Paper Citation Record · LEDGER

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2502.06301.

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

pith.paper-citation-record.v1
2502.06301 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:56:12.926094Z

measured 20 of 20 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 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

20 of 20 outbound references displayed

verified exact0
verified fuzzy18
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 99825ef8-090c-42ad-9f17-e31af988d065 · outbound

This paper cites Sutton and Andrew G.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Sutton and Andrew G

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.234331Z

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.

Observation acd655cd-5862-438a-a10f-5ab6d50c0f8e · outbound

This paper cites De Jong.Evolutionary Computation.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning De Jong.Evolutionary Computation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.220588Z

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.

Observation bed0ab23-efaa-4ab3-8e32-fe67734f71a2 · outbound

This paper cites Friedrich Frommann Verlag, Stuttgart-Bad Cannstatt, Germany, 1973.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Friedrich Frommann Verlag, Stuttgart-Bad Cannstatt, Germany, 1973

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.207222Z

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.

Observation 2e8e717b-300c-4bdb-beaa-232bc15c7a36 · outbound

This paper cites Efficacy of modern neuro-evolutionary strategies for continuous control optimization.Fron- tiers in Robotics and AI, 7, 2020.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Efficacy of modern neuro-evolutionary strategies for continuous control optimization.Fron- tiers in Robotics and AI, 7, 2020

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.193006Z

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.

Observation e4327f0b-b6e2-4510-8cb8-56ea0169c517 · outbound

This paper cites Venkate- sha Prasad, and Chris Verhoeven.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Venkate- sha Prasad, and Chris Verhoeven

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.179259Z

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.

Observation 35afc798-f47e-47c9-9627-0e142d3cbfdc · outbound

This paper cites Combining evolution and deep reinforcement learning for policy search: A survey.ACM Trans.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Combining evolution and deep reinforcement learning for policy search: A survey.ACM Trans

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.165163Z

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.

Observation da90fa00-21b6-489a-899f-7d8f10ba2710 · outbound

This paper cites Stanley.Novelty Search and the Problem with Objectives, pages 37–56.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Stanley.Novelty Search and the Problem with Objectives, pages 37–56

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.151288Z

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.

Observation 254c99bd-fc9a-48ad-81b2-548e7ae60978 · outbound

This paper cites an unresolved cited work.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-08T15:56:13.137373Z

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.

Observation a59926a3-93db-426e-945e-7d6f7c2138cb · outbound

This paper cites Pugh, Lisa B.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Pugh, Lisa B

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.123456Z

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.

Observation 7fce9110-a63d-4847-9bf0-42d8b2b15019 · outbound

This paper cites Stanley, and Jeff Clune.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Stanley, and Jeff Clune

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.109513Z

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.

Observation a586f444-8417-4df2-ac6f-8a8f8c87175f · outbound

This paper cites Evolution strategies as a scalable alternative to reinforcement learning.arXiv, 2017.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Evolution strategies as a scalable alternative to reinforcement learning.arXiv, 2017

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.095741Z

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.

Observation 327e997c-3d92-45fb-a96c-f57680b1b3b8 · outbound

This paper cites Attention is all you need.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Attention is all you need

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.081735Z

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.

Observation e3e2bf46-08bd-4ef0-9810-074f58094ce0 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning An image is worth 16x16 words: Transformers for image recognition at scale

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.067905Z

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.

Observation c0604f62-8275-42b2-b8a6-9276a1c4020e · outbound

This paper cites Decision Transformer: Reinforcement Learning via Sequence Modeling.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Decision Transformer: Reinforcement Learning via Sequence Modeling

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T15:56:12.899684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01264edb-e442-4d90-b50a-cb790beaf7e9 · outbound

This paper cites Offline reinforcement learning as one big sequence modeling problem.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Offline reinforcement learning as one big sequence modeling problem

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.052663Z

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.

Observation debfbcc8-090a-4317-8713-6de02721030b · outbound

This paper cites Utilizing evolution strategies to train transformers in reinforcement learning, 2025.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Utilizing evolution strategies to train transformers in reinforcement learning, 2025

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.036347Z

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.

Observation eba538db-f2d9-4f21-a2d6-c32c6d05eac3 · outbound

This paper cites Natural evolution strategies.Journal of Machine Learning Research, 15(27):949–980, 2014.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning Natural evolution strategies.Journal of Machine Learning Research, 15(27):949–980, 2014

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.021648Z

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.

Observation 488e0243-f3fa-484a-9c7c-3b28623e6c7e · outbound

This paper cites First-order and second-order variants of the gradient descent in a unified framework.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning First-order and second-order variants of the gradient descent in a unified framework

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:13.005820Z

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.

Observation ccbb8586-46f9-4dbc-a20c-444390a346c0 · outbound

This paper cites MuJoCo: A physics engine for model-based control.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning MuJoCo: A physics engine for model-based control

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:12.991255Z

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.

Observation 4b9338a6-3053-4c82-b6ac-dfbc52f8276f · outbound

This paper cites OpenAI Gym.arXiv, 06 2016.

Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning OpenAI Gym.arXiv, 06 2016

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:56:12.975389Z

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.

Pith citing papers

No inbound Pith citation observations are available.