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Paper Citation Record · LEDGER

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning

As of 13 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2607.17760.

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

pith.paper-citation-record.v1
2607.17760 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T17:09:59.692161Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

59 of 59 outbound references displayed

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External citation measurements

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Outbound references

Observation ed4851a7-4f04-4232-811b-8e244ad42bc8 · outbound

This paper cites 1990 , doi =.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning 1990 , doi =

Reference 1

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Observation 145b0cdc-7475-49d2-ae9c-3f8cea3d12ce · outbound

This paper cites Journal of Basic Engineering , volume=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Journal of Basic Engineering , volume=

Reference 2

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Observation c9f5b4f5-087c-435f-805a-507a70486bc2 · outbound

This paper cites Scaling Learning Algorithms Towards.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Scaling Learning Algorithms Towards

Reference 3

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Observation 1a008e90-dad3-466b-a021-8bf5810a4be9 · outbound

This paper cites and Osindero, Simon and Teh, Yee Whye , journal =.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning and Osindero, Simon and Teh, Yee Whye , journal =

Reference 4

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Observation 98f7329c-a5a7-416b-bf0b-552960a556d2 · outbound

This paper cites 2016 , publisher=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning 2016 , publisher=

Reference 5

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source=arxiv_source observed=2026-08-01T17:09:55.041382Z digest=sha256:811efec17ef62dc651bdbcb4b330496d36b1cf1e198283500f836fd07c17d316

Observation 30bfb89e-3fbd-4eba-b344-b8d93285b072 · outbound

This paper cites 2018 , publisher=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning 2018 , publisher=

Reference 6

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source=arxiv_source observed=2026-08-01T17:09:55.167794Z digest=sha256:b2bc9bac6dcda5ec5fdce810b1499d21898a54feb9d8a01d4d9f75cd7976cc75

Observation f5234d46-996c-4379-a284-061db527c00b · outbound

This paper cites and Russell, Stuart J.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning and Russell, Stuart J

Reference 7

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Observation 83f9554e-75e2-4751-8da5-2d593751a66e · outbound

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning International Conference on Machine Learning , year=

Reference 8

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Observation 2b42e777-8c35-4118-b247-ba39adbd6969 · outbound

This paper cites 2017 , eprint=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning 2017 , eprint=

Reference 9

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Observation 4baecfa6-466f-4b8f-b9db-04adab1df920 · outbound

This paper cites 2016 , eprint=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning 2016 , eprint=

Reference 10

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Observation 3da0040a-582e-47e7-b255-74b8820e0961 · outbound

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Advances in Neural Information Processing Systems , editor=

Reference 11

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Observation 2dcd94bc-24fc-4a5b-bd06-63dfaedb0ff1 · outbound

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning International Conference on Learning Representations , year=

Reference 12

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Observation fa04c3d8-987f-4bbe-965b-711e182214bb · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 13

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Observation eeeca7d3-d2e3-444f-bc3d-783746d44ba5 · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 14

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This paper cites 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=

Reference 15

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Observation 9a614bc0-45e2-43ea-9500-fc52255c1916 · outbound

This paper cites Conference on robot learning , pages=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Conference on robot learning , pages=

Reference 16

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source=arxiv_source observed=2026-08-01T17:09:56.057675Z digest=sha256:afc5c3b7095351106bde74cbfda8c07eae87522739450af8f9ef5cc80b40c638

Observation 5c89edee-61a3-479c-8f33-09684b0e64f2 · outbound

This paper cites 2020 , url =.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning 2020 , url =

Reference 17

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Observation 3e371397-935e-4fa2-878f-226b70d6a26e · outbound

This paper cites Conference on robot learning , pagesfu2018airl=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Conference on robot learning , pagesfu2018airl=

Reference 18

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Observation f5965891-aa36-43d8-b866-e321867e46ef · outbound

This paper cites Proceedings of the 38th International Conference on Machine Learning , pages =.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Proceedings of the 38th International Conference on Machine Learning , pages =

Reference 19

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Observation ca9f53ad-4ccc-4ea4-be52-f77719301f04 · outbound

This paper cites Neural Information Processing Systems , year=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Neural Information Processing Systems , year=

Reference 20

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Observation 9c9f290f-70f7-45f4-8dd4-dad648fef37f · outbound

This paper cites Dragan and Sergey Levine , booktitle=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Dragan and Sergey Levine , booktitle=

Reference 21

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning 2021 , url=

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Deep RL Workshop NeurIPS 2021 , year=

