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Paper Citation Record · LEDGER

Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2305.10282.

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

pith.paper-citation-record.v1
2305.10282 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T06:03:45.594050Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T21:34:31.000857Z

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 ad6cfff7-faae-4f86-a40c-86a473a6e2cf · inbound

Balancing optimism and pessimism in offline-to-online learning cites this paper.

Balancing optimism and pessimism in offline-to-online learning Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-08T06:03:45.594050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T06:03:45.594050Z digest=sha256:2a6a9867e9c4304f3bae19d08fbf5dee5bd5df3167df9f7f816c9a736bb9d420

Observation 412ddb58-3fe5-4cfd-84a0-dfdd76f85eca · inbound

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods cites this paper.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning

Reference 2013

Resolution
verified exact
local_arxiv, observed 2026-08-05T21:34:31.049836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:34:29.418727Z digest=sha256:a1802701cb6a359768227958dd92f82db2c37a416ddf6d349f0c19f9ecdb02f1