Paper Citation Record · LEDGER

Can Learned Optimization Make Reinforcement Learning Less Difficult?

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2407.07082.

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

pith.paper-citation-record.v1
2407.07082 v3

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 2026-08-22T06:32:14.747728+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:16:28.937160Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2b79ed6f-1eb3-4c4a-8054-13d8babdbe4b · inbound

Celo: Training Versatile Learned Optimizers on a Compute Diet cites this paper.

Celo: Training Versatile Learned Optimizers on a Compute Diet Can Learned Optimization Make Reinforcement Learning Less Difficult?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T17:01:13.785256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 721330c9-be0b-40bf-bf43-596a35d3b977 · inbound

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning cites this paper.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Can Learned Optimization Make Reinforcement Learning Less Difficult?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T19:16:28.937160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fccf9ced-5d15-4f59-a74e-442bc1bb0471 · inbound

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback cites this paper.

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback Can Learned Optimization Make Reinforcement Learning Less Difficult?

Reference 265

Resolution: verified exact; local_arxiv, observed 2026-08-03T04:44:18.380119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aebf1e82-e7d4-420b-b10e-aacea1cd344b · inbound

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details cites this paper.

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details Can Learned Optimization Make Reinforcement Learning Less Difficult?

Reference 264

Resolution: unresolved; no resolver link, observed 2026-08-05T15:25:40.590903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.