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Paper Citation Record · LEDGER

Optimization Issues in KL-Constrained Approximate Policy Iteration

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2102.06234.

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

pith.paper-citation-record.v1
2102.06234 v1

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 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:08:14.376134Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T18:14:34.240235Z

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 7f8938ac-71b1-42de-abd2-3a705b91283a · inbound

Fast Convergence of Softmax Policy Mirror Ascent cites this paper.

Fast Convergence of Softmax Policy Mirror Ascent Optimization Issues in KL-Constrained Approximate Policy Iteration

Reference 21

Resolution
verified exact
local_arxiv, observed 2026-08-12T18:14:34.247286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T18:14:33.882424Z digest=sha256:75ef41afdb71d6ccf81227a61be2dd50ec99ee3039683b699c5a5c745cd62443

Observation a8c9afe5-3b56-498f-9129-1c8a0280e79a · inbound

Aligning Frozen LLMs by Reinforcement Learning: An Iterative Reweight-then-Optimize Approach cites this paper.

Aligning Frozen LLMs by Reinforcement Learning: An Iterative Reweight-then-Optimize Approach Optimization Issues in KL-Constrained Approximate Policy Iteration

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-15T19:08:14.376134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:08:14.376134Z digest=sha256:42eebee18d99738a6c8dd987de7a7bd3cbc630dd5bd90011b3776db27dc37024