Paper Citation Record · LEDGER

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models

As of 19 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 5 inbound Pith citation observations for arXiv:2506.12822.

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

pith.paper-citation-record.v1
2506.12822 v1

Coverage vector

measured 74 of 74 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Source: arxiv_reference, observed 2026-07-04T09:59:44.448427Z

Reference resolution

74 of 74 outbound references displayed

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External citation measurements

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Outbound references

Observation 68ee3853-d796-4007-b9af-4e41c84ac977 · outbound

This paper cites write newline.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models write newline

Reference 1

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This paper cites Deep Reinforcement Learning from Policy-Dependent Human Feedback.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Deep Reinforcement Learning from Policy-Dependent Human Feedback

Reference 2

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Observation 65d87bfe-81af-4a10-a255-9fe155452616 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Constitutional AI: Harmlessness from AI Feedback

Reference 3

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This paper cites AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?

Reference 4

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This paper cites Vision-Language Models as a Source of Rewards.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Vision-Language Models as a Source of Rewards

Reference 5

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models G., Candido, S., Castro, P

Reference 6

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This paper cites K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al

Reference 7

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Observation 860e48b7-e2c2-4796-922c-af17bf59873c · outbound

This paper cites Towards human-level bimanual dexterous manipulation with reinforcement learning.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Towards human-level bimanual dexterous manipulation with reinforcement learning

Reference 8

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This paper cites F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D

Reference 9

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This paper cites Can foundation models perform zero-shot task specification for robot manipulation? In Learning for Dynamics and Control Conference (L4DC), 2022.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Can foundation models perform zero-shot task specification for robot manipulation? In Learning for Dynamics and Control Conference (L4DC), 2022

Reference 10

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models K., Joty, S., Li, B., and Bing, L

Reference 11

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This paper cites Minedojo: Building open-ended embodied agents with internet-scale knowledge.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Minedojo: Building open-ended embodied agents with internet-scale knowledge

Reference 12

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This paper cites Guided cost learning: Deep inverse optimal control via policy optimization.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Guided cost learning: Deep inverse optimal control via policy optimization

Reference 13

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This paper cites Learning robust rewards with adversarial inverse reinforcement learning.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Learning robust rewards with adversarial inverse reinforcement learning

Reference 14

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Unresolved cited work

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This paper cites Chatgpt outperforms crowd workers for text-annotation tasks.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Chatgpt outperforms crowd workers for text-annotation tasks

Reference 16

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models B., and Kambhampati, S

Reference 17

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This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 18

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Autoreward: Closed-loop reward design with large language models for autonomous driving

Reference 19

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Deep residual learning for image recognition

Reference 20

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Unresolved cited work

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models and Ermon, S

Reference 22

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Reward learning from human preferences and demonstrations in atari

Reference 23

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Reinforcement learning friendly vision-language model for minecraft

Reference 24

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Scalable deep reinforcement learning for vision-based robotic manipulation

Reference 25

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Champion-level drone racing using deep reinforcement learning

Reference 26

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This paper cites Motif: Intrinsic Motivation from Artificial Intelligence Feedback.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Motif: Intrinsic Motivation from Artificial Intelligence Feedback

Reference 27

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Unresolved cited work

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Offline reinforcement learning with implicit q-learning

Reference 29

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models M., Bullard, K., and Sadigh, D

Reference 30

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This paper cites R., Bishop, C., Hall, E., Carbune, V., Rastogi, A., et al.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models R., Bishop, C., Hall, E., Carbune, V., Rastogi, A., et al

Reference 31

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This paper cites PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training

Reference 32

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models B-pref: Benchmarking preference-based reinforcement learning

Reference 33

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This paper cites Scalable agent alignment via reward modeling: a research direction.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Scalable agent alignment via reward modeling: a research direction

Reference 34

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Nonlinear inverse reinforcement learning with gaussian processes

Reference 35

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Silkie: Preference Distillation for Large Visual Language Models

