Paper Citation Record · LEDGER

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2505.19769.

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

pith.paper-citation-record.v1
2505.19769 v2

Coverage vector

measured 55 of 55 reference resolution

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measured 1 of 1 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: arxiv_reference, observed 2026-05-21T14:10:13.290156Z

Reference resolution

55 of 55 outbound references displayed

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External citation measurements

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Outbound references

Observation 27024c23-bf04-4f95-940f-23bed71c8ed5 · outbound

This paper cites Reinforcement learning control of a flexible two-link manipulator: An experimental investiga- tion,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Reinforcement learning control of a flexible two-link manipulator: An experimental investiga- tion,

Reference 2

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Observation 1abc96b1-5307-4a78-b372-3c4875891862 · outbound

This paper cites A hierarchical deep reinforcement learning framework for 6-dof ucav air-to-air combat,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning A hierarchical deep reinforcement learning framework for 6-dof ucav air-to-air combat,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:48.786602Z

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Observation 225c0f0c-5c41-4fbc-9909-df8e35703d79 · outbound

This paper cites Conrft: A reinforced fine-tuning method for vla models via consistency policy,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Conrft: A reinforced fine-tuning method for vla models via consistency policy,

Reference 4

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Observation 14bdcf12-d5af-4640-95fb-450e6caad0a4 · outbound

This paper cites End-to-end robotic rein- forcement learning without reward engineering,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning End-to-end robotic rein- forcement learning without reward engineering,

Reference 5

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Observation 8eee8e24-f7e1-4009-b36e-a37673c1e7d2 · outbound

This paper cites Deep learning in robotics: Survey on model structures and training strategies,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Deep learning in robotics: Survey on model structures and training strategies,

Reference 6

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Observation c56853e9-357e-4fdb-9f96-d96174c16464 · outbound

This paper cites Deep reinforcement learning-based automatic exploration for navigation in unknown environment,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Deep reinforcement learning-based automatic exploration for navigation in unknown environment,

Reference 7

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Observation d46f445b-e656-431f-adc1-abe139950ee4 · outbound

This paper cites Reward design with language models,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Reward design with language models,

Reference 8

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Observation b501e6ce-83e3-458f-9aa3-add038e931a7 · outbound

This paper cites A survey of inverse reinforcement learning: Challenges, methods and progress,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning A survey of inverse reinforcement learning: Challenges, methods and progress,

Reference 9

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Observation 7da671b2-4475-4e32-99d6-66747769b72d · outbound

This paper cites Concrete Problems in AI Safety.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Concrete Problems in AI Safety

Reference 10

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Observation c3cbd773-c63e-4274-929f-098bf17c90eb · outbound

This paper cites VIP: towards universal visual reward and representation via value-implicit pre-training,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning VIP: towards universal visual reward and representation via value-implicit pre-training,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:49.073408Z

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Observation 825f694e-f6b6-4ed6-9990-55202c3d94f6 · outbound

This paper cites Robot fine-tuning made easy: Pre-training rewards and policies for autonomous real-world reinforcement learning,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Robot fine-tuning made easy: Pre-training rewards and policies for autonomous real-world reinforcement learning,

Reference 13

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Observation 596ccf72-8360-4465-a1e6-5a2919135b43 · outbound

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

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Roboclip: One demonstration is enough to learn robot policies,

Reference 14

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Observation 76f78089-5877-4bfa-9fb6-a977c136a0d6 · outbound

This paper cites RL-VLM-F: reinforcement learning from vision language foundation model feedback,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning RL-VLM-F: reinforcement learning from vision language foundation model feedback,

Reference 15

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Observation e7b7cd0a-ab1a-42a6-943f-d3d1263da7e7 · outbound

This paper cites Video diffusion models,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Video diffusion models,

Reference 16

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Observation cb591e85-a4c9-451c-bdfe-29f39d0749d3 · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Imagen Video: High Definition Video Generation with Diffusion Models

Reference 17

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Observation f5b35545-33cc-4a89-a8ef-6156b2b944a0 · outbound

This paper cites Learning universal policies via text-guided video generation,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Learning universal policies via text-guided video generation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:46.835479Z

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Observation 34755670-cc72-4d5b-96d0-f8eeb95c7bf3 · outbound

