Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improving Policy Gradient by Exploring Under-appreciated Rewards
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1611.09321.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T12:16:31.952713Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-17T12:04:10.853240Z
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 b1d4b1dd-1805-41ae-8243-6baae72081ec · inbound
Training Language Models to Self-Correct via Reinforcement Learning cites this paper.
Training Language Models to Self-Correct via Reinforcement Learning Improving Policy Gradient by Exploring Under-appreciated Rewards
Reference 224
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-17T12:04:10.855601Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 42d83879-5ccc-4995-84eb-62af187d97d4 · inbound
Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment cites this paper.
Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Improving Policy Gradient by Exploring Under-appreciated Rewards
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T12:16:31.952713Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c4b94ee6-79d0-4931-8ac2-ba475ccf4576 · inbound
Bidirectional Soft Actor-Critic: Leveraging Forward and Reverse KL Divergence for Efficient Reinforcement Learning cites this paper.
Bidirectional Soft Actor-Critic: Leveraging Forward and Reverse KL Divergence for Efficient Reinforcement Learning Improving Policy Gradient by Exploring Under-appreciated Rewards
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:42:34.196122Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.