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Paper Citation Record · LEDGER

SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2401.16013.

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

pith.paper-citation-record.v1
2401.16013 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:27:55.390069Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T15:48:35.804876Z

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 d991f1a3-6ec4-42a9-b978-4fe163b283cb · inbound

Diffusion Policy Policy Optimization cites this paper.

Diffusion Policy Policy Optimization SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

Reference 57

Resolution
verified exact
arxiv_id, observed 2026-05-16T08:48:14.947648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-16T08:48:14.776754Z digest=sha256:5edaa33ba11f4df76f47da516c08c3c2f5db68be861fcf915a17c260db6abad7

Observation 17ad71ac-ea60-4e13-b93e-b56ce8ac5c77 · inbound

Fabrica: Dual-Arm Assembly of General Multi-Part Objects via Integrated Planning and Learning cites this paper.

Fabrica: Dual-Arm Assembly of General Multi-Part Objects via Integrated Planning and Learning SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-07T10:27:55.390069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:27:55.390069Z digest=sha256:3a24d5fa859e97967c2d2622870abccc63db5870c677adfb9b29810e09b6cf55

Observation ef2f7635-d2b4-47f7-8b4f-41571e71225a · inbound

mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity cites this paper.

mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T01:07:50.046835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:07:50.046835Z digest=sha256:96f3d7f02b215ab33126cb95181cabe1cb95741edc4dc6707c28730939d49ad6

Observation 2f6c16c4-2070-4b18-99bd-b7491fe0df93 · inbound

Robust Peg-in-Hole Assembly under Uncertainties via Compliant and Interactive Contact-Rich Manipulation cites this paper.

Robust Peg-in-Hole Assembly under Uncertainties via Compliant and Interactive Contact-Rich Manipulation SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T22:08:35.969977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:08:35.969977Z digest=sha256:e01142349b16145f267065291729311ce7d2787bf6e3a4cb7fc4501838434c1d

Observation 9e3790b2-9ba3-4b4f-b199-b332c6ae941e · inbound

SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training cites this paper.

SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T19:53:06.162466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:53:06.162466Z digest=sha256:0175600c47c8635a923fdf350f15b6ea8acaedad8b82d05a3bc6c002e65cd0d0

Observation 257f1160-2502-46af-9abf-89bc6d9b1436 · inbound

Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation cites this paper.

Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-02T20:06:55.965466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T20:06:55.965466Z digest=sha256:20c8bb2ea5cc901e3d648036322ada72c0e249a8e801a0cde40ce94a10536881

Observation d0624eba-5175-4d75-9c5b-4bd98e51c873 · inbound

EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models cites this paper.

EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-06-29T22:13:59.906708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-29T22:10:08.682307Z digest=sha256:20a40999d10c1d62bbfc40d81823e858ea91a778a445e83368bb6a6206b23ab1

Observation 0f9bfae4-41d1-4474-92fd-578acbd6ac4a · inbound

Improving Robotic Generalist Policies via Flow Reversal Steering cites this paper.

Improving Robotic Generalist Policies via Flow Reversal Steering SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

Reference 86

Resolution
verified exact
arxiv_id, observed 2026-07-03T15:48:35.806256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-27T06:20:19.209180Z digest=sha256:f8aec53b2af90c73597750fab38ff97fc14c55db428eceb73dcdcd8871f39c62