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Paper Citation Record · LEDGER

Reinforcement Learning: From Algorithms To Foundation Models

As of 23 August 2026, this Paper Citation Record lists 100 of 300 outbound references and 0 inbound Pith citation observations for arXiv:2607.17560.

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

pith.paper-citation-record.v1
2607.17560 v1

Coverage vector

measured 100 of 300 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T17:45:05.707426Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

100 of 300 outbound references displayed

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External citation measurements

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Outbound references

Observation c0a00d9f-6fd9-4bbe-b802-da0aeef250bb · outbound

This paper cites Neurocomputing , volume=.

Reinforcement Learning: From Algorithms To Foundation Models Neurocomputing , volume=

Reference 1

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Observation b870e278-e208-4083-b0f4-59d0d018cbc4 · outbound

This paper cites Recurrent Autoregressive Diffusion: Global Memory Meets Local Attention.

Reinforcement Learning: From Algorithms To Foundation Models Recurrent Autoregressive Diffusion: Global Memory Meets Local Attention

Reference 2

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Observation 63b50bcd-1362-4301-b189-b20377cd82ea · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models International Conference on Machine Learning , pages=

Reference 3

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

Reinforcement Learning: From Algorithms To Foundation Models Advances in neural information processing systems , pages=

Reference 4

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

Reinforcement Learning: From Algorithms To Foundation Models Advances in neural information processing systems , volume=

Reference 5

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Observation 9b76066b-76a6-4b7a-a57d-8323fbf0896f · outbound

This paper cites Mastering the game of Go without human knowledge , volume =.

Reinforcement Learning: From Algorithms To Foundation Models Mastering the game of Go without human knowledge , volume =

Reference 6

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

Reinforcement Learning: From Algorithms To Foundation Models International Conference on Learning Representations , year=

Reference 7

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Reinforcement Learning: From Algorithms To Foundation Models , title =

Reference 8

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Reinforcement Learning: From Algorithms To Foundation Models 2025 , url=

Reference 9

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Observation edaf7c8a-d22d-4669-8187-be1b461e815d · outbound

This paper cites ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model.

Reinforcement Learning: From Algorithms To Foundation Models ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model

Reference 10

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Observation c63ca2c6-6eea-4377-a2a0-1d4f5b88b8e2 · outbound

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Reinforcement Learning: From Algorithms To Foundation Models 2025 , eprint=

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Observation 24a890dd-0a68-407a-be65-fe06b65ea4ae · outbound

This paper cites How Far is Video Generation from World Model: A Physical Law Perspective.

Reinforcement Learning: From Algorithms To Foundation Models How Far is Video Generation from World Model: A Physical Law Perspective

Reference 12

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Observation 706d17ac-89a3-4f87-ac4e-5526b101c973 · outbound

This paper cites CAT3D: Create Anything in 3D with Multi-View Diffusion Models.

Reinforcement Learning: From Algorithms To Foundation Models CAT3D: Create Anything in 3D with Multi-View Diffusion Models

Reference 13

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Reinforcement Learning: From Algorithms To Foundation Models 2025 , eprint=

Reference 14

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Observation 70445cbf-937f-4a73-8ba4-149320fddcaa · outbound

This paper cites TesserAct: Learning 4D Embodied World Models.

Reinforcement Learning: From Algorithms To Foundation Models TesserAct: Learning 4D Embodied World Models

Reference 15

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Observation 54611844-c5c2-4784-baa9-9e37a057de8a · outbound

This paper cites StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text.

Reinforcement Learning: From Algorithms To Foundation Models StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text

Reference 16

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Observation a928d014-8f9e-4704-9ac5-f7458837cecd · outbound

This paper cites GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control.

Reinforcement Learning: From Algorithms To Foundation Models GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control

Reference 17

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Observation c5a0d21a-f785-4d6c-b570-904250ce32c4 · outbound

This paper cites Aether: Geometric-Aware Unified World Modeling.

