Paper Citation Record · LEDGER

Multiple-Frequencies Population-Based Training

As of 20 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2506.03225.

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

pith.paper-citation-record.v1
2506.03225 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:22:12.191394Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact5
verified fuzzy14
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9277282f-542e-4949-87fa-50add0e2b1ac · outbound

This paper cites Deep reinforcement learning at the edge of the statistical precipice.

Multiple-Frequencies Population-Based Training Deep reinforcement learning at the edge of the statistical precipice

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.932235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 59b55995-4a28-4940-9041-d773b0ce19d3 · outbound

This paper cites Dehb: Evolutionary hyberband for scalable, robust and efficient hyperparameter optimization.

Multiple-Frequencies Population-Based Training Dehb: Evolutionary hyberband for scalable, robust and efficient hyperparameter optimization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.034156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf93739c-4af3-468c-a504-6208059fc2b5 · outbound

This paper cites Agent57: Outperforming the A tari human benchmark.

Multiple-Frequencies Population-Based Training Agent57: Outperforming the A tari human benchmark

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.910733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 584f88fa-b6c0-49a9-849d-eec523cf022b · outbound

This paper cites Random search for hyper-parameter optimization.

Multiple-Frequencies Population-Based Training Random search for hyper-parameter optimization

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.890376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 97d3da53-db5d-4d90-8aa0-effb3a915dde · outbound

This paper cites Algorithms for hyper-parameter optimization.

Multiple-Frequencies Population-Based Training Algorithms for hyper-parameter optimization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.049199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a838f0a9-4dc9-4c09-a9aa-937fb6badf3c · outbound

This paper cites JAX : composable transformations of P ython+ N um P y programs, 2018.

Multiple-Frequencies Population-Based Training JAX : composable transformations of P ython+ N um P y programs, 2018

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.054039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8156596f-6c89-4e73-88de-d91278db7eb1 · outbound

This paper cites Robust Autonomy Emerges from Self-Play.

Multiple-Frequencies Population-Based Training Robust Autonomy Emerges from Self-Play

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.059284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 488b3ef7-c3ea-40be-9b9b-6a7a06d0a2ef · outbound

This paper cites Faster Improvement Rate Population Based Training.

Multiple-Frequencies Population-Based Training Faster Improvement Rate Population Based Training

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.064031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f70428d4-4c1f-4fd6-873b-3c723261e8dd · outbound

This paper cites Hyperparameters in reinforcement learning and how to tune them.

Multiple-Frequencies Population-Based Training Hyperparameters in reinforcement learning and how to tune them

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.851255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 512a7e88-7906-458c-aa6d-4dc6ab7cd833 · outbound

This paper cites BOHB : Robust and efficient hyperparameter optimization at scale.

Multiple-Frequencies Population-Based Training BOHB : Robust and efficient hyperparameter optimization at scale

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.829756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9a6c291d-752e-4ce4-88ec-3d86c3577638 · outbound

This paper cites Hyperparameter optimization.

Multiple-Frequencies Population-Based Training Hyperparameter optimization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.805967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f47feaee-f810-456a-8799-737908e2dd41 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

Multiple-Frequencies Population-Based Training Model-agnostic meta-learning for fast adaptation of deep networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.789744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ebc7eed6-7e75-490d-bd20-492ab42f7ba7 · outbound

This paper cites Franke, Gregor Koehler, Andr \'e Biedenkapp, and Frank Hutter.

Multiple-Frequencies Population-Based Training Franke, Gregor Koehler, Andr \'e Biedenkapp, and Frank Hutter

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.772781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7df6aecc-dac8-4164-9830-d5316aaed0ee · outbound

This paper cites Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, and Olivier Bachem.

Multiple-Frequencies Population-Based Training Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, and Olivier Bachem

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.755390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eee2772b-4f44-46da-ace5-bba2560cb11f · outbound

This paper cites Deep reinforcement learning that matters.

Multiple-Frequencies Population-Based Training Deep reinforcement learning that matters

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.097718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09db5e96-ed3d-4cf2-870f-81fd6d1fe03d · outbound

This paper cites Population Based Training of Neural Networks.

