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Paper Citation Record · LEDGER

How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.10093.

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

pith.paper-citation-record.v1
2410.10093 v1

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

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:16:31.933188Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T16:34:25.466718Z

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 a4256de8-453d-45da-95c8-e554d4f05985 · inbound

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment cites this paper.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-11T12:16:31.933188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:16:31.933188Z digest=sha256:f7b1a402c34f3cbc2537b3f7ff0e908921e407557aeba5f65aebac8e00379762

Observation b81d8143-0d29-4284-9d02-6ba928991711 · inbound

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities cites this paper.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective

Reference 100

Resolution
verified exact
local_arxiv, observed 2026-08-06T16:34:25.472318Z

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.

source=pdf_text observed=2026-08-06T16:34:25.246579Z digest=sha256:61b9f07ac6f6e23d95fc0785db756ec324f59ffb1f79d9a878a23c1e73d7fc90