This paper cites Learning to act from actionless videos through dense correspondences,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Learning to act from actionless videos through dense correspondences,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:46.646711Z

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Observation 30648508-4028-4a03-b905-5a8c9fae517c · outbound

This paper cites Learning interactive real-world simu- lators,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Learning interactive real-world simu- lators,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:46.467102Z

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Observation 3a26a60b-4bf6-4682-a7ff-9bfef229619d · outbound

This paper cites Any-point trajectory modeling for policy learning,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Any-point trajectory modeling for policy learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:46.275066Z

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Observation e1c6dba3-f10f-4dc2-897a-232876ce1fe9 · outbound

This paper cites Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own

Reference 22

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Observation 3354cdda-7796-4340-a7eb-7ce23412e17a · outbound

This paper cites Video prediction models as rewards for reinforcement learning,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Video prediction models as rewards for reinforcement learning,

Reference 23

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Observation bcb3a6e7-552d-493a-91a3-dab15c6ec1ae · outbound

This paper cites Diffusion reward: Learning rewards via conditional video diffusion,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Diffusion reward: Learning rewards via conditional video diffusion,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:45.862965Z

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Observation 4e6c10ec-2b98-40fc-8f91-d205cd41a0c0 · outbound

This paper cites Stabilizing diffusion model for robotic control with dynamic programming and transition feasibility,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Stabilizing diffusion model for robotic control with dynamic programming and transition feasibility,

Reference 25

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Observation 1a1cc20f-eb0e-48bd-8f7f-2541f323aaa2 · outbound

This paper cites Decision making with visualizations: a cognitive framework across disciplines,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Decision making with visualizations: a cognitive framework across disciplines,

Reference 26

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Observation b90ebe25-2d59-47f3-8a65-4c6ca05b7ec9 · outbound

This paper cites MAT: Morphological adaptive trans- former for universal morphology policy learning,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning MAT: Morphological adaptive trans- former for universal morphology policy learning,

Reference 27

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Observation ed71ba69-0257-41dc-98c5-54dc3feab07a · outbound

This paper cites Boosting continuous control with consistency policy,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Boosting continuous control with consistency policy,

Reference 28

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Observation 8626712b-1b7e-47e5-888b-bd508010bb37 · outbound

This paper cites Proximal policy optimization with policy feedback,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Proximal policy optimization with policy feedback,

Reference 29

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Observation 16c2e37e-f51c-466b-aa1a-d8b76582137c · outbound

This paper cites Interactive language: Talking to robots in real time,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Interactive language: Talking to robots in real time,

Reference 30

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Observation 43fb337f-fa29-4fc4-9474-ed9ebcc4b697 · outbound

This paper cites The surprising effectiveness of representation learning for visual imitation,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning The surprising effectiveness of representation learning for visual imitation,

Reference 31

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Observation 523292ed-9b83-4171-a46b-47684c13c040 · outbound

This paper cites Watch and act: Learning robotic manipulation from visual demonstration,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Watch and act: Learning robotic manipulation from visual demonstration,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:44.200187Z

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Observation b15d748d-8a0a-425d-8dbe-0eefc7f996d5 · outbound

This paper cites Apprenticeship learning via inverse reinforce- ment learning,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Apprenticeship learning via inverse reinforce- ment learning,

Reference 33

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Observation e410135c-a395-471f-80fa-cc2e501c6dd1 · outbound

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

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Guided cost learning: Deep inverse optimal control via policy optimization,

Reference 34

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Observation 8a3aab42-5c1e-403f-b2e3-1050e03c649f · outbound

This paper cites XIRL: cross-embodiment inverse reinforcement learning,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning XIRL: cross-embodiment inverse reinforcement learning,

Reference 35

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Observation 208e572a-6a09-43ea-a329-27ac0f909880 · outbound

This paper cites Unsupervised perceptual rewards for imitation learning,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Unsupervised perceptual rewards for imitation learning,

Reference 36

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Observation f8b2f30f-c960-40f3-befa-a3bfe6ce27b6 · outbound

This paper cites Learning generalizable robotic reward functions from.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Learning generalizable robotic reward functions from

Reference 37

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Observation 594138cd-4e9a-40e8-b77a-858a9245ce68 · outbound

This paper cites Can pre-trained text-to-image models generate visual goals for reinforcement learning?.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Can pre-trained text-to-image models generate visual goals for reinforcement learning?

