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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 15 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 2026-08-15T06:32:42.880941+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=sha256:2176b8707d2141ebc6a5d25a7343fdf3b3fcf2cf8d7108c66ad8f53caa561830

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=sha256:4b44841cc506924818f56954853bb826b1694f48bbfbf6bfcd0d9a0d5ed0a82e

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=sha256:18e2a7818163c3f95752950078b63c0dfb6ce31ee42e788fa29a8ff2c182c805

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=sha256:000889296e1ecc9178b0efa8971948ea68d2c9e486002d14edd303213b1e3734

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=sha256:92868bc8a5c423aa25a0d7ea704810f550ef226bcb4ddf74513a2da566445682

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=sha256:9d9158ae7d30e88d0da73a3663d1287f422546b04d7896411d551e92a8e3d174

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=sha256:1ad1cf3d3336e891726c142b145788ce4763454b39cc26ec7730d3046aa0e598

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=sha256:8cb4a1fb2f4b2f20c34aec27e57b8aaab8a4b7acb983fb404c11efe22c3f3421

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=sha256:0f59bd7083d6ad531b839d4f3123bbaf85ba2ccd26272529f077d63b7de41c0d

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:f9b232e7cf3092789d0ea2a15fb6c48bd61eb7a377a6e1079f35c66f8e77e39d

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=sha256:0ccce7a6a84cba15daa5edb9ecf36c481884f99e6875073ef6fdc954d5bc8b87

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:c22ab0efe13c486ab276ff3a48436ef97bab8bcae3adc1c59db62a2c57832f88

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=sha256:10763a557829be2535329421c77ed88e011e5fb75dd88146288ad3ef5a19b4c3

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:fc81572cb52c3e52fd21a66dde2f3966d9101fd89ccb4dfe81e15544cd230f93

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=sha256:056f6e2ba1dc2360a1117b0be8bad30e604e0acd8ea82bd5206a7484885d2b44

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:a9576a46d04e940fceb4fcb27c1e7fca36b87fd330d08df66de756764e0e511e

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=sha256:7bf6ce0fb92081643b92964019c17b1cc8478f8a536b6b17decd6ab3265e65f8

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=sha256:9d8680df28eb103158e294946c860e9b00d4d2d058300c95e40c69a06d04e07d

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=sha256:8676af727ada8c054fa98bed155c061a93a40de31fe146219ba7f0bfaecef4f9

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:b330ee8cff6170867e578abd694c0c7e6b2bfae58d1fe56198c6741c02208622

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:d064b5881f3e3582230b88c7f4ef462873eba8a3ae9a4dec217e12d504cfd71c

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=sha256:55fbfc472ccd42e49be3f6fc42e6c4920ad2beada8c963121a043ba047e2d7d7

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:e208cc8d94cab75bd8b9eab36ce9a9022c3278051f7cfd9687e8adc9358eb4c9

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:d57bd1c659dfd18f6449535419f98cedf8539c162b4bfcd7e71504a258f02447

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:ead4776bce67d216a396ff2c6408208d5b86a17e8069b07da092b82b4fb03d7a

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=sha256:970df9ca4afff191117d794a8a6d5d6b75d01cedbcc4cc8e0c635b3e27fbd813

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=sha256:109de36a0534db0922cd8a3fe97f6aea4fe59a7e2f1f5010398cedfd2e60674e

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=sha256:462e6e739e2d93cda960a0cdd1137e6aba8ceddb8884522c1652509636b2c352

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=sha256:530882e1529b32a536ade262062a3b2303ae8414858c70ee887ab8c30355ed32

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:d4bd461867c365274ee18795cbebc7db630580115fc7165ff6fde0898faf8521

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=sha256:89aef8ae4e9e327e722fc882ed4ae021372ca7e0c8bad24189f1ea0e78b3a0f3

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=sha256:9d94189775d1d23c979385a4888282707feaa70a896d0069d0a486191620e9d1

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=sha256:3fd954d46acdc5d5c692546c2028c4b38e0199bbc005f149e30108db45fd48f9

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=sha256:54027e83e2b123ba90a6866f5deda28f3165d5a99d342e34bc55c16c63c0ede4

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=sha256:20108e480399c1e9a3d9211126f31864bb272e4f82bbbabb65182a430ed104bf

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=sha256:6c1a7bdf0b0f62b7d893582541f545f09773f55fa3ae0b345846430abe53b8f5

Pith citing papers

No inbound Pith citation observations are available.