Multiple-Frequencies Population-Based Training Population Based Training of Neural Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.102205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 633d8309-c3bb-494c-aee3-3331cdd71403 · outbound

This paper cites Hyperband: A novel bandit-based approach to hyperparameter optimization.

Multiple-Frequencies Population-Based Training Hyperband: A novel bandit-based approach to hyperparameter optimization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.719967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 138bc37d-9e79-4c80-a584-c34ff91f96ea · outbound

This paper cites an unresolved cited work.

Multiple-Frequencies Population-Based Training Unresolved cited work

Reference 18

Resolution: verified exact; doi, observed 2026-08-07T11:22:12.311519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8a51ca2d-c154-4c05-b8b9-d412311751e1 · outbound

This paper cites Provably efficient online hyperparameter optimization with population-based bandits.

Multiple-Frequencies Population-Based Training Provably efficient online hyperparameter optimization with population-based bandits

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.701548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5428cee0-1fef-4dfc-ae09-ab2b66bbbe4a · outbound

This paper cites Tuning mixed input hyperparameters on the fly for efficient population based autorl.

Multiple-Frequencies Population-Based Training Tuning mixed input hyperparameters on the fly for efficient population based autorl

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.683373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0cdee8c1-9d52-4f8a-9f36-b939b85cd4b6 · outbound

This paper cites Automated reinforcement learning (autorl): A survey and open problems.

Multiple-Frequencies Population-Based Training Automated reinforcement learning (autorl): A survey and open problems

Reference 21

Resolution: verified exact; doi, observed 2026-08-07T11:22:12.294513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 60daa1b4-3e28-40c1-94fc-ffb681da7ae3 · outbound

This paper cites Policy Distillation.

Multiple-Frequencies Population-Based Training Policy Distillation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.136807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11e0e02d-e09d-4154-ae2b-520dfbb0b25f · outbound

This paper cites Proximal Policy Optimization Algorithms.

Multiple-Frequencies Population-Based Training Proximal Policy Optimization Algorithms

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.142290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76e417fe-20a9-4866-bc0c-08dba7d9d4fa · outbound

This paper cites an unresolved cited work.

Multiple-Frequencies Population-Based Training Unresolved cited work

Reference 24

Resolution: verified exact; doi, observed 2026-08-07T11:22:12.277715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 387628c5-e65f-4168-9beb-a1827e040875 · outbound

This paper cites an unresolved cited work.

Multiple-Frequencies Population-Based Training Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.153426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51e6b5d6-6b16-4642-8a87-d472fa8029d2 · outbound

This paper cites V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control.

Multiple-Frequencies Population-Based Training V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.160191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfb20547-870a-4469-9b27-cb9fd12c7e77 · outbound

This paper cites Adapting Crossover in Evolutionary Algorithms.

Multiple-Frequencies Population-Based Training Adapting Crossover in Evolutionary Algorithms

Reference 27

Resolution: verified exact; doi, observed 2026-08-07T11:22:12.244917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2a3e4fe4-b97e-4b1d-a182-dfb90cf35ea5 · outbound

This paper cites Sutton and Andrew G.

Multiple-Frequencies Population-Based Training Sutton and Andrew G

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.171604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7fb554d-1622-48cb-8b00-3ee226a28729 · outbound

This paper cites Ball, Vu Nguyen, Binxin Ru, and Michael Osborne.

Multiple-Frequencies Population-Based Training Ball, Vu Nguyen, Binxin Ru, and Michael Osborne

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.656346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0ad8d103-808f-494b-b1fc-392a78be557a · outbound

This paper cites Meta-gradient reinforcement learning.

Multiple-Frequencies Population-Based Training Meta-gradient reinforcement learning

Reference 30

Resolution: verified exact; raw_fallback, observed 2026-08-07T11:22:12.492116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6a90400e-f386-479e-b627-0947d4a4976d · outbound

This paper cites On the importance of hyperparameter optimization for model-based reinforcement learning.

Multiple-Frequencies Population-Based Training On the importance of hyperparameter optimization for model-based reinforcement learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:22:12.640169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 07d74611-f451-42cc-bcea-cf63da2eab88 · outbound

This paper cites write newline.

Multiple-Frequencies Population-Based Training write newline

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:12.191394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.