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Paper Citation Record · LEDGER

Policy Expansion for Bridging Offline-to-Online Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2302.00935.

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

pith.paper-citation-record.v1
2302.00935 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:14:46.513693Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T14:25:45.841795Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • 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 d018c99c-319e-49f4-b0e0-a31927ab0fdf · inbound

Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL cites this paper.

Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL Policy Expansion for Bridging Offline-to-Online Reinforcement Learning

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T14:14:46.513693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:14:46.513693Z digest=sha256:9c0f5c058bb95a33ce08e4c35b358897c6abffd16a8fac6ccdc9bc5869cd262f

Observation 5da8b341-7bea-4905-9769-d2ff68f8c0dd · inbound

Reinforcement Learning via Implicit Imitation Guidance cites this paper.

Reinforcement Learning via Implicit Imitation Guidance Policy Expansion for Bridging Offline-to-Online Reinforcement Learning

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T05:37:46.395221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:37:46.395221Z digest=sha256:fc78e3ebcf133634666eff034c167ae386a5e2f2081ee5e58da405b8c71e459d

Observation 024ad57a-0b56-4c04-85c1-c5ee7f3e0583 · inbound

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

EXPO: Stable Reinforcement Learning with Expressive Policies Policy Expansion for Bridging Offline-to-Online Reinforcement Learning

Reference 28

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T05:12:05.255598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T05:09:02.111308Z digest=sha256:62f89a6b257262f3f64a6ff64c6d8b6b203835e6ab618240131f46ad688d94e3

Observation e7981c2a-9b58-4e68-ae9f-df17a4c05f3b · inbound

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

Online Pre-Training for Offline-to-Online Reinforcement Learning Policy Expansion for Bridging Offline-to-Online Reinforcement Learning

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T18:27:02.595382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:27:02.595382Z digest=sha256:866431e1382c3164f335b3d696751c8b94302ce2e7806ca3f1079351638c4367

Observation fb9ce7a3-292d-4538-8c65-9aa20660a83d · inbound

Behavioral Exploration: Learning to Explore via In-Context Adaptation cites this paper.

Behavioral Exploration: Learning to Explore via In-Context Adaptation Policy Expansion for Bridging Offline-to-Online Reinforcement Learning

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T18:15:47.326956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:15:47.326956Z digest=sha256:e54a551b3d4b90f9151b5c92e7c3a0ce0fdc28cf81112c5b95215c3a425b9d5e

Observation 2233e8eb-d19c-47d4-9a82-2084bba93f14 · 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 Policy Expansion for Bridging Offline-to-Online Reinforcement Learning

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-07-01T14:25:45.843996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T21:45:43.298829Z digest=sha256:b0132433d57288d2aebdd000007c1f66dffdd021138193fbb3400a12319bf543

Observation deaa1b91-67e4-481c-8018-c53e9ba5ba7f · inbound

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

COOPO: Cyclic Offline-Online Policy Optimization Algorithm Policy Expansion for Bridging Offline-to-Online Reinforcement Learning

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-20T13:13:18.020381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T13:11:16.568415Z digest=sha256:c1a54408e04b5f05992483367c0c4314643641e7712721a1a9a62889b04b9c27

Observation 99a6b82f-d309-4e44-a87b-4c97316aa44f · inbound

Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning? cites this paper.

Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning? Policy Expansion for Bridging Offline-to-Online Reinforcement Learning

Reference 66

Resolution
unresolved
no resolver link, observed 2026-07-30T11:06:24.304148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T11:06:24.304148Z digest=sha256:59cedcccb3e6abcfb354883594c077c20208e8dadfcf296071f18137eedd0168

Observation 94deceb8-0c11-4433-b87a-394c4ee5677b · inbound

Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning? cites this paper.

Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning? Policy Expansion for Bridging Offline-to-Online Reinforcement Learning

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-05T04:27:43.955043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T04:27:43.955043Z digest=sha256:2b1a20c629da3895553f53fbe5bd0629d6ea8b89839676b1797ce563dc0a46d8