Reinforcement Learning: From Algorithms To Foundation Models Aether: Geometric-Aware Unified World Modeling

Reference 18

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Observation 2d5efb82-6d67-4d94-aa17-e3c39a1c6d8d · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models Advances in neural information processing systems , volume=

Reference 19

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Observation 237db79e-5381-4b0f-8e05-fa4c4b7a7bcc · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 20

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Observation 11582683-41a2-491f-b1cd-0643e6dc3e21 · outbound

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Reinforcement Learning: From Algorithms To Foundation Models GPT-4 Technical Report

Reference 21

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Observation 81b32478-8614-421f-aa6f-70379b2eef48 · outbound

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Reinforcement Learning: From Algorithms To Foundation Models Reward Guided Latent Consistency Distillation

Reference 22

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Observation 28c3cdee-c7e9-4157-a7e3-5d61323d92eb · outbound

This paper cites The Vendi Score: A Diversity Evaluation Metric for Machine Learning.

Reinforcement Learning: From Algorithms To Foundation Models The Vendi Score: A Diversity Evaluation Metric for Machine Learning

Reference 23

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Observation 8e7952d9-8bf9-4d48-9fb6-79da13f6e355 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Reinforcement Learning: From Algorithms To Foundation Models Classifier-Free Diffusion Guidance

Reference 24

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Observation 5153650f-cd84-4874-a757-251c177ed9b2 · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

Reinforcement Learning: From Algorithms To Foundation Models CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 25

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Observation 8d665ceb-56fd-410e-8ca3-06663233d0e7 · outbound

This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

Reinforcement Learning: From Algorithms To Foundation Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 26

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Observation 9c61bbb2-ba47-46e8-b893-8fcc0d79266d · outbound

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Reinforcement Learning: From Algorithms To Foundation Models Movie Gen: A Cast of Media Foundation Models

Reference 27

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Observation 2d55b8a4-80d4-4941-be35-fb2a9aa35aea · outbound

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Reinforcement Learning: From Algorithms To Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 28

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Observation a346701a-1427-4467-94a4-0992f343157d · outbound

This paper cites Diffusion Policy Policy Optimization.

Reinforcement Learning: From Algorithms To Foundation Models Diffusion Policy Policy Optimization

Reference 29

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Observation 85bb7192-fcc0-4678-bb0c-efd03a928a0a · outbound

This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

Reinforcement Learning: From Algorithms To Foundation Models Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 30

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Reinforcement Learning: From Algorithms To Foundation Models Diffusion Posterior Sampling for General Noisy Inverse Problems

Reference 31

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Reinforcement Learning: From Algorithms To Foundation Models arXiv preprint arXiv:2410.05954 , year=

Reference 32

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Observation b08892b1-ff07-48fd-9774-82f4e73389fc · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

Reinforcement Learning: From Algorithms To Foundation Models Training Diffusion Models with Reinforcement Learning

Reference 33

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Reinforcement Learning: From Algorithms To Foundation Models 2024 , howpublished =

Reference 34

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Reinforcement Learning: From Algorithms To Foundation Models DreamFusion: Text-to-3D using 2D Diffusion

Reference 35

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Reinforcement Learning: From Algorithms To Foundation Models Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 36

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Reinforcement Learning: From Algorithms To Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 37

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Observation 288d7957-8278-4475-8855-597c75481abf · outbound

This paper cites Video Diffusion Alignment via Reward Gradients.

Reinforcement Learning: From Algorithms To Foundation Models Video Diffusion Alignment via Reward Gradients

Reference 38

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Reinforcement Learning: From Algorithms To Foundation Models The Twelfth International Conference on Learning Representations , year=

Reference 39

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source=arxiv_source observed=2026-08-01T17:44:58.918052Z digest=sha256:a260f82bc6bd59b9715d8e5210cd5d0963b190cbf9fd89d17d4bfdaf483171c6

Observation 4d21d48a-ac7a-4ef5-9701-39b3f5c00842 · outbound

This paper cites Lumiere: A Space-Time Diffusion Model for Video Generation.