Reference 23

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This paper cites Proceedings of the 39th International Conference on Machine Learning , pages =.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Proceedings of the 39th International Conference on Machine Learning , pages =

Reference 24

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Observation f448c726-385f-429a-acda-c276ca681972 · outbound

This paper cites Proceedings of the 40th International Conference on Machine Learning , year =.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Proceedings of the 40th International Conference on Machine Learning , year =

Reference 25

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This paper cites International conference on machine learning , pages=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning International conference on machine learning , pages=

Reference 26

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Advances in neural information processing systems , volume=

Reference 27

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 28

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning International conference on machine learning , pages=

Reference 29

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning The Twelfth International Conference on Learning Representations , year=

Reference 30

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This paper cites Proceedings of the 39th International Conference on Machine Learning , year =.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Proceedings of the 39th International Conference on Machine Learning , year =

Reference 31

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning The Eleventh International Conference on Learning Representations , year=

Reference 32

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Advances in Neural Information Processing Systems , editor=

Reference 33

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning , author=

Reference 34

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning International Conference on Learning Representations , year=

Reference 35

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos

Reference 36

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning 2nd Conference on Robot Learning , year=

Reference 37

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Observation dc7bfc09-67dc-4ce0-a557-7f276775133c · outbound

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning 2018 , eprint=

Reference 38

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Observation fa3b6d2d-dbb8-4e18-ac6f-385908cbddd4 · outbound

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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Proceedings of the 38th International Conference on Machine Learning , year =

Reference 39

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source=arxiv_source observed=2026-08-01T17:09:57.996038Z digest=sha256:22a64fc0177ab149d58df6e3382476adf459e5e223fd37767fab657b6eb83178

Observation 03d432a9-786b-4d11-9a23-bed7bd6fb696 · outbound

This paper cites Conference on robot learning , pages=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Conference on robot learning , pages=

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source=arxiv_source observed=2026-08-01T17:09:58.086878Z digest=sha256:d9c2a7a906203baee1eb785e42a0f1d100d1e546308ead711e080cf149caf515

Observation abbc73c2-9c23-4335-afcd-d254db6bdb59 · outbound

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Advances in neural information processing systems , volume=

Reference 41

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source=arxiv_source observed=2026-08-01T17:09:58.170870Z digest=sha256:35e4f0178c72d38b606ec777ed4e9f388c0dcc81ceb58cdac8cc366a5666dcb4

Observation 6df95de4-3235-4d91-9e19-c8e4366c83d6 · outbound

This paper cites One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning

Reference 42

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source=arxiv_source observed=2026-08-01T17:09:58.264345Z digest=sha256:a77b6768ba257ab0a546cde6beff647edfc18beb5e3b5af2a844fbc16e57d0e7

Observation d5090d62-7bfc-47ce-a40c-c6bcbdcd7ac3 · outbound

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Advances in neural information processing systems , volume=

Reference 43

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source=arxiv_source observed=2026-08-01T17:09:58.343078Z digest=sha256:4a6a8a79b0e72b6f6b904421e1ef058dc44b07720a3f496f8a253c9a255698f8

Observation e773526f-6a99-4e7e-8b4b-aacc54b7472c · outbound

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Forty-first International Conference on Machine Learning , year=

Reference 44

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source=arxiv_source observed=2026-08-01T17:09:58.426664Z digest=sha256:d08ce02d165f61168bccc9b18ae5a0e653b8d534887de0c563a79db17e929ff8

Observation 7b7db707-62a2-4377-b004-572a32f13e8e · outbound

This paper cites 6th Annual Conference on Robot Learning , year=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning 6th Annual Conference on Robot Learning , year=

Reference 45

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source=arxiv_source observed=2026-08-01T17:09:58.518394Z digest=sha256:85bf496fa90faeca03f0cfa9502d8a1e75f69f0099dd44189e4167e753300003

Observation 638ecf4b-38c6-46ab-8375-b8f391043699 · outbound

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning International Conference on Learning Representations , year=

Reference 46

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source=arxiv_source observed=2026-08-01T17:09:58.571350Z digest=sha256:c797ad8fe2b4d0331f159cc13221ba423632c2e3461ddcad11b83e5039e157cd

Observation 772d3b7a-4514-4978-b7f2-c9c822b39ec1 · outbound

This paper cites an unresolved cited work.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Unresolved cited work

Reference 47

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source=arxiv_source observed=2026-08-01T17:09:58.657100Z digest=sha256:d20f8f93801b3262465c368ea47942ed23addb0dbbf7e34f56bf42ced347d79a

