Paper Citation Record · LEDGER

PAWS: Preference Learning with Advantage-Weighted Segments

As of 17 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2606.11982.

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

pith.paper-citation-record.v1
2606.11982 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T10:22:51.007376Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

53 of 53 outbound references displayed

verified exact2
verified fuzzy0
unresolved43
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation acd44dbf-29af-4b70-97ee-d018374f89bc · outbound

This paper cites NeurIPS Datasets and Benchmarks Track , year =.

PAWS: Preference Learning with Advantage-Weighted Segments NeurIPS Datasets and Benchmarks Track , year =

Reference 1

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Observation 81578717-6903-46bf-a39a-24737ecb80f3 · outbound

This paper cites Journal of Machine Learning Research , volume =.

PAWS: Preference Learning with Advantage-Weighted Segments Journal of Machine Learning Research , volume =

Reference 2

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Observation 5ab03840-8823-4ec9-bf6b-61095e9df7cf · outbound

This paper cites Proceedings of the 37th International Conference on Neural Information Processing Systems , year =.

PAWS: Preference Learning with Advantage-Weighted Segments Proceedings of the 37th International Conference on Neural Information Processing Systems , year =

Reference 3

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Observation c44413fc-bfd2-4ed5-8dd6-d254581798a4 · outbound

This paper cites International Conference on Learning Representations (ICLR) , year =.

PAWS: Preference Learning with Advantage-Weighted Segments International Conference on Learning Representations (ICLR) , year =

Reference 4

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Observation 9ab2b5ad-7be8-4b6b-bf2c-7d9eec15d349 · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning , year =.

PAWS: Preference Learning with Advantage-Weighted Segments Proceedings of the 41st International Conference on Machine Learning , year =

Reference 5

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Observation 4831dc82-f64c-4e00-b32f-f9a6d6545e57 · outbound

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

PAWS: Preference Learning with Advantage-Weighted Segments Advances in Neural Information Processing Systems , volume =

Reference 6

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Observation e25ba11a-4ae1-448c-8a58-e4a6bd9a45a6 · outbound

This paper cites Proceedings of the 34th International Conference on Neural Information Processing Systems , year =.

PAWS: Preference Learning with Advantage-Weighted Segments Proceedings of the 34th International Conference on Neural Information Processing Systems , year =

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-27T10:22:51.007376Z

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Observation 35b7ee3d-265d-49be-8304-c112ed447fc2 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

PAWS: Preference Learning with Advantage-Weighted Segments Fine-Tuning Language Models from Human Preferences

Reference 8

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Source-reported events for the cited work

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Observation a724ca4a-cb26-4ac3-8886-9510f09ae5ad · outbound

This paper cites Preference Transformer: Modeling Human Preferences using Transformers for.

PAWS: Preference Learning with Advantage-Weighted Segments Preference Transformer: Modeling Human Preferences using Transformers for

Reference 9

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Source-reported events for the cited work

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Observation 7d18e2af-bedd-40c2-9a08-e4624264b690 · outbound

This paper cites Nature , year =.

PAWS: Preference Learning with Advantage-Weighted Segments Nature , year =

Reference 10

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Source-reported events for the cited work

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Observation 8ebc35ec-39f2-4c8a-a342-6552218ff125 · outbound

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PAWS: Preference Learning with Advantage-Weighted Segments International Conference on Learning Representations , year =

Reference 11

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Observation 71a12045-9e9d-4d8d-839e-502f0440374d · outbound

This paper cites Transactions on Machine Learning Research , year =.

PAWS: Preference Learning with Advantage-Weighted Segments Transactions on Machine Learning Research , year =

Reference 12

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Observation 759c58c9-a30f-48cd-b900-5d437f58e889 · outbound

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

PAWS: Preference Learning with Advantage-Weighted Segments Proceedings of the AAAI Conference on Artificial Intelligence , volume =

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-27T10:22:51.007376Z

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Observation 387a86f7-d6d1-4b1e-8e23-29172935bc09 · outbound

This paper cites Journal of Machine Learning Research , volume =.

PAWS: Preference Learning with Advantage-Weighted Segments Journal of Machine Learning Research , volume =

Reference 14

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Observation 2488a7e6-b8c0-4946-9667-ed294de75967 · outbound

This paper cites Advances in neural information processing systems , volume =.

PAWS: Preference Learning with Advantage-Weighted Segments Advances in neural information processing systems , volume =

Reference 15

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Observation c075e239-4c6f-4c99-930a-4b47c27ad302 · outbound

This paper cites AWAC: Accelerating Online Reinforcement Learning with Offline Datasets.

PAWS: Preference Learning with Advantage-Weighted Segments AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 16

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Source-reported events for the cited work

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Observation 6f985021-32f5-467e-9c9f-8a712afeae4b · outbound

This paper cites 2004 , publisher =.

PAWS: Preference Learning with Advantage-Weighted Segments 2004 , publisher =

Reference 17

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Observation b2b6cb4c-3ff6-41ff-99b5-dfa8b583a97a · outbound

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

PAWS: Preference Learning with Advantage-Weighted Segments International Conference on Learning Representations , year =

Reference 18

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Observation 99ea51cf-6111-41b4-af8d-121fc4af3347 · outbound

This paper cites Proceedings of the 24th international conference on Machine learning , pages =.

PAWS: Preference Learning with Advantage-Weighted Segments Proceedings of the 24th international conference on Machine learning , pages =

Reference 19

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Observation 6c02fddf-0003-4e30-8a2b-060bba30961f · outbound

This paper cites Advances in neural information processing systems , volume =.