Reinforcement Learning: From Algorithms To Foundation Models Lumiere: A Space-Time Diffusion Model for Video Generation

Reference 40

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source=arxiv_source observed=2026-08-01T17:44:58.985826Z digest=sha256:f41bd55c64bcc08fcb63a6505a29bb727307e07fefbe385271a62ac62cd5cfcf

Observation fedb1410-0de4-490d-a5d9-e5097e0994b4 · outbound

This paper cites LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models.

Reinforcement Learning: From Algorithms To Foundation Models LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models

Reference 41

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source=arxiv_source observed=2026-08-01T17:44:59.146474Z digest=sha256:dc9429b5e246cf9e0d0a5e598501430660b05e14ccf7c0becceac20154964801

Observation f7db3b5b-c555-4a3f-b4fd-9722c38b540b · outbound

This paper cites Model compression via distillation and quantization.

Reinforcement Learning: From Algorithms To Foundation Models Model compression via distillation and quantization

Reference 42

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source=arxiv_source observed=2026-08-01T17:44:59.286963Z digest=sha256:f7cade3333dc9c153ce54e982718a5c36eacad79810bf2ccb23c7182f86a4f43

Observation 438f91ba-166a-46b0-bee6-162edc9325ba · outbound

This paper cites Auto-Encoding Variational Bayes.

Reinforcement Learning: From Algorithms To Foundation Models Auto-Encoding Variational Bayes

Reference 43

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source=arxiv_source observed=2026-08-01T17:44:59.431823Z digest=sha256:edb4e7f70c785f3d09b288c4048f8c068841f6fb9037fd3f0c55e8e57ec85a24

Observation 7e947831-4817-4b48-b3cb-66f6ba62ce45 · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models Advances in neural information processing systems , volume=

Reference 44

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source=arxiv_source observed=2026-08-01T17:44:59.536543Z digest=sha256:e2e59de104028992f47d0882f9d2068685f923ba876b9defd93bb5a8a1a84204

Observation a920633f-dddd-4c0b-92be-50cd1f89d993 · outbound

This paper cites SF-V: Single Forward Video Generation Model.

Reinforcement Learning: From Algorithms To Foundation Models SF-V: Single Forward Video Generation Model

Reference 45

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source=arxiv_source observed=2026-08-01T17:44:59.614556Z digest=sha256:c3fab2b1be5d0229af4a7a41f0ffcf0e58e8f550a6b7181652d4166fe1125f85

Observation 00b0ad3f-bfac-4e01-bfa1-a8ba076d06c1 · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 46

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source=arxiv_source observed=2026-08-01T17:44:59.701593Z digest=sha256:217fe095fb8e4aa016c114847fd1bffc1c83e62dc00ab7949359f603b0932932

Observation e5cb8752-8037-4a28-8650-591ebab88071 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

Reinforcement Learning: From Algorithms To Foundation Models Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 47

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source=arxiv_source observed=2026-08-01T17:44:59.778901Z digest=sha256:dfb32bb29b99729660b234574d99f94d15889a47f6930c8f9747754cbc336e2d

Observation 445b7770-3047-44e0-b01f-d6216e290d6e · outbound

This paper cites 2024 , url =.

Reinforcement Learning: From Algorithms To Foundation Models 2024 , url =

Reference 48

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source=arxiv_source observed=2026-08-01T17:44:59.858338Z digest=sha256:48f84e0bc7814541779ddc3f17c13c408fdb32976917b3a9648b5830800523c2

Observation 595be110-e365-4719-8bb5-b14246a8ed2e · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 49

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source=arxiv_source observed=2026-08-01T17:45:00.014478Z digest=sha256:e9a13243304e36aa73b90edee8a8d3c6d64b71a2f9fb9bc93aa212b7a2ed47ca

Observation ec7b007f-9e6e-44b5-b0ea-1ca71256ebac · outbound

This paper cites Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control.

