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Paper Citation Record · LEDGER

Real-time Policy Distillation in Deep Reinforcement Learning

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1912.12630.

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

pith.paper-citation-record.v1
1912.12630 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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:04:33.375549Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T18:50:50.302342Z

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 3f39f943-d78a-4820-8275-10afc997d5ca · inbound

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation cites this paper.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation Real-time Policy Distillation in Deep Reinforcement Learning

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-12T13:04:33.375549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:04:33.375549Z digest=sha256:5723ceb5211b8d2cfafa4d306c1846fc0591b7c18070593c2dc2b5af997bfb4a

Observation 6e375caa-a28a-48df-813b-a699e22a5bc3 · inbound

Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning cites this paper.

Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning Real-time Policy Distillation in Deep Reinforcement Learning

Reference 32

Resolution
verified exact
local_arxiv, observed 2026-08-08T18:50:50.307561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-08T18:50:49.876454Z digest=sha256:fd16b129b10fa4a243a92724e26a47ca87c3ac0076888e10540fc5bd3658d886