Paper Citation Record · LEDGER

A Minimalist Approach to Offline Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2106.06860.

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

pith.paper-citation-record.v1
2106.06860 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 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 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:49.563459Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T15:48:35.790054Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 51a66283-e572-4a08-be80-fce6ef0dd05f · inbound

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

Offline Reinforcement Learning with Implicit Q-Learning A Minimalist Approach to Offline Reinforcement Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:47:05.657711Z

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.

Observation 5d26abf5-f678-4528-b515-ff9311f8250f · inbound

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning cites this paper.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning A Minimalist Approach to Offline Reinforcement Learning

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:49.563459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89ae6e6c-7ec4-41bc-9482-af7ed4a5175e · inbound

LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading cites this paper.

LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading A Minimalist Approach to Offline Reinforcement Learning

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:12:59.558808Z

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.

Observation f6bb7935-011d-45b5-b48d-5cdb1e8878b1 · inbound

Value Flows cites this paper.

Value Flows A Minimalist Approach to Offline Reinforcement Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:29.132863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45e82ea6-8e6e-453e-9480-8986a521a066 · inbound

CA2: Code-Aware Agent for Automated Game Testing cites this paper.

CA2: Code-Aware Agent for Automated Game Testing A Minimalist Approach to Offline Reinforcement Learning

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:55:05.004799Z

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.

Observation 9a684a99-7f3b-44ab-81da-afe85ad7232c · inbound

Abstraction for Offline Goal-Conditioned Reinforcement Learning cites this paper.

Abstraction for Offline Goal-Conditioned Reinforcement Learning A Minimalist Approach to Offline Reinforcement Learning

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:16.557654Z

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.

Observation 28adf420-75c1-48b3-875d-27c10d4c23aa · inbound

Improving Robotic Generalist Policies via Flow Reversal Steering cites this paper.

Improving Robotic Generalist Policies via Flow Reversal Steering A Minimalist Approach to Offline Reinforcement Learning

Reference 95

Resolution: verified exact; arxiv_id, observed 2026-07-03T15:48:35.791427Z

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.

Observation 28d6258e-19ea-4d11-b988-7dcafbc0e075 · inbound

Conservative Query and Adaptive Regularization for Offline RL Under Uncertainty Estimation cites this paper.

Conservative Query and Adaptive Regularization for Offline RL Under Uncertainty Estimation A Minimalist Approach to Offline Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T13:15:11.305436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 284ecaa6-f965-42ab-b8fb-ab7552a9bd9b · 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? A Minimalist Approach to Offline Reinforcement Learning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-30T11:06:22.422202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ba61b2f-ad5d-4c3f-90f1-50f313f9c14c · 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? A Minimalist Approach to Offline Reinforcement Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T04:27:43.875428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4576c67-2ef9-4af1-9b26-b064f136544e · inbound

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills cites this paper.

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills A Minimalist Approach to Offline Reinforcement Learning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-04T19:45:30.154815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.