Reinforcement Learning: From Algorithms To Foundation Models Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control

Reference 50

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source=arxiv_source observed=2026-08-01T17:45:00.086809Z digest=sha256:d8c7b5e2a47f057d7ce178628cb9f268e259f0d3b8d0268ff2550575a74b6dfa

Observation e792df1e-5c6f-43dd-989e-cc68d380bd2d · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models International Conference on Learning Representations , year=

Reference 51

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source=arxiv_source observed=2026-08-01T17:45:00.256789Z digest=sha256:434bd8c77e3ced33fbe355a560a6f8709be3aa306f0877c4f3d6e332f735ba32

Observation 7b73ba5c-815a-4356-bc9a-16ee66006ae8 · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models International Conference on Machine Learning , pages=

Reference 52

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source=arxiv_source observed=2026-08-01T17:45:00.389116Z digest=sha256:edf6990592b771a5643f9860342105e3a941a16adf691511556c5069c7f4d9a4

Observation a569d30e-5864-4bff-9021-318c6741f642 · outbound

This paper cites Video Diffusion Models.

Reinforcement Learning: From Algorithms To Foundation Models Video Diffusion Models

Reference 53

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source=arxiv_source observed=2026-08-01T17:45:00.517806Z digest=sha256:1cd4131d4c823618e1643de2a01e7d5bf52135ee4c0973ea34b6d203dd44b10c

Observation 5aab833e-2032-4249-a53c-d79fe921b3fb · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models Imagen Video: High Definition Video Generation with Diffusion Models

Reference 54

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source=arxiv_source observed=2026-08-01T17:45:00.614135Z digest=sha256:067254d667739459e78d080df3a6f13df0bc7c1450a3878a76bca79ff9befd19

Observation b5c126bf-825f-4ba3-9157-b5e08e49ebf1 · outbound

This paper cites Flexible Diffusion Modeling of Long Videos.

Reinforcement Learning: From Algorithms To Foundation Models Flexible Diffusion Modeling of Long Videos

Reference 55

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source=arxiv_source observed=2026-08-01T17:45:00.724789Z digest=sha256:c73991cbbb72fafefdcea10820eb845c6edff1b9767f4314b4854ed04343a1e7

Observation 6662caa6-708a-44db-99c8-e9d3f65da8b8 · outbound

This paper cites Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer.

Reinforcement Learning: From Algorithms To Foundation Models Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer

Reference 56

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source=arxiv_source observed=2026-08-01T17:45:00.981903Z digest=sha256:c702b5ed3fc6efddb35065fb2d10cd58cadd463422cc82eefa1b925dffaa643b

Observation 9b876f10-e446-41f2-a2c5-317c5b27cf97 · outbound

This paper cites Machine Learning for LiDAR-Based Navigation System.

Reinforcement Learning: From Algorithms To Foundation Models Machine Learning for LiDAR-Based Navigation System

Reference 57

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source=arxiv_source observed=2026-08-01T17:45:01.175869Z digest=sha256:c4822e1bded7c55eac0b1d25b052213709f5f33f61fa06544089c341f1ab4e06

Observation 0fb29b42-b429-4ab8-ac95-96c3d9af3089 · outbound

This paper cites Make-A-Video: Text-to-Video Generation without Text-Video Data.

Reinforcement Learning: From Algorithms To Foundation Models Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 58

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source=arxiv_source observed=2026-08-01T17:45:01.269896Z digest=sha256:36f8b1a7f9dc96fac8ed65310fd2736f16cbf1f2d25d76751cba8e402271e267

Observation e88fb795-6692-4acb-9737-08acdf07829a · outbound

This paper cites Phenaki: Variable Length Video Generation From Open Domain Textual Description.

Reinforcement Learning: From Algorithms To Foundation Models Phenaki: Variable Length Video Generation From Open Domain Textual Description

Reference 59

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source=arxiv_source observed=2026-08-01T17:45:01.436983Z digest=sha256:1884b125bc4a113f59f02c8593d620f074a49f7587e47b3332ae78962797ffaa

Observation 12f65ec7-5dcb-4065-b90d-4e7010ec2c5b · outbound

This paper cites VideoGPT: Video Generation using VQ-VAE and Transformers.

