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Paper Citation Record · LEDGER

Leverage the Average: an Analysis of KL Regularization in RL

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2003.14089.

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

pith.paper-citation-record.v1
2003.14089 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T00:53:27.989253Z

measured 0 of 1 external citation measurements

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

Source: cited_works

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 fa639a5d-d1df-4de3-b211-77a8127240ce · inbound

GeMPO: Generalized Measure Matching for Online Diffusion Reinforcement Learning cites this paper.

GeMPO: Generalized Measure Matching for Online Diffusion Reinforcement Learning Leverage the Average: an Analysis of KL Regularization in RL

Reference 53

Resolution
unresolved
no resolver link, observed 2026-07-14T23:47:45.866615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T23:47:45.866615Z digest=sha256:4d40c74c0f5bef0cd7c17ad7571629c7ecf0d5b09a27b8897ea1f0e042b5f1be

Observation 352f964e-166d-4f4f-8e61-4f50a47f5763 · inbound

Abstention as an Action Can Kill Both the Reward Gradient and the KL Anchor: Collapse Law and Repair for Error-Penalized Reinforcement Learning cites this paper.

Abstention as an Action Can Kill Both the Reward Gradient and the KL Anchor: Collapse Law and Repair for Error-Penalized Reinforcement Learning Leverage the Average: an Analysis of KL Regularization in RL

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-04T00:53:27.989253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T00:53:27.989253Z digest=sha256:64ecc04dc7042f25c0f6f5ed54390b5db09e82189abaef44efe7db5b7cd9332a