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Paper Citation Record · LEDGER

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

As of 11 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 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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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=sha256:800d7b1e4b34c55ce22304e569f99b006be729972f6054270d9a6c2e034d1aec

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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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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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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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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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=

Reference 22

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

Reference 38

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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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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=

Reference 40

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source=arxiv_source observed=2026-08-01T17:09:58.086878Z digest=sha256:0b4e5b4c91854ae6134d2cd93587a62f96412e421e0f26b141a001523b155901

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:baa6cdf376534a13ba87f6a76418212bef48a8d939cd0adc37a342c6f31ed80b

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:eb6acd00fdb0ba61220195415b8057093e85d2f977e9f2575296ac50aa2cb60f

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:f780c17a9f5b7217f05fb7c2fb501c98d3d2937f5101baf5bcf3c295db66cf64

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:d9355f30610efed77e1e906f206a1edc0479cb78dd784e5909c5777fc163fd2e

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=sha256:547d01f0a50133ddcb7e14c772effd3b49ad879a475486c93ebe7ce408e201ef

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=sha256:9f054ddee7af95e5a34ecd242e6473a8bceca7fcd9e297d4240dd8a1c01c44dc

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:af971b1d861b8f68784b83f95795a4198acc1cc2785a065f9f50842fd96ab55e

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=sha256:6b2cd977ed95bfbc828b9d8f60cf2811965a04b7ce6231b9dec5c7115f20019c

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:fc804b4189c03325bfa2e764d6d47286e4853a5578651f7d029d8e29758f60b2

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:c30d2c03ae02c36be47a295d7d9bf32c1e94d9d838850165382367bb1f223599

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=sha256:793cabf369ac096764535f9ad54f304ec7f62511b8c39f3340aa90e4d73c75ad

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=sha256:940f5b071f4256456fabade1367e32ee049b9b1e51496ece5c137b0cba474245

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:a9244b5bc99c31e967ff58eb9314090ef2267ecd4320ec7ae56110a389bcb596

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:f8565a8fc601f63d7f27b8abf7bd8a61d227e91a5845d4f69a2d3e0ad71c7532

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:a0294965e9e5b4513788b7cf8e38c2eb32501a98033e3fd4a568ec6469778858

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=sha256:72181c9e5106051d9ac2fd9f4dd9babb5b03ceca2e53b18544f699812973195b

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=sha256:166e955e8375d5abad675e233a9a7646ea2ad01ccff1f35a8f471478a8b33c1a

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=sha256:4a2986e988c9e2dda33962bd8986015ebf1d2add5dcf6533753bb38fd487c095

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=sha256:9fcd285da1291b59b0242b62e7e4bb6fdb7026ceaf7caac3f80441502ec2e378

Pith citing papers

No inbound Pith citation observations are available.