Paper Citation Record · LEDGER

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning

As of 15 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2608.03872.

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

pith.paper-citation-record.v1
2608.03872 v1

Coverage vector

measured 40 of 40 reference resolution

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measured 40 of 40 standing notices

One-hop event checks from named stored sources.

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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

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation c6a5e125-819a-4c7c-81a0-a85a03ff8c17 · outbound

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

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.464484Z

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Observation 3dbb61d6-3dbf-49e8-a7ff-f65676dc94be · outbound

This paper cites SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.455706Z

Source-reported events for the cited work

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Observation 07bbcc99-6cde-46cd-8fe9-77c30a925837 · outbound

This paper cites Efficient Online Reinforcement Learning with Offline Data,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Efficient Online Reinforcement Learning with Offline Data,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.447346Z

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Observation 7970e09c-8226-4a0b-84c3-cc097a6ca283 · outbound

This paper cites Reinforcement Learning in Robotics: A Survey,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Reinforcement Learning in Robotics: A Survey,

Reference 4

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Observation ee003f70-77fd-41ec-9553-583f223dc2f9 · outbound

This paper cites QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.430466Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b3096895-cdc3-49b6-a3f4-ac879ef16933 · outbound

This paper cites How to Train Your Robot with Deep Reinforcement Learning: Lessons We Have Learned,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning How to Train Your Robot with Deep Reinforcement Learning: Lessons We Have Learned,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.421970Z

Source-reported events for the cited work

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Observation 00b39d8e-ce23-45e3-bc7e-87952e9e97d3 · outbound

This paper cites Addressing Function Approximation Error in Actor-Critic Methods,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Addressing Function Approximation Error in Actor-Critic Methods,

Reference 7

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Observation 40e94431-dc38-4d66-b60b-a0f90c99a8b7 · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Offline Reinforcement Learning with Implicit Q-Learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.404067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2a819dc0-b74a-4df3-8ca1-d924d20837c4 · outbound

This paper cites A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.395749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 34b44d0c-441e-4c62-8145-4a128c3a3356 · outbound

This paper cites HG-DAgger: Interactive Imitation Learning with Human Experts,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning HG-DAgger: Interactive Imitation Learning with Human Experts,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.386727Z

Source-reported events for the cited work

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Observation f17c478f-2b73-4db6-9063-41c732a28843 · outbound

This paper cites Deep Reinforcement Learning from Human Preferences,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Deep Reinforcement Learning from Human Preferences,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.378305Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4a128a76-9719-4ecd-acc5-a861f86a7e73 · outbound

This paper cites Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.369472Z

Source-reported events for the cited work

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Observation 0483ecda-299b-411f-be2a-7c5315a3f4ac · outbound

This paper cites Positive-Unlabeled Reward Learning,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Positive-Unlabeled Reward Learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.360738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0cb43490-8acb-4247-9fd0-dd6f41a666e9 · outbound

This paper cites Human-Guided Online Reward Adaptation for Real-Robot Arm Manipulation,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Human-Guided Online Reward Adaptation for Real-Robot Arm Manipulation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.350941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 69bc05e6-d327-4c3b-9aed-b739e9514e80 · outbound

This paper cites On Calibration of Modern Neural Networks,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning On Calibration of Modern Neural Networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:32.194875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 723355f4-b2c4-4eb5-b93a-4753b1fbb54b · outbound

This paper cites Learning from Imbalanced Data,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Learning from Imbalanced Data,

Reference 16

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Observation 2ac28726-310c-4896-acc0-01e4ed9f700b · outbound

This paper cites A Survey on Concept Drift Adaptation,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning A Survey on Concept Drift Adaptation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:31.758765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 59d41c21-fca4-407a-857b-bdf9634e7584 · outbound

This paper cites Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:31.454474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d5cb31de-eda3-46a4-bd7d-c5bb94bd9ddd · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learning via Action Diffusion,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Diffusion Policy: Visuomotor Policy Learning via Action Diffusion,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:31.134034Z

Source-reported events for the cited work

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Observation a22c2192-035a-45e7-90ae-1e414fee8020 · outbound