Reference 38

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Observation 90b5adef-e3bb-4709-9b87-4e6f5eaaddad · outbound

This paper cites Language instructed reinforcement learning for human-ai coordination,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Language instructed reinforcement learning for human-ai coordination,

Reference 39

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Observation 4bc8b726-a2ce-4e53-a4b9-3fad6a2b9833 · outbound

This paper cites Language to rewards for robotic skill synthesis,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Language to rewards for robotic skill synthesis,

Reference 40

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Observation 158d2f3b-1ed7-4a32-9688-514c3968db6f · outbound

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

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Robogen: Towards unleashing infinite data for automated robot learning via generative simulation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:42.579438Z

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Observation 3b267ee1-508a-4082-a601-92184463a9b2 · outbound

This paper cites Eureka: Human-level reward design via coding large language models,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Eureka: Human-level reward design via coding large language models,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:42.509950Z

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Observation 31f54546-38a7-49ca-9b49-c7675c8da5d8 · outbound

This paper cites Vision-language models as success detectors,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Vision-language models as success detectors,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:42.404777Z

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Observation 7c2d1506-22b4-4b15-9929-f653089ed579 · outbound

This paper cites Guiding pretraining in reinforcement learning with large language models,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Guiding pretraining in reinforcement learning with large language models,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:42.230637Z

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Observation aafd8bb6-9e97-4c32-a0d7-c954245f4438 · outbound

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

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Vision-language models are zero-shot reward models for reinforcement learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:42.156413Z

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Observation 9f6e7692-a622-497e-b82a-dc2409268921 · outbound

This paper cites LIV: language-image representations and rewards for robotic control,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning LIV: language-image representations and rewards for robotic control,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:42.046295Z

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Observation 9511828c-f62d-4486-9488-6da528c69c60 · outbound

This paper cites Llmscenario: Large language model driven scenario generation,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Llmscenario: Large language model driven scenario generation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:41.759632Z

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Observation 1ae77ca1-1927-4e32-9360-ba9b95853b56 · outbound

This paper cites Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models

Reference 48

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Observation d21e32e1-5bce-4843-90c4-0721f22ba25d · outbound

This paper cites Denoising diffusion implicit models,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Denoising diffusion implicit models,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:41.510138Z

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Observation ccc18db5-19c4-420e-924e-28ccc8f14de0 · outbound

This paper cites Taming transformers for high- resolution image synthesis,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Taming transformers for high- resolution image synthesis,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:41.358889Z

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Observation fb8dedd4-20e4-4fdc-8dba-e18dfc216d01 · outbound

This paper cites Exploration by random network distillation,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Exploration by random network distillation,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:41.189959Z

Source-reported events for the cited work

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Observation e74a59dc-86ef-468c-b20e-0455c97b71e1 · outbound

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

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:40.969274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0d2eb78e-a383-4876-ad14-476908a4ada9 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Learning transferable visual models from natural language supervi- sion,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:47.571243Z

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Observation d859a0c4-18ee-4edd-a98c-5bd354ebe924 · outbound

This paper cites Mastering visual continuous control: Improved data-augmented reinforcement learning,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Mastering visual continuous control: Improved data-augmented reinforcement learning,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:40.768703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 317c1489-65bc-4a26-9f5b-844fd14b6e39 · outbound

This paper cites Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T14:12:39.869877Z

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Unavailable: canonical work link unavailable.

Observation 83a06b74-1583-4d1f-9d23-e07fc8b507cc · outbound

This paper cites Con- trolvideo: Training-free controllable text-to-video generation,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Con- trolvideo: Training-free controllable text-to-video generation,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:40.468780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 242fe782-a447-48be-b781-d5e518ed0b1d · outbound

This paper cites Ldr: Learning discrete representa- tion to improve noise robustness in multiagent tasks,.

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning Ldr: Learning discrete representa- tion to improve noise robustness in multiagent tasks,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:12:40.214633Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 8c6c6ecb-c957-4102-8fde-ac21f52f0c8e · inbound

Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning cites this paper.

Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:10:13.292099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.