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Paper Citation Record · LEDGER

Policy Expansion for Bridging Offline-to-Online Reinforcement Learning

As of 8 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 2026-08-07T06:34:17.273281+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

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  • 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=sha256:9ff296615f63a7439887dcc55f9dba545141da119e495820a6530e101d897ba8

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:cfffd4b8e99eafc1434236c61d263b39ed9992d1321b7863111fe368d24a94d0

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-19T05:09:02.111308Z digest=sha256:fda55ac3cc600f1f28fbeb745ccdf175f5a5f2939ea32cd7153526cd554a1d7a

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:e03d9ee05b2fad463d03d19945756d260ba859dcc4eff19bf8df876430440835

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-30T21:45:43.298829Z digest=sha256:4333fe94a363a14d73d8df6204ce8a6e76bca13d550b90b959d5d0440b00edb4

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-20T13:11:16.568415Z digest=sha256:9c7329bad2aa3ba59eb02ea54492b710b0befbf74f08ce4861763d47b8f82fb6

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=sha256:7d84d5ea874229abc9f97780dd00c7dc315aef0127a6169e7b5b6116adeb6776

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:d3868a3936030a00eb647b539ab3e68c0fb5f98446dadbdede961a51ddd73240