This paper cites Implicit Behavioral Cloning,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Implicit Behavioral Cloning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:31.028914Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 32dbd220-c69c-442c-be16-d6c59c2bd1f1 · outbound

This paper cites Flow Matching for Generative Modeling,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Flow Matching for Generative Modeling,

Reference 21

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Observation 9a1dcbfa-d030-4544-b0af-da1932723aba · outbound

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

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:30.713808Z

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Observation dbb56229-7bfe-4981-8084-ad4297a29541 · outbound

This paper cites Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:30.580894Z

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Observation 6d19b647-08af-4fa0-a909-304ec2ad1165 · outbound

This paper cites Reinforcement Learning with Action Chunking.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Reinforcement Learning with Action Chunking

Reference 24

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Observation 83299f6f-d6a1-4a29-8389-38c1d2a70f88 · outbound

This paper cites Diffusion Policy Policy Optimization,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Diffusion Policy Policy Optimization,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:30.513155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d2f9b537-f368-497f-969b-1ce7105477e0 · outbound

This paper cites ReinFlow: Fine-Tuning Flow Matching Policy with Online Reinforcement Learning,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning ReinFlow: Fine-Tuning Flow Matching Policy with Online Reinforcement Learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:30.409585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6923b95e-7b69-446d-afe5-258403ac8ab1 · outbound

This paper cites Flow Matching Policy Gradients,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Flow Matching Policy Gradients,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:30.323687Z

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Observation 6cd7f075-b8fa-4009-92b8-47037a1cc47d · outbound

This paper cites SAC Flow: Sample-Efficient Reinforcement Learning of Flow-Based Policies via Velocity-Reparameterized Sequential Model- ing,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning SAC Flow: Sample-Efficient Reinforcement Learning of Flow-Based Policies via Velocity-Reparameterized Sequential Model- ing,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:30.197362Z

Source-reported events for the cited work

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Observation 2517fc5d-0c76-4dc7-a093-946cd622f38c · outbound

This paper cites Reinforcement Learning with Augmented Data,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Reinforcement Learning with Augmented Data,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:29.994478Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e7cf9319-7bf0-4076-bc40-bdd9ec4bd6f4 · outbound

This paper cites Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:29.825849Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ccc354a0-1743-4129-8397-473e10f00ace · outbound

This paper cites Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:29.674788Z

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Observation bc970c47-367d-4ff0-a7e2-d1f5bbaa39a3 · outbound

This paper cites AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:29.554995Z

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Observation f97f4f2d-835f-4c2c-a10a-38183256577b · outbound

This paper cites Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control,

Reference 33

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Observation dc7d9fb0-6292-4c23-88a6-5756ed7bfc09 · outbound

This paper cites Experience Replay for Continual Learning,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Experience Replay for Continual Learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:29.280319Z

Source-reported events for the cited work

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Observation 474f25e8-3476-42bd-b255-702e7abdd32d · outbound

This paper cites Regularizing Action Policies for Smooth Control with Reinforcement Learning,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Regularizing Action Policies for Smooth Control with Reinforcement Learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:29.166038Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e85a1045-5005-4159-8b4c-1f52b905c0ff · outbound

This paper cites Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:29.009607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7c8b414a-ce74-4b4e-82e7-1ad220966588 · outbound

This paper cites Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:28.840976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a2cf5f71-6aba-4db1-acfc-d02d664a6107 · outbound

This paper cites Imitation Bootstrapped Rein- forcement Learning,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Imitation Bootstrapped Rein- forcement Learning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:28.686166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8d219ba2-7854-4e8b-bbaf-96a92aaa9b08 · outbound

This paper cites Deep Residual Learning for Image Recognition,.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning Deep Residual Learning for Image Recognition,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:46:28.536036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e6f0ca36-6b27-4b5b-9b06-5f9778184faf · outbound

This paper cites UniRelight: Learning Joint Decomposition and Synthesis for Video Relighting.

EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning UniRelight: Learning Joint Decomposition and Synthesis for Video Relighting

Reference 40

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

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