Paper Citation Record · LEDGER

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only

As of 8 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2505.16856.

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

pith.paper-citation-record.v1
2505.16856 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:57:46.595010Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T13:11:16.568415Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T13:13:18.063828Z

Reference resolution

22 of 22 outbound references displayed

verified exact1
verified fuzzy4
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e7312754-442e-45ba-a88b-ee4ac8fec3f6 · outbound

This paper cites Is Conditional Generative Modeling all you need for Decision-Making?.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only Is Conditional Generative Modeling all you need for Decision-Making?

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:44.563327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddec04fc-0c8f-4096-b3bf-0f0d762108ec · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:44.985644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdffa202-d7d7-4237-889c-d54e5cf02b8d · outbound

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

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only Offline Reinforcement Learning with Implicit Q-Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:45.287992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1227962e-e897-4bac-8abd-4895c0763f16 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:45.532496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29cab017-bd44-464d-b574-9ca054a70db0 · outbound

This paper cites Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:45.725844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd6dce6e-0cd0-42ae-b33a-bf09de606e61 · outbound

This paper cites Behavior Regularized Offline Reinforcement Learning.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only Behavior Regularized Offline Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:45.828145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1db2435d-d8c6-4259-8446-4e8bf1354ca8 · outbound

This paper cites Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:46.019748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e013ce3-a10a-471b-aa15-3e97c64c1a33 · outbound

This paper cites Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:57:47.046425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e68f0f04-9717-4c55-bae4-336e6ffd1a20 · outbound

This paper cites ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:46.159807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 526a60c2-719a-4d3d-a26a-1709df4352be · outbound

This paper cites Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:46.275862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9dc4928-078a-4b0a-ae05-ac0a84fe3080 · outbound

This paper cites Reinformer: Max-Return Sequence Modeling for Offline RL.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only Reinformer: Max-Return Sequence Modeling for Offline RL

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:46.353786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d645912-5e95-4d0f-b754-c3ac3eb5a55b · outbound

This paper cites A closely related problem setting is explored in Jump-start reinforcement learning (JSRL) Uchendu et al.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only A closely related problem setting is explored in Jump-start reinforcement learning (JSRL) Uchendu et al

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:48.185476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f813b151-7d58-472b-9a56-6ef2d09bd9b0 · outbound

This paper cites to retaining offline data, the approach of not retaining offline data generally results in improved average normalized scores, accompanied by increasing variance.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only to retaining offline data, the approach of not retaining offline data generally results in improved average normalized scores, accompanied by increasing variance

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:47.979056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ab389c53-f24d-4b5c-915f-931b0bcaea1b · outbound

This paper cites In our experiments, the pre-trained policy serves as the policy prior.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only In our experiments, the pre-trained policy serves as the policy prior

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:47.831310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f62dd1c5-efe2-46b8-bf7e-512d6e575192 · outbound

This paper cites doi: 10.1016/j.robot.2008.10.024.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only doi: 10.1016/j.robot.2008.10.024

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:44.662518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcd94878-eafd-4482-8672-efd02332bc5d · outbound

This paper cites AWAC: Accelerating Online Reinforcement Learning with Offline Datasets.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:45.643556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 344e76bf-c1c8-4503-b413-d65496c998d9 · outbound

This paper cites Elastic Decision Transformer.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only Elastic Decision Transformer

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:45.913949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 963ee686-3c8d-4410-a165-50f527e98b0b · outbound

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

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only OpenVLA: An Open-Source Vision-Language-Action Model

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:45.169227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89eecea1-fdcd-4137-960e-c7136a72294c · outbound

This paper cites Off-policy deep reinforcement learning without exploration.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only Off-policy deep reinforcement learning without exploration

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:48.345158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 20cfc6a2-fa91-4288-86fb-cbf6c81b5c33 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:45.414360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef582d84-4c4b-44e8-9edd-05a8791a980d · outbound

This paper cites Randomized Ensembled Double Q-Learning: Learning Fast Without a Model.

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:44.897231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28005ef9-f6da-4e9e-82e9-5284e244accf · outbound

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

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:44.786459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 5dc93c48-cbd9-4eda-a6c5-b228f351456b · inbound

COOPO: Cyclic Offline-Online Policy Optimization Algorithm cites this paper.

COOPO: Cyclic Offline-Online Policy Optimization Algorithm Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:13:18.065518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.