6062e0b29d4b11

Observation fb4dff3b-b020-4250-ad50-0c996a57523b · outbound

This paper cites Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models

Reference 19

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source=arxiv_source observed=2026-08-03T03:50:58.258747Z digest=sha256:fa1ffa8cb7339e365163bf6ece8c2b51061b8e38b23f31a27dfe3d144a81a373

Observation 7e265a5f-c706-4087-8eea-5f0e0b7845d6 · outbound

This paper cites TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models

Reference 20

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source=arxiv_source observed=2026-08-03T03:50:58.338763Z digest=sha256:d749b8edac118f0bc4c5f61f390ecac235184f11a41857131f90b17143668fb5

Observation 143fc603-97c9-4060-baf3-640385ead246 · outbound

This paper cites Advances in Neural Information Processing Systems , volume =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Advances in Neural Information Processing Systems , volume =

Reference 21

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source=arxiv_source observed=2026-08-03T03:50:58.421891Z digest=sha256:bd9ed66a0e98f087009f29d580249f55db5925d1eae022ff0e02b7593c7e394c

Observation 7e0eecf9-0775-45c4-88dd-b3463b7da17c · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 22

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source=arxiv_source observed=2026-08-03T03:50:58.502223Z digest=sha256:0e9431098f71698aa72d3a9413b236158dab04d661b65ec8f976c17137870731

Observation dce76c63-7c73-4c37-ac19-d7e11911d089 · outbound

This paper cites IEEE Transactions on Knowledge and Data Engineering , volume =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs IEEE Transactions on Knowledge and Data Engineering , volume =

Reference 23

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source=arxiv_source observed=2026-08-03T03:50:58.582981Z digest=sha256:53969a4a95ccb1529890c659d1b069a907919a8b3313f08e41b451a41e76675d

Observation 891e5ca8-2274-4d12-915e-77838b9ed7b7 · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 24

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source=arxiv_source observed=2026-08-03T03:50:58.669078Z digest=sha256:6655cc3e8e96b472d49696e7e27b9ccd6a7d484b9ff2f599a21f219741ba736b

Observation 8b875ad6-5590-4349-8c7b-a8afcb801813 · outbound

This paper cites Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 25

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source=arxiv_source observed=2026-08-03T03:50:58.753155Z digest=sha256:6b4c47bc30b64b9e901fa765271b149dcf15a241b7c735a5d4195ef93d154697

Observation db488867-646b-43f5-955a-eb3583255f3c · outbound

This paper cites MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts

Reference 26

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source=arxiv_source observed=2026-08-03T03:50:58.835610Z digest=sha256:2d2aa028a927356ee85b76a0e8b2bbf89b67e3fded86a3152b7e4fd03155ab78

Observation f95fc609-8b2b-4b8c-88a5-72801ce26d94 · outbound

This paper cites an unresolved cited work.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Unresolved cited work

Reference 27

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source=arxiv_source observed=2026-08-03T03:50:58.894249Z digest=sha256:ade72ec8465c6f037ac005043498a73f0a4df0d5853746bf921c6bfe7e6912a7

Observation adcadb19-3cd8-4a19-a1de-0ea8b56f6c48 · outbound

This paper cites Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers) , pages =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers) , pages =

Reference 28

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source=arxiv_source observed=2026-08-03T03:50:58.955765Z digest=sha256:470c9006882762529f44d59c9947ddfbb7ee6e3b225d83f46795e5c52f99c9c0

Observation 113a9142-769a-4291-9121-43e2a648acd2 · outbound

This paper cites an unresolved cited work.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Unresolved cited work

Reference 29

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source=arxiv_source observed=2026-08-03T03:50:58.971890Z digest=sha256:28747a550bed1049ece0719a2b85d3fc2328891ca9091ba4d732fd649b735990

Observation 04622993-07b7-4915-82d9-9fc10043d3ad · outbound

This paper cites an unresolved cited work.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Unresolved cited work

Reference 30

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source=arxiv_source observed=2026-08-03T03:50:59.020982Z digest=sha256:427ca733d5f2daabf8bda9ca8279ca5b95e109449fbd7dd0d649ce504f5d0e42

Observation 354bc783-aba8-4c5e-80c9-3249ca475f2c · outbound

This paper cites ACM Computing Surveys , volume =.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs ACM Computing Surveys , volume =

Reference 31

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source=arxiv_source observed=2026-08-03T03:50:59.186728Z digest=sha256:6b516d3e3876d8685e615b0a94d05e7e458c95bb52ab8630be7931a281309c14

Observation 94b7a491-dda5-4163-a5d8-1d6599ff2bed · outbound

This paper cites Parameter-Efficient Continual Fine-Tuning: A Survey.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Parameter-Efficient Continual Fine-Tuning: A Survey

Reference 32

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source=arxiv_source observed=2026-08-03T03:50:59.444442Z digest=sha256:25013028490d70ba3a5e958e6dc503e6fa565d39be609f0f2f3d698682124c47

Observation 2b4893f7-3f76-4f33-a52c-a591f920ed5a · outbound

This paper cites CHIRPs: Change-Induced Regret Proxy metrics for Lifelong Reinforcement Learning.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs CHIRPs: Change-Induced Regret Proxy metrics for Lifelong Reinforcement Learning

Reference 33

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source=arxiv_source observed=2026-08-03T03:50:59.663566Z digest=sha256:a141d305a3902ec5ffe0e874d0a8ea762ee8f7da5b9664281805a432555562e5

Observation d83adc6c-6466-4318-82d5-53c5b2060bff · outbound

This paper cites Bossens and Adam J.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Bossens and Adam J

Reference 34

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source=arxiv_source observed=2026-08-03T03:50:59.832415Z digest=sha256:ee5a6d5cf9871b1ad0ed896e3a7cf1c26d769b937a55bc0f655695253f883ef9

Observation cb24f58e-f5b1-47f3-a22f-5a8e8f7f37e8 · outbound

This paper cites 2025 , eprint=.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs 2025 , eprint=

Reference 35

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source=arxiv_source observed=2026-08-03T03:50:59.946612Z digest=sha256:b05bd80e23746f632cda3c49f8bb94858fe8932a7ecbab7a5be039c3733dfa57

Observation 9cf63e09-089a-4166-9b39-f98d685bddb5 · outbound

This paper cites M o RE : A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs M o RE : A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning

Reference 36

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source=arxiv_source observed=2026-08-03T03:51:00.045896Z digest=sha256:ead0addcb89ceef2f2f66316af680bdca0604cba3a3950f23d13be136364f36b

Pith citing papers

No inbound Pith citation observations are available.