PAWS: Preference Learning with Advantage-Weighted Segments Advances in neural information processing systems , volume =

Reference 20

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Observation 8187606f-c1de-4073-b985-5ac91cc39c33 · outbound

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

PAWS: Preference Learning with Advantage-Weighted Segments Advances in Neural Information Processing Systems , volume =

Reference 21

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Observation 8c2fc8d0-a141-40cf-b0ed-0aba6604c8ac · outbound

This paper cites Advances in Neural Information Processing Systems (NeurIPS) , year =.

PAWS: Preference Learning with Advantage-Weighted Segments Advances in Neural Information Processing Systems (NeurIPS) , year =

Reference 22

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Observation ab89b561-5bbf-4629-93f7-932e89aab344 · outbound

This paper cites Proximal Policy Optimization Algorithms.

PAWS: Preference Learning with Advantage-Weighted Segments Proximal Policy Optimization Algorithms

Reference 23

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Observation ce49d7a1-5390-4592-9480-e559ae6bb3ec · outbound

This paper cites International Conference on Machine Learning (ICML) , year =.

PAWS: Preference Learning with Advantage-Weighted Segments International Conference on Machine Learning (ICML) , year =

Reference 24

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Observation 673d7464-3f29-42d2-9c1d-d077a980f23a · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning.

PAWS: Preference Learning with Advantage-Weighted Segments Offline Reinforcement Learning with Implicit Q-Learning

Reference 25

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Observation 53003653-8543-4981-9322-398fd386453d · outbound

This paper cites Proceedings of the 32nd International Conference on Machine Learning (ICML) , series =.

PAWS: Preference Learning with Advantage-Weighted Segments Proceedings of the 32nd International Conference on Machine Learning (ICML) , series =

Reference 26

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Observation 7cf078c1-3899-4263-b9d8-4f1119ad30d3 · outbound

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PAWS: Preference Learning with Advantage-Weighted Segments The Twelfth International Conference on Learning Representations , year =

Reference 27

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PAWS: Preference Learning with Advantage-Weighted Segments The Thirteenth International Conference on Learning Representations , year =

Reference 28

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Observation 5b2483c5-49d6-4d52-8f3b-8313b07552bc · outbound

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PAWS: Preference Learning with Advantage-Weighted Segments Geometry-aware

Reference 29

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Observation 7926bc03-cc69-4fd8-bd52-469cd1f61460 · outbound

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PAWS: Preference Learning with Advantage-Weighted Segments , title =

Reference 30

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Observation db7cc7be-7a40-41c2-89df-7cff68283aad · outbound

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PAWS: Preference Learning with Advantage-Weighted Segments Cognition , volume =

Reference 31

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Observation 5e426a98-0132-45a8-9aa1-6a8e552c89cd · outbound

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PAWS: Preference Learning with Advantage-Weighted Segments Unpacking

Reference 32

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Observation c7357dd1-5ff0-48df-88c0-a1d5e3e3a445 · outbound

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PAWS: Preference Learning with Advantage-Weighted Segments High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 33

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Observation 056e8dba-613d-4a65-89ce-75b125ea3084 · outbound

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PAWS: Preference Learning with Advantage-Weighted Segments , author =

Reference 34

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This paper cites WebGPT: Browser-assisted question-answering with human feedback.

PAWS: Preference Learning with Advantage-Weighted Segments WebGPT: Browser-assisted question-answering with human feedback

Reference 35

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PAWS: Preference Learning with Advantage-Weighted Segments International Conference on Machine Learning , pages =

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PAWS: Preference Learning with Advantage-Weighted Segments PrefMMT: Modeling Human Preferences in Preference-based Reinforcement Learning with Multimodal Transformers

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PAWS: Preference Learning with Advantage-Weighted Segments Conference on Robot Learning , pages =

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PAWS: Preference Learning with Advantage-Weighted Segments Advances in Neural Information Processing Systems , pages =

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PAWS: Preference Learning with Advantage-Weighted Segments Proceedings of the 42nd International Conference on Machine Learning (ICML) , year =

Reference 40

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PAWS: Preference Learning with Advantage-Weighted Segments Proceedings of the 35th International Conference on Machine Learning (ICML) , year =

Reference 41

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PAWS: Preference Learning with Advantage-Weighted Segments International Conference on Learning Representations (ICLR) , year =

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PAWS: Preference Learning with Advantage-Weighted Segments The International Journal of Robotics Research (IJRR) , year =

Reference 43

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PAWS: Preference Learning with Advantage-Weighted Segments Advances in Neural Information Processing Systems (NeurIPS) , year =

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PAWS: Preference Learning with Advantage-Weighted Segments Proceedings of the 5th Conference on Robot Learning (CoRL) , year =

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PAWS: Preference Learning with Advantage-Weighted Segments Advances in Neural Information Processing Systems , year =

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PAWS: Preference Learning with Advantage-Weighted Segments 36th Conference on Neural Information Processing Systems (NeurIPS 2023) , year =

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PAWS: Preference Learning with Advantage-Weighted Segments Hindsight Preference Learning for Offline Preference-based Reinforcement Learning

Reference 48

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PAWS: Preference Learning with Advantage-Weighted Segments Hindsight

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PAWS: Preference Learning with Advantage-Weighted Segments Gymnasium: A Standard Interface for Reinforcement Learning Environments

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PAWS: Preference Learning with Advantage-Weighted Segments International conference on machine learning , pages =

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PAWS: Preference Learning with Advantage-Weighted Segments D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 52

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PAWS: Preference Learning with Advantage-Weighted Segments International Conference on Learning Representations , year=

Reference 53

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Pith citing papers

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