Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Can Learned Optimization Make Reinforcement Learning Less Difficult?
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.07082.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T17:01:13.785256Z
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 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 2026-08-13T06:32:02.005865+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.