Reinforcement Learning: From Algorithms To Foundation Models VideoGPT: Video Generation using VQ-VAE and Transformers

Reference 60

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source=arxiv_source observed=2026-08-01T17:45:01.626955Z digest=sha256:9fbaabe51da43f152073c7cd08b036d8794f44cf2f3a0c20c04b077a56781703

Observation 7a119ddd-421c-4516-ae60-3d3070e2efba · outbound

This paper cites Asymptotically mean value harmonic functions in sub-Riemannian and RCD settings.

Reinforcement Learning: From Algorithms To Foundation Models Asymptotically mean value harmonic functions in sub-Riemannian and RCD settings

Reference 61

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source=arxiv_source observed=2026-08-01T17:45:01.769399Z digest=sha256:1f70945145ccf7d1182b65957a5460c2a34c36ee97f1a779a11edc6462bcbaf7

Observation 4ab330d3-c356-43ef-8b2c-5f1aa027f95e · outbound

This paper cites CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers.

Reinforcement Learning: From Algorithms To Foundation Models CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 62

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source=arxiv_source observed=2026-08-01T17:45:01.994074Z digest=sha256:ae841be8a375bedd9d0ac7905693d1ae591b276555ab2463ca39d1ae982a4607

Observation f07e34a7-056f-4f6f-b77a-5e6bf2515efc · outbound

This paper cites Dual PatchNorm.

Reinforcement Learning: From Algorithms To Foundation Models Dual PatchNorm

Reference 63

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source=arxiv_source observed=2026-08-01T17:45:02.139610Z digest=sha256:755d42b15e587e7b30cd6ce9021a513a65df1bbb775f50de93ddf74657251696

Observation 81487659-7576-44ac-a1cf-e5b6e1468096 · outbound

This paper cites ModelScope Text-to-Video Technical Report.

Reinforcement Learning: From Algorithms To Foundation Models ModelScope Text-to-Video Technical Report

Reference 64

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source=arxiv_source observed=2026-08-01T17:45:02.235216Z digest=sha256:7b647d03dd3ca582010a71abf4ec00a063d21b1e63ecfa97fa15f2543711d056

Observation 96dd2668-cb71-4196-b67d-9df7b4af1397 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

Reinforcement Learning: From Algorithms To Foundation Models Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 65

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source=arxiv_source observed=2026-08-01T17:45:02.317738Z digest=sha256:5e6f48e87e481e26591b6d1785046d3d94162d1dc3ee0321b210b14dcd11de57

Observation e3cd2a2f-317a-4085-b278-6ef6c4bde939 · outbound

This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

Reinforcement Learning: From Algorithms To Foundation Models Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 66

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source=arxiv_source observed=2026-08-01T17:45:02.397187Z digest=sha256:a6c9053071c44f3f0f1070bcd396e3b28f27d9d946fbb129f68960a7f6e905d8

Observation cd038472-1e2b-4d7b-981c-bada2819372e · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

Reinforcement Learning: From Algorithms To Foundation Models Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 67

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source=arxiv_source observed=2026-08-01T17:45:02.455811Z digest=sha256:5546ae09c3e8b07ab1c1e33aa4e86863b4384cf84a540c3464dbeacc59132576

Observation 98f58f59-9dc7-4952-bc87-4bec0da9dc6d · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Reinforcement Learning: From Algorithms To Foundation Models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 68

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source=arxiv_source observed=2026-08-01T17:45:02.544426Z digest=sha256:e09c3f3263e16d6f5a0e94919a5ef3a4e3127ababf1c1d25f6e47c86c002330c

Observation 2bafc43c-fa13-4fc9-a5e4-bbdd13ab08ee · outbound

This paper cites 2024 , archivePrefix=.

Reinforcement Learning: From Algorithms To Foundation Models 2024 , archivePrefix=

Reference 69

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source=arxiv_source observed=2026-08-01T17:45:02.552169Z digest=sha256:ce9c4fc0997ce95f0a0f105d1825bca80f2cf1881eaa1b2fc90035f959144631

Observation 31d300ca-1f11-47c4-9f8c-d1a03cf60a75 · outbound

This paper cites 2025 , eprint=.

