Paper Citation Record · LEDGER

Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 30 inbound Pith citation observations for arXiv:2210.06718.

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

pith.paper-citation-record.v1
2210.06718 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 30 of 30 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 30 of 30 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:27:41.065666Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:50:12.306741Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4ee7bc27-b8b3-44e2-be21-0238b4b01c53 · inbound

From Foresight to Forethought: VLM-In-the-Loop Policy Steering via Latent Alignment cites this paper.

From Foresight to Forethought: VLM-In-the-Loop Policy Steering via Latent Alignment Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-09T14:27:41.065666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only cites this paper.

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

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Observation e7753a3a-af6e-4cb7-ab0c-504b6df18e02 · inbound

Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment cites this paper.

Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:00.439564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b408ba0-19b1-410c-bd43-e0e121d4bbbf · inbound

Reinforcement Learning via Implicit Imitation Guidance cites this paper.

Reinforcement Learning via Implicit Imitation Guidance Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:37:46.383535Z

Source-reported events for the cited work

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Observation 3e703aba-876f-44e6-abea-a1f2da435afd · inbound

EXPO: Stable Reinforcement Learning with Expressive Policies cites this paper.

EXPO: Stable Reinforcement Learning with Expressive Policies Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T05:12:05.259804Z

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 767f32c3-af73-45db-9a21-063a76a9d3bb · inbound

Online Pre-Training for Offline-to-Online Reinforcement Learning cites this paper.

Online Pre-Training for Offline-to-Online Reinforcement Learning Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T18:27:02.465774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 526b2c15-4c8c-4ea7-8dee-d7cd56523cfc · inbound

Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review cites this paper.

Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 162

Resolution: unresolved; no resolver link, observed 2026-08-06T17:42:59.023990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54d71233-b41f-407d-9024-bd9fa7794d14 · inbound

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods cites this paper.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:30.243849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61191bb3-ed59-4c20-bad3-3192f7dd2288 · inbound

The Three Regimes of Offline-to-Online Reinforcement Learning cites this paper.

The Three Regimes of Offline-to-Online Reinforcement Learning Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T13:00:08.709075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2dc4c0c-1335-4d2d-9833-b78d661542cd · inbound

Learning Upper Lower Value Envelopes to Shape Online RL: A Principled Approach cites this paper.

Learning Upper Lower Value Envelopes to Shape Online RL: A Principled Approach Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T08:45:50.812731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfaca071-6b06-4792-aa97-c858755680ae · inbound

On the Sample Complexity of Differentially Private Policy Optimization cites this paper.

On the Sample Complexity of Differentially Private Policy Optimization Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:45:54.791452Z

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 5fe5774a-48df-4fd5-a21a-3bdccbd329b0 · inbound

On the Complexity of Offline Reinforcement Learning with $Q^\star$-Approximation and Partial Coverage cites this paper.

On the Complexity of Offline Reinforcement Learning with $Q^\star$-Approximation and Partial Coverage Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T00:05:19.691967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b234c6c-1aac-4e6c-8ffb-f70253b147a7 · inbound

OP-GRPO: Efficient Off-Policy GRPO for Flow-Matching Models cites this paper.

OP-GRPO: Efficient Off-Policy GRPO for Flow-Matching Models Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.892690Z

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 64a95906-4a9a-49a6-9fd2-4af53865b724 · inbound

WOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning cites this paper.

WOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:10:54.307546Z

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 2de8b1eb-8296-4ad6-b7c6-3fdafcb22eda · inbound

Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation cites this paper.

Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:55:24.163245Z

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 975e4bed-bd57-4f19-abf5-fb3ea33ed49f · inbound

Fisher Decorator: Refining Flow Policy via a Local Transport Map cites this paper.

Fisher Decorator: Refining Flow Policy via a Local Transport Map Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:51:46.640676Z

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 49d113e4-ba79-4d98-8959-46d1516a6769 · inbound

Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies cites this paper.

Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:36:12.446351Z

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 39a0a6bc-4bbd-43fc-b36f-1e8850532b61 · inbound

Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies cites this paper.

Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-01T08:15:32.342064Z

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 0b2b7717-b975-48fe-9716-aed5571caef9 · inbound

SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data cites this paper.

SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:41:08.508849Z

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 fdc32471-d64f-439d-bd74-0d8826c39e84 · inbound

SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data cites this paper.

SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-21T09:19:56.740644Z

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 796d031f-96a3-44c4-a061-5a62bb85114e · inbound

Sample-Mean Anchored Thompson Sampling for Offline-to-Online Learning with Distribution Shift cites this paper.

Sample-Mean Anchored Thompson Sampling for Offline-to-Online Learning with Distribution Shift Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:01:24.988012Z

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 fe4e1566-4459-4778-935c-a9e6bb1344db · inbound

Sample-Mean Anchored Thompson Sampling for Offline-to-Online Learning with Distribution Shift cites this paper.

Sample-Mean Anchored Thompson Sampling for Offline-to-Online Learning with Distribution Shift Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:09:45.449771Z

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 fb0c0acb-c715-42a3-90ff-ba797ec27a00 · inbound

ROAD: Adaptive Data Mixing for Offline-to-Online Reinforcement Learning via Bi-Level Optimization cites this paper.

ROAD: Adaptive Data Mixing for Offline-to-Online Reinforcement Learning via Bi-Level Optimization Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:33:27.192556Z

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 acb0340c-2a99-43bc-9711-85dba150d4d4 · inbound

Peng's Q($\lambda$) for Conservative Value Estimation in Offline Reinforcement Learning cites this paper.

Peng's Q($\lambda$) for Conservative Value Estimation in Offline Reinforcement Learning Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:25:45.850150Z

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 634974ff-59c9-40ec-a2f8-321d59b8e1f4 · inbound

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

COOPO: Cyclic Offline-Online Policy Optimization Algorithm Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:13:18.031118Z

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 c40f1fc8-4888-48af-ab4b-b102ebdd2e46 · inbound

When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning? cites this paper.

When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning? Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-07-03T23:29:02.983617Z

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 87b49c29-d6ad-4e76-8c37-c3c35c24bd8a · inbound

FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation cites this paper.

FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:50:12.308569Z

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 c84440eb-076d-4640-b8b7-a7d1d2dbf63f · inbound

Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 282

Resolution: unresolved; no resolver link, observed 2026-07-11T13:53:36.775836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8aa1f197-80c9-41b7-b6f9-9f1cb3891903 · inbound

Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 283

Resolution: unresolved; no resolver link, observed 2026-08-02T08:41:05.419064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ae795ad-3718-4759-94ea-ce72fd92563d · inbound

A Unified Algorithmic Framework for Hybrid Reinforcement Learning in Tabular MDPs with Shifted Transition Dynamics cites this paper.

A Unified Algorithmic Framework for Hybrid Reinforcement Learning in Tabular MDPs with Shifted Transition Dynamics Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T03:13:35.351631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.