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Paper Citation Record · LEDGER

To the Max: Reinventing Reward in Reinforcement Learning

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.01361.

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

pith.paper-citation-record.v1
2402.01361 v2

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 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T22:06:24.412259Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T23:29:02.965421Z

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 2eeca29a-349a-41e0-9501-e0b41b1b9853 · inbound

ADAPT: A Self-Calibrating Proactive Autoscaler for Container Orchestration cites this paper.

ADAPT: A Self-Calibrating Proactive Autoscaler for Container Orchestration To the Max: Reinventing Reward in Reinforcement Learning

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-19T19:27:43.477272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-19T19:26:15.876258Z digest=sha256:a1688b93e0c0d5b588c42a2a2ddd3e1450cba0f4712f6e151ca5b69063a9440a

Observation 339fe95b-8e46-45fe-8c86-939723a82227 · inbound

When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning? cites this paper.

When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning? To the Max: Reinventing Reward in Reinforcement Learning

Reference 31

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T23:29:02.967822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-06-26T22:06:24.412259Z digest=sha256:74770ab1653ca2ed00e4e9f1ede4b348125e5b89bb876955def66f7e8e8270a8