Paper Citation Record · LEDGER

Reinforcement Learning for Flow-Matching Policies

As of 10 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 9 inbound Pith citation observations for arXiv:2507.15073.

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

pith.paper-citation-record.v1
2507.15073 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:47:41.955782Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:24:58.463499Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T04:17:36.880164Z

Reference resolution

27 of 27 outbound references displayed

verified exact0
verified fuzzy2
unresolved25
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7f832cbb-2e48-488e-b47e-e50bb117805e · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

Reinforcement Learning for Flow-Matching Policies Training Diffusion Models with Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:40.641859Z

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Observation efe58cf4-e773-4d57-a944-838414a31da3 · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

Reinforcement Learning for Flow-Matching Policies RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:40.918955Z

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Observation c84c4e0b-78e2-4fe5-a352-f92aaf9c9d54 · outbound

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

Reinforcement Learning for Flow-Matching Policies Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.260048Z

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Observation 01624f29-4bef-4cc2-b6d5-324553a162f0 · outbound

This paper cites RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment.

Reinforcement Learning for Flow-Matching Policies RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.400153Z

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Observation 6c4e6d43-e587-4005-806b-3c8e424a3f01 · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

Reinforcement Learning for Flow-Matching Policies PaLM-E: An Embodied Multimodal Language Model

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.517438Z

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Observation 9f0cfd26-cbb5-444f-8159-a30454f40814 · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

Reinforcement Learning for Flow-Matching Policies PaLM-E: An Embodied Multimodal Language Model

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.632681Z

Source-reported events for the cited work

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Observation acbc030e-251d-44f7-9aeb-f18fdd388352 · outbound

This paper cites CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation.

Reinforcement Learning for Flow-Matching Policies CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.749121Z

Source-reported events for the cited work

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Observation db182ae3-bb80-4e1a-9a23-eac3251d5bc3 · outbound

This paper cites Planning with Diffusion for Flexible Behavior Synthesis.

Reinforcement Learning for Flow-Matching Policies Planning with Diffusion for Flexible Behavior Synthesis

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.901307Z

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Observation eb367f21-c9e2-43ae-8730-ddcaf01d5f89 · outbound

This paper cites OpenVLA: An Open-Source Vision-Language-Action Model.

Reinforcement Learning for Flow-Matching Policies OpenVLA: An Open-Source Vision-Language-Action Model

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.908301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c472cac-20d5-4e4e-a927-03706a803155 · outbound

This paper cites A Self-Correcting Vision-Language-Action Model for Fast and Slow System Manipulation.

Reinforcement Learning for Flow-Matching Policies A Self-Correcting Vision-Language-Action Model for Fast and Slow System Manipulation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.911320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79b4e05b-730d-46b6-a826-5e32e755241e · outbound

This paper cites Flow Matching for Generative Modeling.

Reinforcement Learning for Flow-Matching Policies Flow Matching for Generative Modeling

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.914645Z

Source-reported events for the cited work

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Observation 1c40ee75-4e41-4c6f-9e8c-ad3e2558d1ec · outbound

This paper cites Flow Matching Guide and Code.

Reinforcement Learning for Flow-Matching Policies Flow Matching Guide and Code

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.918358Z

Source-reported events for the cited work

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Observation 17a60899-bc3c-4287-b812-d2c81ae6ff18 · outbound

This paper cites Generative Trajectory Stitching through Diffusion Composition.

Reinforcement Learning for Flow-Matching Policies Generative Trajectory Stitching through Diffusion Composition

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.921914Z

Source-reported events for the cited work

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Observation 4054e499-7d5e-443d-8403-71c41592b2c5 · outbound

This paper cites Grounding multimodal llms to embodied agents that ask for help with reinforcement learning.

Reinforcement Learning for Flow-Matching Policies Grounding multimodal llms to embodied agents that ask for help with reinforcement learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.925766Z

Source-reported events for the cited work

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Observation 70218636-21da-4b21-bfea-bff1a54fb854 · outbound

This paper cites Diffusion Policy Policy Optimization.

Reinforcement Learning for Flow-Matching Policies Diffusion Policy Policy Optimization

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.929342Z

Source-reported events for the cited work

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Observation c3340881-7e64-42c4-993f-d772d72dc46e · outbound

This paper cites Proximal Policy Optimization Algorithms.

Reinforcement Learning for Flow-Matching Policies Proximal Policy Optimization Algorithms

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.932555Z

Source-reported events for the cited work

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Observation 9fdc379b-bbd8-44a5-aba5-95705e5f7cdb · outbound

This paper cites SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics.

