Paper Citation Record · LEDGER

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning

As of 19 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 2 inbound Pith citation observations for arXiv:2602.02762.

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

pith.paper-citation-record.v1
2602.02762 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T05:21:40.043150Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T23:24:42.433556Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T23:25:07.048665Z

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fff1d71b-d56c-4ba6-8155-c605e96cc368 · outbound

This paper cites write newline.

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning write newline

Reference 1

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Observation e509a80c-0464-45dd-91ac-2f7fe5aea275 · outbound

This paper cites and Saxe, A.

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning and Saxe, A

Reference 2

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Observation e48e0b32-3f1f-4a2f-91f1-7d62259aacaf · outbound

This paper cites Video pretraining (vpt): Learning to act by watching unlabeled online videos.

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Video pretraining (vpt): Learning to act by watching unlabeled online videos

Reference 3

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Unresolved cited work

Reference 4

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This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 5

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Observation b174fa0a-6344-4483-9834-25ead499f1a7 · outbound

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Unresolved cited work

Reference 6

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Observation d1a4d8ad-3dd0-470c-bace-f57e70881072 · outbound

This paper cites D., Edwards, A., Parker-Holder, J., Shi, Y., Hughes, E., Lai, M., Mavalankar, A., Steigerwald, R., Apps, C., Aytar, Y., Bechtle, S.

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning D., Edwards, A., Parker-Holder, J., Shi, Y., Hughes, E., Lai, M., Mavalankar, A., Steigerwald, R., Apps, C., Aytar, Y., Bechtle, S

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This paper cites S., Brutzkus, A., Srebro, N., and Soudry, D.

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning S., Brutzkus, A., Srebro, N., and Soudry, D

Reference 8

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Diffusion policy: Visuomotor policy learning via action diffusion

Reference 9

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This paper cites Y., Bansal, A., Geiping, J., Goldblum, M., and Goldstein, T.

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Y., Bansal, A., Geiping, J., Goldblum, M., and Goldstein, T

Reference 10

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This paper cites Leveraging Procedural Generation to Benchmark Reinforcement Learning.

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 11

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

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Learning universal policies via text-guided video generation

Reference 12

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Unresolved cited work

Reference 13

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This paper cites Imitating latent policies from observation.

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Imitating latent policies from observation

Reference 14

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures

Reference 15

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This paper cites Implicit regularization of discrete gradient dynamics in linear neural networks.

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Implicit regularization of discrete gradient dynamics in linear neural networks

Reference 16

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Prediction with action: Visual policy learning via joint denoising process

Reference 17

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations

Reference 18

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Dreamgen: Unlocking generalization in robot learning through neural trajectories

Reference 19

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Learning to Act from Actionless Videos through Dense Correspondences

Reference 20

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning and Panchenko, D

Reference 21

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Unified Video Action Model

Reference 22

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Autoregressive image generation without vector quantization

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Dreamitate: Real-World Visuomotor Policy Learning via Video Generation

Reference 24

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Video Generators are Robot Policies

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Libero: Benchmarking knowledge transfer for lifelong robot learning

Reference 26

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Videoagenttrek: Computer use pretraining from unlabeled videos

Reference 27

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning and Tsybakov, A

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Unresolved cited work

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning MIT Press, 2018

Reference 30

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Combining self-supervised learning and imitation for vision-based rope manipulation

Reference 31

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs

Reference 33

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Unresolved cited work

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning State-only imitation learning for dexterous manipulation

Reference 35

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Lecture notes

Reference 36

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning High-resolution image synthesis with latent diffusion models

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning and Jiang, M

Reference 38

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Unresolved cited work

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning and Ben-David, S

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning S., Gunasekar, S., and Srebro, N

Reference 41

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Unresolved cited work

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation

Reference 43

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Behavioral Cloning from Observation

Reference 44

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Recent Advances in Imitation Learning from Observation

Reference 45

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Q., and Louis, A

Reference 46

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Neural discrete representation learning

Reference 47

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Unresolved cited work

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation

Reference 49

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Latent Action Pretraining from Videos

Reference 50

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Become a proficient player with limited data through watching pure videos

Reference 51

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Learning to drive by watching youtube videos: Action-conditioned contrastive policy pretraining

Reference 52

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

Reference 53

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On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning mazelab: A customizable framework to create maze and gridworld environments

Reference 54

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Pith citing papers

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Latent Geometry Beyond Search: Amortizing Planning in World Models cites this paper.

Latent Geometry Beyond Search: Amortizing Planning in World Models On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning

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Observation 1b9183e9-73de-40d3-bf01-ec1b3e5acf69 · inbound

Latent Geometry Beyond Search: Amortizing Planning in World Models cites this paper.

Latent Geometry Beyond Search: Amortizing Planning in World Models On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning

Reference 6

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