Paper Citation Record · LEDGER

CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

As of 20 August 2026, this Paper Citation Record lists 1 of 1 outbound references and 21 inbound Pith citation observations for arXiv:2508.02219.

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

pith.paper-citation-record.v1
2508.02219 v1

Coverage vector

measured 1 of 1 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:08:58.294304Z

measured 22 of 22 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 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:07:53.590762Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

1 of 1 outbound references displayed

verified exact0
verified fuzzy0
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation a7f4d75d-58df-4404-b182-07bfda9e54ac · outbound

This paper cites Reservoir Computing with Evolved Critical Neural Cellular Automata.

CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning Reservoir Computing with Evolved Critical Neural Cellular Automata

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T05:08:58.294304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation ffd37e1d-829b-40a6-81a6-fcaa01cf79ac · inbound

Welcome New Doctor: Continual Learning with Expert Consultation and Autoregressive Inference for Whole Slide Image Analysis cites this paper.

Welcome New Doctor: Continual Learning with Expert Consultation and Autoregressive Inference for Whole Slide Image Analysis CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T05:07:53.590762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88b5839f-a449-4f4b-b509-ff0bbf97773e · inbound

Reflection-Based Task Adaptation for Self-Improving VLA cites this paper.

Reflection-Based Task Adaptation for Self-Improving VLA CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T07:31:02.961697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f3d6fb73-8d81-4939-ade9-134f28c319f0 · inbound

$\pi^{*}_{0.6}$: a VLA That Learns From Experience cites this paper.

$\pi^{*}_{0.6}$: a VLA That Learns From Experience CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:34:59.381175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 51010b89-9ed4-4b0f-abd3-f04895bb0a42 · inbound

VGAS: Value-Guided Action-Chunk Selection for Few-Shot Vision-Language-Action Adaptation cites this paper.

VGAS: Value-Guided Action-Chunk Selection for Few-Shot Vision-Language-Action Adaptation CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:00:26.213398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e81779a8-a0f2-4b59-b4f8-ea76061484f3 · inbound

TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation cites this paper.

TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-21T13:14:10.887379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bf91abe5-6edc-4b13-9ff7-e5a33d800efd · inbound

ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training cites this paper.

ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T23:47:55.110932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5819ae54-4cdd-48d7-8798-c01e51650057 · 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 CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 15

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c1b2ae4-9a19-4c87-85f7-95f661e3136d · inbound

ViVa: A Video-Generative Value Model for Robot Reinforcement Learning cites this paper.

ViVa: A Video-Generative Value Model for Robot Reinforcement Learning CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:25:59.340149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ea91d723-9389-46d6-a35f-a9721eeda47f · inbound

Activation Steering for Aligned Open-ended Generation without Sacrificing Coherence cites this paper.

Activation Steering for Aligned Open-ended Generation without Sacrificing Coherence CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-13T00:03:53.609175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db7ed091-d023-47be-a8f6-9384ee99b3ba · inbound

Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems cites this paper.

Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:06:05.531969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3384052e-8083-4719-9094-efaa0a0613cb · inbound

Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems cites this paper.

Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 112

Resolution: verified exact; arxiv_id, observed 2026-05-09T23:29:43.568251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation efb2fe58-967b-434c-9d00-032ac521fac1 · inbound

ProcVLM: Learning Procedure-Grounded Progress Rewards for Robotic Manipulation cites this paper.

ProcVLM: Learning Procedure-Grounded Progress Rewards for Robotic Manipulation CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-12T01:41:20.036806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7f949a9d-3c77-4ad3-bdb8-208446f44c00 · inbound

ACSAC: Adaptive Chunk Size Actor-Critic with Causal Transformer Q-Network cites this paper.

ACSAC: Adaptive Chunk Size Actor-Critic with Causal Transformer Q-Network CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:17:08.104251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9cc1ce54-94b5-4893-b918-36da1505d50e · inbound

DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization cites this paper.

DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 131

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T12:43:17.271396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ee49567d-47c8-4cbf-9403-3016844ae176 · inbound

BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models cites this paper.

BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-29T07:23:12.776418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a09b51ed-97f9-4532-bb9f-afc98228c689 · inbound

FiberTune: Preserving Action-Fiber Visual Residuals in Vision-Language-Action Fine-Tuning cites this paper.

FiberTune: Preserving Action-Fiber Visual Residuals in Vision-Language-Action Fine-Tuning CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:37:26.708865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e23c959d-fe8e-439b-841b-65b3e2c36ad9 · inbound

DexPIE: Stable Dexterous Policy Improvement from Real-World Experience cites this paper.

DexPIE: Stable Dexterous Policy Improvement from Real-World Experience CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-03T01:07:30.710980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d1b2db18-6b1c-428a-9846-3a3fa2cdbe30 · inbound

PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models cites this paper.

PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:09:43.327483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a99dc23a-4316-4149-8d24-a4d546e96fd5 · inbound

Trust Your Instincts: Confidence-Driven Test-Time RL for Vision-Language-Action Models cites this paper.

Trust Your Instincts: Confidence-Driven Test-Time RL for Vision-Language-Action Models CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:04:21.144703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8eda68da-2f80-4b0c-83c9-5ac33c38d557 · inbound

WorldSample: Closed-loop Real-robot RL with World Modelling cites this paper.

WorldSample: Closed-loop Real-robot RL with World Modelling CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:58:02.464658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 734a8d37-c8f0-4800-aedc-394c6387aa0d · inbound

RedFlow: Redirect Failure into Action-Level Corrections for Flow-matching VLA Policy cites this paper.

RedFlow: Redirect Failure into Action-Level Corrections for Flow-matching VLA Policy CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T01:32:27.131743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.