Reinforcement Learning for Flow-Matching Policies SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.939393Z

Source-reported events for the cited work

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Observation 4c22ff31-ea36-4d63-ada6-f96c2174257a · outbound

This paper cites Understanding the performance gap between online and offline alignment algorithms.

Reinforcement Learning for Flow-Matching Policies Understanding the performance gap between online and offline alignment algorithms

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.942955Z

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Observation af23b2b7-c6d2-49f7-971e-1fa979cc42cb · outbound

This paper cites DanceGRPO: Unleashing GRPO on Visual Generation.

Reinforcement Learning for Flow-Matching Policies DanceGRPO: Unleashing GRPO on Visual Generation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.947500Z

Source-reported events for the cited work

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Observation a5e5b0ef-f4a5-45b9-9a80-0e5e981565f9 · outbound

This paper cites We start by collecting 30, 000 demonstration trajectories from πD.

Reinforcement Learning for Flow-Matching Policies We start by collecting 30, 000 demonstration trajectories from πD

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:47:42.388467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b9d8e59c-968e-445c-a9d5-e857999a8adf · outbound

This paper cites To generate samples, we use Euler integration with 4 steps.

Reinforcement Learning for Flow-Matching Policies To generate samples, we use Euler integration with 4 steps

Reference 128

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:47:42.377747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dec882b2-9fe1-4f14-86ff-b35940099ed7 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Reinforcement Learning for Flow-Matching Policies DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.936108Z

Source-reported events for the cited work

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Observation 26c3e4af-5f70-4080-ad3c-c67e36c79162 · outbound

This paper cites Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation.

Reinforcement Learning for Flow-Matching Policies Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.896245Z

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Observation abb0bb71-57f2-43ce-9940-7aa8cc843015 · outbound

This paper cites Refined Policy Distillation: From VLA Generalists to RL Experts.

Reinforcement Learning for Flow-Matching Policies Refined Policy Distillation: From VLA Generalists to RL Experts

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.904894Z

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Observation d2fc7afe-b7ad-4a44-b7ec-7a1ceb508c48 · outbound

This paper cites Simple Hierarchical Planning with Diffusion.

Reinforcement Learning for Flow-Matching Policies Simple Hierarchical Planning with Diffusion

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.128597Z

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Observation c0d62655-327a-46d1-aaca-c7d260a4f923 · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

Reinforcement Learning for Flow-Matching Policies $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:40.788104Z

Source-reported events for the cited work

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Observation ee54109a-fd62-43ca-b7d4-536076fd702d · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Reinforcement Learning for Flow-Matching Policies DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T15:47:41.831875Z

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Pith citing papers

Observation bf0da5b4-75dc-40ec-8ee7-0448eedf1071 · inbound

Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models cites this paper.

Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models Reinforcement Learning for Flow-Matching Policies

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T10:24:58.463499Z

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Observation 0c692d02-33ce-452c-b53d-ba76960fcef3 · inbound

From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning cites this paper.

From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning Reinforcement Learning for Flow-Matching Policies

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-14T23:46:32.301737Z

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Observation 9761a3f3-fc2e-4afb-bc34-77176a4f413e · inbound

HapticVLA: Contact-Rich Manipulation via Vision-Language-Action Model without Inference-Time Tactile Sensing cites this paper.

HapticVLA: Contact-Rich Manipulation via Vision-Language-Action Model without Inference-Time Tactile Sensing Reinforcement Learning for Flow-Matching Policies

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T05:51:28.959396Z

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Observation 4422a34a-b1f5-46b8-863f-d15820c07ad0 · inbound

Preserving Foundational Capabilities in Flow-Matching VLAs through Conservative SFT cites this paper.

Preserving Foundational Capabilities in Flow-Matching VLAs through Conservative SFT Reinforcement Learning for Flow-Matching Policies

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:56:27.226180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d6b7d634-4c3b-4b13-8d56-87f054558f5d · inbound

Preserving Foundational Capabilities in Flow-Matching VLAs through Conservative SFT cites this paper.

Preserving Foundational Capabilities in Flow-Matching VLAs through Conservative SFT Reinforcement Learning for Flow-Matching Policies

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:09:12.156167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 63313854-9e4e-451b-9f58-7dda7d785a68 · inbound

Contrastive Conceptor Activation Steering (COAST): Unlocking Vision-Language-Action Models through Hidden States cites this paper.

Contrastive Conceptor Activation Steering (COAST): Unlocking Vision-Language-Action Models through Hidden States Reinforcement Learning for Flow-Matching Policies

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T14:33:21.515152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c33a8b6f-1a78-47fd-b24c-41837581b798 · inbound