Reinforcement Learning: From Algorithms To Foundation Models 2025 , eprint=

Reference 70

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source=arxiv_source observed=2026-08-01T17:45:02.556548Z digest=sha256:977cfd2ece9a14ede90cf5595d95b867c55b7722e53c3b32f0d19dbbfd103c5e

Observation 46b40b90-817b-4adf-8dc7-1b2d3df63f87 · outbound

This paper cites Forty-first international conference on machine learning , year=.

Reinforcement Learning: From Algorithms To Foundation Models Forty-first international conference on machine learning , year=

Reference 71

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source=arxiv_source observed=2026-08-01T17:45:02.694086Z digest=sha256:13b2304e52f3082466a668f35b95e8a5f2fbebf49077cdcbe9a184d7cc552759

Observation 7f957b20-d95c-4eae-bb15-aebb3cce938c · outbound

This paper cites Flow Matching for Generative Modeling.

Reinforcement Learning: From Algorithms To Foundation Models Flow Matching for Generative Modeling

Reference 72

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source=arxiv_source observed=2026-08-01T17:45:02.843870Z digest=sha256:df5fd6b82c20e2b871a95e1f48c8655a14c7a00691a9e1dd5c3e47610c819d58

Observation 26e90bc9-99f9-4aa6-bfd7-6e31931a786a · outbound

This paper cites AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data.

Reinforcement Learning: From Algorithms To Foundation Models AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data

Reference 73

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source=arxiv_source observed=2026-08-01T17:45:02.946775Z digest=sha256:b900e742890322c5ecf5543d7b786c74e254620cc757050d76fc2f0ad03c13b4

Observation 1739d006-cbf8-4018-8af3-4ceb3f0177e9 · outbound

This paper cites VideoLCM: Video Latent Consistency Model.

Reinforcement Learning: From Algorithms To Foundation Models VideoLCM: Video Latent Consistency Model

Reference 74

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source=arxiv_source observed=2026-08-01T17:45:03.052709Z digest=sha256:a42545c33d71090469bedcbc2803ab7fee74fe8052d4a1c21790465b601d94da

Observation cbe0bf94-1414-40f6-8572-2abb37004938 · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models International Conference on Learning Representations , year=

Reference 75

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source=arxiv_source observed=2026-08-01T17:45:03.133782Z digest=sha256:b2cabec2d860d3de8f7d306268892cd2d4005f5abf6d3614ba9659985fe7f518

Observation 180823f8-7bf7-4b9b-905a-ea3551adabd1 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Reinforcement Learning: From Algorithms To Foundation Models Progressive Distillation for Fast Sampling of Diffusion Models

Reference 76

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source=arxiv_source observed=2026-08-01T17:45:03.209255Z digest=sha256:d2686bd3880e5eabd09d308a6c369a9a71ea2597ea6347aa8ce046d842b69ad0

Observation 1c4256d7-3394-4d0a-82ba-572592b37521 · outbound

This paper cites Knowledge distillation: A good teacher is patient and consistent.

Reinforcement Learning: From Algorithms To Foundation Models Knowledge distillation: A good teacher is patient and consistent

Reference 77

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source=arxiv_source observed=2026-08-01T17:45:03.435462Z digest=sha256:3246f96e34df66d31f43cac84cf229f8293326851027a18cc2073dd2b9ffe734

Observation e25bb162-5f66-466e-9980-602bbece1269 · outbound

This paper cites Directly Fine-Tuning Diffusion Models on Differentiable Rewards.

Reinforcement Learning: From Algorithms To Foundation Models Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 78

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source=arxiv_source observed=2026-08-01T17:45:03.551916Z digest=sha256:999d6aaef4c44b370798df4164e0e46b566d1d50a2a0f51330347dbe9e56729e

Observation cfc3cdfe-0e33-4146-a930-9a8677d50b28 · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 79

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source=arxiv_source observed=2026-08-01T17:45:03.700518Z digest=sha256:ed81c11971d8c06c09bea0228cb10051d4c5161099250d1e32aec2d2fa9b124e

Observation c8145e81-49ad-4ef6-b877-ed4bf15304c3 · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 80

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source=arxiv_source observed=2026-08-01T17:45:03.846107Z digest=sha256:b73f369f058c9fd77f968ef5866527729e35b86960581c780caf501e3223598c

Observation 885445ab-eacf-483f-a953-47177b84ab8c · outbound

This paper cites InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation.

