Pith. sign in

Paper Citation Record · LEDGER

On Reward-Free Reinforcement Learning with Linear Function Approximation

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2006.11274.

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

pith.paper-citation-record.v1
2006.11274 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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T20:42:07.966293Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T16:46:05.269563Z

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 71452cbf-5e6d-4c22-ae29-9827f29452c3 · inbound

Multi-agent imitation learning with function approximation: Linear Markov games and beyond cites this paper.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond On Reward-Free Reinforcement Learning with Linear Function Approximation

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-02T20:42:07.966293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T20:42:07.966293Z digest=sha256:b631dab9284869d12bf7deb1d7da9369a7c1e0594b1f781f2fd7da68cab56e88

Observation af7bfae2-95c5-4990-9f75-9fa2ac215260 · inbound

Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs cites this paper.

Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs On Reward-Free Reinforcement Learning with Linear Function Approximation

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:46:05.276253Z

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.

source=arxiv_source observed=2026-05-09T15:12:54.575483Z digest=sha256:b2e8c7fe6fb96026bb29fb204b8dad0da15f169d5f204d9fdf579c7047475d95