Reference 36

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models M., Stepputtis, S., Campbell, J., and Sycara, K

Reference 37

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models J., Kumar, V., Zhang, A., Bastani, O., and Jayaraman, D

Reference 38

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models J., Liang, W., Wang, G., Huang, D.-A., Bastani, O., Jayaraman, D., Zhu, Y., Fan, L., and Anandkumar, A

Reference 39

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models L., Muresan, S., Squire, S., Tellex, S., Arumugam, D., and Yang, L

Reference 40

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models K., Loftin, R., Peng, B., Wang, G., Roberts, D

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.464415Z

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This paper cites Zero-shot reward specification via grounded natural language.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Zero-shot reward specification via grounded natural language

Reference 42

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models R3m: A universal visual representation for robot manipulation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.450140Z

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models T., Burch, N., Anthony, T., et al

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.443290Z

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Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 45

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This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 46

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This paper cites Vision-language models are zero-shot reward models for reinforcement learning.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Vision-language models are zero-shot reward models for reinforcement learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.427785Z

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Observation 152e4a5f-c165-4bbf-b6ea-99fa8a4866d3 · outbound

This paper cites Alfred: A benchmark for interpreting grounded instructions for everyday tasks.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Alfred: A benchmark for interpreting grounded instructions for everyday tasks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.420356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 47344c4e-5fef-4ef5-8a8b-101afd9f329e · outbound

This paper cites Mastering the game of go without human knowledge.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Mastering the game of go without human knowledge

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.413259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fb2e8de6-62c5-4a73-b20b-371a725d6b4d · outbound

This paper cites Defining and characterizing reward gaming.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Defining and characterizing reward gaming

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.406207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bc7cffd5-1760-4a80-b29c-ed8ab87adccf · outbound

This paper cites Roboclip: One demonstration is enough to learn robot policies.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Roboclip: One demonstration is enough to learn robot policies

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.398503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bf5652b2-7b96-4a8d-b8e8-691262f6813c · outbound

This paper cites an unresolved cited work.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Unresolved cited work

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T20:11:50.073605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31100d27-f70c-4fa8-adb3-8340e9ff7d8a · outbound

This paper cites BC-IRL: Learning Generalizable Reward Functions from Demonstrations.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models BC-IRL: Learning Generalizable Reward Functions from Demonstrations

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:11:50.215316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b82791c4-b971-4804-91ea-9bb40540b6c1 · outbound

This paper cites N., Klissarov, M., Precup, D., Yang, S., and Anand, A.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models N., Klissarov, M., Precup, D., Yang, S., and Anand, A

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.388067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3501a304-cb98-4603-9b4d-5673e051e416 · outbound

This paper cites M., Mathieu, M., Dudzik, A., Chung, J., Choi, D.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models M., Mathieu, M., Dudzik, A., Chung, J., Choi, D

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.381016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 92a42e9a-0cbe-4c9a-bf1e-62977c6c1554 · outbound

This paper cites Prefclm: Enhancing preference-based reinforcement learning with crowdsourced large language models.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Prefclm: Enhancing preference-based reinforcement learning with crowdsourced large language models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.374280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 03bfdcc6-f154-466d-99a4-dde754301ab3 · outbound

This paper cites Rl-vlm-f: Reinforcement learning from vision language foundation model feedback.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Rl-vlm-f: Reinforcement learning from vision language foundation model feedback

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.367587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e0471fd0-7f57-4b2c-af2a-4befcde083e3 · outbound

This paper cites Robogen: Towards unleashing infinite data for automated robot learning via generative simulation.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Robogen: Towards unleashing infinite data for automated robot learning via generative simulation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.360775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0edab5db-26c1-4525-876f-eed1a70c5a89 · outbound

This paper cites Deep tamer: Interactive agent shaping in high-dimensional state spaces.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Deep tamer: Interactive agent shaping in high-dimensional state spaces

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.353946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3f135604-048a-4d78-9985-8e7d0b55331c · outbound