Reinforcement Learning: From Algorithms To Foundation Models InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

Reference 81

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:04.006638Z digest=sha256:eb1984ee296451ec0affb2d24cb768cb5121cd6c3572a6368faf9c9d8f751287

Observation a49cb5eb-3175-49f8-baef-8c2d6a57e06e · outbound

This paper cites VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation.

Reinforcement Learning: From Algorithms To Foundation Models VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation

Reference 82

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no resolver link, observed 2026-08-01T17:45:04.144003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T17:45:04.144003Z digest=sha256:6875dfce4eeded36e17536b4960d0116c42637b9cb9d070451fc57478cd0051d

Observation f7116048-9b60-4ee5-b882-43947acb843a · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

Reinforcement Learning: From Algorithms To Foundation Models Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 83

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no resolver link, observed 2026-08-01T17:45:04.207411Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:04.207411Z digest=sha256:288dce9d1751e50385cd7d01bdc1eccc0120f15c4a6b2e772be893f7a6ac8a65

Observation a5be6167-3f15-4bf3-8fd8-5b7bbee536d4 · outbound

This paper cites InternVideo2: Scaling Foundation Models for Multimodal Video Understanding.

Reinforcement Learning: From Algorithms To Foundation Models InternVideo2: Scaling Foundation Models for Multimodal Video Understanding

Reference 84

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source=arxiv_source observed=2026-08-01T17:45:04.287534Z digest=sha256:df41decadafcae3ec66d43c93db9431e6c8539c6281aa93bb323d2a73b0d3af8

Observation 66986533-3a33-48ab-a086-83e02aa0d490 · outbound

This paper cites Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=.

Reinforcement Learning: From Algorithms To Foundation Models Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

Reference 85

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source=arxiv_source observed=2026-08-01T17:45:04.365988Z digest=sha256:a8a6fbac81e2c64ed8089497b7f050efae809313096b80f0e7bc375d7cef165e

Observation c22e993c-acc6-4759-a541-70aa26d76ee9 · outbound

This paper cites Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=.

Reinforcement Learning: From Algorithms To Foundation Models Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

Reference 86

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:04.447386Z digest=sha256:83789de210fcbbc115ad1a2d40b90c7b220f82da4e9cc7cb8e6fd4a134e14787

Observation 2fdae58e-14a3-4a97-b4e4-d84f4bea920d · outbound

This paper cites SIGGRAPH Asia 2024 Conference Papers , pages=.

Reinforcement Learning: From Algorithms To Foundation Models SIGGRAPH Asia 2024 Conference Papers , pages=

Reference 87

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:04.538237Z digest=sha256:88756f8709942e913e17ae07be55c85290c31ae932ab182faa94c5af005d9aea

Observation a982b9b2-cb7d-4cec-9ff7-24c5386dfed3 · outbound

This paper cites European Conference on Computer Vision , pages=.

Reinforcement Learning: From Algorithms To Foundation Models European Conference on Computer Vision , pages=

Reference 88

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:04.599126Z digest=sha256:bc959a75bc6b831279fefeab02084d4b15c21030fec20c47dcfcb75aabaaaf5a

Observation 9714246d-8506-4c44-867d-638f3bbd9dce · outbound

This paper cites T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback.

Reinforcement Learning: From Algorithms To Foundation Models T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback

Reference 89

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no resolver link, observed 2026-08-01T17:45:04.688549Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:04.688549Z digest=sha256:01478a8c548b578b397e8b86ed34c85dabb0370ed715c386a4806c88b0c4ab02

Observation 3f73a034-58a8-4b2f-a8e0-f744360b7508 · outbound

This paper cites arXiv preprint arXiv:2410.05677 , year=.