Observation 67770ddc-8f52-4ef6-9cce-d2583d3c1e41 · outbound

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 48

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source=arxiv_source observed=2026-08-01T17:09:58.756024Z digest=sha256:4e6c46f99b3eb286063450bde8c962531d4e31eafa7c8a83daa88172174fc7d6

Observation 5decf9e6-fb01-487a-b515-ac1233f6c478 · outbound

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 49

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source=arxiv_source observed=2026-08-01T17:09:58.837656Z digest=sha256:94c7c2cc02493562b7944bf7499c18fb692e79408a67c5c3b00a8b8c03d6823a

Observation dc49dc7a-d1eb-4469-8cbe-8eed3a084e26 · outbound

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 50

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source=arxiv_source observed=2026-08-01T17:09:58.937556Z digest=sha256:4d00da87baf23c0a75cf58c3f285ece19d5a56a8cfb07df55e969b7b5ccaf929

Observation 8ef9b015-621c-4459-b4a8-0da76857d944 · outbound

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning International Conference on Learning Representations , year=

Reference 51

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source=arxiv_source observed=2026-08-01T17:09:59.021724Z digest=sha256:3811144d0289aafaabf8d82459b62c5f21a0a0b41a439f472b1d3d5a861dbe45

Observation 41d0aba9-2e18-4870-9067-6a7c787f821f · outbound

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

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Advances in neural information processing systems , volume=

Reference 52

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source=arxiv_source observed=2026-08-01T17:09:59.045807Z digest=sha256:6e6a33d9e149bb95047ee2c82316931b9671de585b6fd5822edae7f89e2c4e14

Observation f3acf74e-2024-4613-9ab9-b9d4745650dc · outbound

This paper cites CoRR , volume =.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning CoRR , volume =

Reference 53

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source=arxiv_source observed=2026-08-01T17:09:59.106387Z digest=sha256:4f635a4fc3c1bf2323b5e1ff41a1ee7803a75b8d25ebb285a9f610bb00e3f867

Observation 480f40bd-8175-4398-8773-a64f323a98c5 · outbound

This paper cites ICML , year =.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning ICML , year =

Reference 54

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source=arxiv_source observed=2026-08-01T17:09:59.229452Z digest=sha256:e318d0353f3ec7fbaa23c0a4b94213e2b29922ab12772414803b377318cee82d

Observation 1681649f-a104-462b-8bc7-11be0fa1c5bb · outbound

This paper cites ArXiv , year=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning ArXiv , year=

Reference 55

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source=arxiv_source observed=2026-08-01T17:09:59.325365Z digest=sha256:85f5ab6dfa0d6d03ba4022dbd4e21cc19368f5e7e928230a3a6449b4f9f5be43

Observation 2504735c-d598-4ecc-b211-05bb61618a97 · outbound

This paper cites Proceedings of the 37th International Conference on Machine Learning , articleno =.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Proceedings of the 37th International Conference on Machine Learning , articleno =

Reference 56

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source=arxiv_source observed=2026-08-01T17:09:59.393379Z digest=sha256:20c89455163175604680f3296035e14155fcff36726161daacb910b566dc111c

Observation cb613ac1-407c-45c4-9459-5a729fd09c32 · outbound

This paper cites Proceedings of the 37th International Conference on Machine Learning , articleno =.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Proceedings of the 37th International Conference on Machine Learning , articleno =

Reference 57

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source=arxiv_source observed=2026-08-01T17:09:59.478563Z digest=sha256:8352f443351f5700dfc01e419154a9aa9b0ee0db49bbba29c42fc29632ce5276

Observation ff3f9f21-7086-4c93-acea-b5e6ccfc1d4c · outbound

This paper cites Proximal Policy Optimization Algorithms.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 58

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source=arxiv_source observed=2026-08-01T17:09:59.570679Z digest=sha256:0fdbb9a14503824293a39b27f5a49e1f881dc5e8540e706ac970732e23bc707e

Observation 10c40e40-8a22-49f3-9843-afda982a4d98 · outbound

This paper cites Conference on Robot Learning (CoRL) , year=.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning Conference on Robot Learning (CoRL) , year=

Reference 59

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source=arxiv_source observed=2026-08-01T17:09:59.692161Z digest=sha256:bd85694f7ca40fa49c52d386618d50dfeb0d4292a458fb1c200ea926e1719c75

Pith citing papers

No inbound Pith citation observations are available.