Paper Citation Record · LEDGER

Hyperspherical Normalization for Scalable Deep Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2502.15280.

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

pith.paper-citation-record.v1
2502.15280 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:58:50.269251Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:30:07.802722Z

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 0ac955de-f366-46dd-950e-f7f7e1d1f34f · inbound

Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners cites this paper.

Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T12:58:50.269251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c276368b-ee5a-4e92-a097-6a6b93eaab6a · inbound

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies cites this paper.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T04:39:02.965845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a15785bd-3cd3-434a-b66b-5b95ea1f79b5 · inbound

Use the Online Network If You Can: Towards Fast and Stable Reinforcement Learning cites this paper.

Use the Online Network If You Can: Towards Fast and Stable Reinforcement Learning Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:34:22.302146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1127596-0c19-4a28-8f0e-9d2f0212eca6 · inbound

What Does Flow Matching Bring To TD Learning? cites this paper.

What Does Flow Matching Bring To TD Learning? Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:36:17.937174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f9d073a-5ea2-4f7f-a7d9-3f3f246c324d · inbound

Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments cites this paper.

Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T18:45:22.126026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1e90bed-0e47-43ae-976d-ccae02563f58 · inbound

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control cites this paper.

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:49.887967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0263d68d-081f-44fa-bbf0-7b9ee2f94491 · inbound

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control cites this paper.

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-19T17:12:41.370176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9a5bd423-7a68-40e2-8e89-b8c0846a6298 · inbound

Intentional Updates for Streaming Reinforcement Learning cites this paper.

Intentional Updates for Streaming Reinforcement Learning Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:21:06.697209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4c21db8a-d1d7-4f55-8a8e-e747c6a223b6 · inbound

Extending Differential Temporal Difference Methods for Episodic Problems cites this paper.

Extending Differential Temporal Difference Methods for Episodic Problems Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:35:38.893791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b6b20824-970f-41bd-a8d2-86ba57a37264 · inbound

When Does Non-Uniform Replay Matter in Reinforcement Learning? cites this paper.

When Does Non-Uniform Replay Matter in Reinforcement Learning? Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:36:24.593975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 81011237-dd3a-46cb-af41-3365828d8606 · inbound

When Does Non-Uniform Replay Matter in Reinforcement Learning? cites this paper.

When Does Non-Uniform Replay Matter in Reinforcement Learning? Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:32:24.798324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d68dfb19-ba2f-438e-ada3-8fd0be1eb333 · inbound

When Does Non-Uniform Replay Matter in Reinforcement Learning? cites this paper.

When Does Non-Uniform Replay Matter in Reinforcement Learning? Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:09:12.760062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b9829a60-1ec6-4a0b-8b7f-5eba2d3d05ed · inbound

EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control cites this paper.

EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:03:39.745561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d02e5389-a42f-4330-bad1-8b3e78c7f3a7 · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 179

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:30:07.804404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa141ece-641a-460d-a3f9-086b1f930be3 · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-02T10:14:08.476965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfa1387d-f957-4d8f-83a9-a6d4ab728197 · inbound

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping cites this paper.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-07-31T23:37:36.767776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.