Reinforcement Learning: From Algorithms To Foundation Models arXiv preprint arXiv:2410.05677 , year=

Reference 90

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:04.771852Z digest=sha256:caaef9287d92455a61df13b519f4f321ac18a60762c9d657563f7aec430bf9d4

Observation 44ac57d1-b534-4e71-b4ad-8865d7650ee0 · outbound

This paper cites Improved Distribution Matching Distillation for Fast Image Synthesis.

Reinforcement Learning: From Algorithms To Foundation Models Improved Distribution Matching Distillation for Fast Image Synthesis

Reference 91

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:04.850920Z digest=sha256:4c0b16f53b88a36247ec9289f88755419cef27a56e5db98b8ffc28715e6a0069

Observation f7d0a9c0-3534-4d8d-b9aa-25da9c292dcf · outbound

This paper cites Fast Video Generation with Sliding Tile Attention.

Reinforcement Learning: From Algorithms To Foundation Models Fast Video Generation with Sliding Tile Attention

Reference 92

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:04.926134Z digest=sha256:83435a77769d43ba516a40910f1ad3b98ff166f543c5e30f7d9871fa7063e485

Observation 43a3c6f9-7dc3-40c4-bf61-dcf2d81d2dba · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

Reinforcement Learning: From Algorithms To Foundation Models Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 93

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:04.984308Z digest=sha256:5702c977f736ce613f4c6938e9adc51f35362a9ff72b66bd397ddade3c9439b1

Observation 4b3bd8df-17fb-4530-a197-33c541c6eaad · outbound

This paper cites Tackling the Generative Learning Trilemma with Denoising Diffusion GANs.

Reinforcement Learning: From Algorithms To Foundation Models Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 94

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:05.050814Z digest=sha256:291c25d9794ff7f9bb154f9408521f8ca021d8506790f82da7c6c4f1688460f8

Observation af759e7b-b0db-470a-8c60-e36fe0a9092f · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models Advances in neural information processing systems , volume=

Reference 95

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no resolver link, observed 2026-08-01T17:45:05.174344Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:05.174344Z digest=sha256:ee07e563472fa897373518e4b0d23723ac9687e51a69df0250fefcfa27433481

Observation e0f966ab-b7b2-4980-bc8d-6c54e8719454 · outbound

This paper cites Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion.

Reinforcement Learning: From Algorithms To Foundation Models Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion

Reference 96

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:05.334328Z digest=sha256:60d95b0947f3708ee78a8b590b02e52f527f226b540445c208d342500b47800d

Observation 24cc60be-80e7-4d68-853b-419c7f98782f · outbound

This paper cites EM Distillation for One-step Diffusion Models.

Reinforcement Learning: From Algorithms To Foundation Models EM Distillation for One-step Diffusion Models

Reference 97

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source=arxiv_source observed=2026-08-01T17:45:05.447685Z digest=sha256:6197ac65e220e0bd48d22b79813065a76250534d68cf997bc7ebd3bd14a060c6

Observation f2df04bf-00ae-41e2-839e-dff76993e8b4 · outbound

This paper cites Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models.

Reinforcement Learning: From Algorithms To Foundation Models Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models

Reference 98

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:05.528380Z digest=sha256:60e2deb88dca8634177e3ca01edc81dbba6581166209638486b43bb7823c4765

Observation 1875f8ed-3374-4d45-8019-c1d4148a2ee9 · outbound

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

Reinforcement Learning: From Algorithms To Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 99

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no resolver link, observed 2026-08-01T17:45:05.625057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T17:45:05.625057Z digest=sha256:78dc905cfcced3e07479bd5d7c7aa84b959d08c9bf75b63d4597c3f3031475d5

Observation 6b3dda41-f719-4997-b882-053f781e54a6 · outbound

This paper cites Multistep Distillation of Diffusion Models via Moment Matching.

Reinforcement Learning: From Algorithms To Foundation Models Multistep Distillation of Diffusion Models via Moment Matching

Reference 100

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T17:45:05.707426Z digest=sha256:713c9c850a133d5d536cfdb0c5ce861accc673419fccd3b7b503f1441c602f4f

Pith citing papers

No inbound Pith citation observations are available.