This paper cites To Smooth or Not? When Label Smoothing Meets Noisy Labels.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models To Smooth or Not? When Label Smoothing Meets Noisy Labels

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T20:11:50.094005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4722e6c-bd5d-4f97-be78-d59bd0e4c1b4 · outbound

This paper cites J., Waytowich, N., and Cao, Y.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models J., Waytowich, N., and Cao, Y

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.347047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0454f6b4-69e9-4380-8b8d-e7e885f68c85 · outbound

This paper cites A survey of preference-based reinforcement learning methods.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models A survey of preference-based reinforcement learning methods

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.340165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8cbf753c-9b97-412e-aba5-7d66e7933de1 · outbound

This paper cites Maximum Entropy Deep Inverse Reinforcement Learning.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Maximum Entropy Deep Inverse Reinforcement Learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T20:11:50.103304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f3c16bc-3931-4810-a9e1-23dff2cc7c20 · outbound

This paper cites Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.332839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6c125c0c-8ac2-4e67-8025-1f79f779982a · outbound

This paper cites H., Liu, Y., Luo, Q., Zhong, V., Yang, Y., and Yu, T.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models H., Liu, Y., Luo, Q., Zhong, V., Yang, Y., and Yu, T

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.325574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8dfe1053-c665-45b2-97a7-4efdf4604d4b · outbound

This paper cites S., Hasegawa-Johnson, M.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models S., Hasegawa-Johnson, M

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.318199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 76f92c68-e6b3-4c0e-9d94-6299c2633139 · outbound

This paper cites Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.310928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fd1e9ae3-03b0-441f-993c-9d2c3098e3ff · outbound

This paper cites Rlaif-v: Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Rlaif-v: Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T20:11:50.115163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50d0fdfb-a02a-45a9-8138-a4f0d9b62ad1 · outbound

This paper cites Uni-rlhf: Universal platform and benchmark suite for reinforcement learning with diverse human feedback.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Uni-rlhf: Universal platform and benchmark suite for reinforcement learning with diverse human feedback

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.303190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8843c7e3-8d65-444d-a5d2-8a30acfaeeab · outbound

This paper cites an unresolved cited work.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:11:50.295836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3283974b-49e9-405c-a254-8fabfc8cf2ac · outbound

This paper cites Vision-language models for vision tasks: A survey.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Vision-language models for vision tasks: A survey

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.288621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fd38fa2a-c157-4b9f-9580-a45c191aad7c · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T20:11:50.126624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15e08b51-856f-419a-bd78-e2a3c9a54d08 · outbound

This paper cites an unresolved cited work.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:11:50.280981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1c7435ff-d998-4abb-88c6-544dba34c2c7 · outbound

This paper cites D., Maas, A.

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models D., Maas, A

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:11:50.274071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 8674e6ba-bf86-4071-b0a2-26340077f810 · inbound

Occlusion-robust Stylization for Drawing-based 3D Animation cites this paper.

Occlusion-robust Stylization for Drawing-based 3D Animation Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T10:13:19.750837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b704c8c-fc4a-4656-9aa9-60b8aa3f330d · inbound

Self-Rewarding Vision-Language Model via Reasoning Decomposition cites this paper.

Self-Rewarding Vision-Language Model via Reasoning Decomposition Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-18T21:06:50.849423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0e9d3458-f541-44cc-bb1f-2ae14dbf8d0c · inbound

SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning cites this paper.

SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-13T16:10:12.689957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97bc0aea-a88f-49e0-9bdf-0e44fc281504 · inbound

Learning Process Rewards via Success Visitation Matching for Efficient RL cites this paper.

Learning Process Rewards via Success Visitation Matching for Efficient RL Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T09:59:44.450686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 38e1e6d2-6dc9-4c28-bcfd-3ae2414ab0c7 · inbound

Towards General Language-Conditioned Latent Safety Filters cites this paper.

Towards General Language-Conditioned Latent Safety Filters Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models

Reference 213

Resolution: unresolved; no resolver link, observed 2026-08-04T00:49:36.677298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.