Paper Citation Record · LEDGER

More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.07198.

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

pith.paper-citation-record.v1
2402.07198 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:06:20.410377Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T02:11:15.741874Z

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 e436da6d-d4cb-4263-92f1-287d22690f4f · inbound

Catoni Contextual Bandits are Robust to Heavy-tailed Rewards cites this paper.

Catoni Contextual Bandits are Robust to Heavy-tailed Rewards More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T12:06:20.410377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90abeef2-eaea-45ce-a822-7ef69123956b · inbound

Value Flows cites this paper.

Value Flows More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:34.735036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8df89cf0-199f-437a-9aea-bb257d1208cf · inbound

Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning cites this paper.

Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:00.730098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0f50cbd7-13b1-47b8-a3ef-c2e34c0a4efe · inbound

Quantile-Coupled Flow Matching for Distributional Reinforcement Learning cites this paper.

Quantile-Coupled Flow Matching for Distributional Reinforcement Learning More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:11:15.743849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.