Pith. sign in

Paper Citation Record · LEDGER

Maximum Reward Formulation In Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2010.03744.

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

pith.paper-citation-record.v1
2010.03744 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:26:19.003008Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T23:29:02.946204Z

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 49ddf05e-1189-4f76-a303-5ccc80dee85c · inbound

Recursive Reward Aggregation cites this paper.

Recursive Reward Aggregation Maximum Reward Formulation In Reinforcement Learning

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T18:26:19.003008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:26:19.003008Z digest=sha256:e62c8fd69db8db7c2c13d3cfefc8c0a2808870fbd9c22ef62f4e9067dad22d8e

Observation 7974386d-d0e6-47e0-8402-d53e4cd7811a · inbound

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

When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning? Maximum Reward Formulation In Reinforcement Learning

Reference 68

Resolution
verified exact
arxiv_id, observed 2026-07-03T23:29:02.947516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-26T22:06:24.412259Z digest=sha256:cfd6d47c5ac39b53350d350e7c0b6c473e2ebb34b9dfab881abada333ca66c43