Paper Citation Record · LEDGER

Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2405.16158.

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

pith.paper-citation-record.v1
2405.16158 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:23:18.124920Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T22:13:59.938139Z

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 12df5705-2899-420a-b31a-a2d39af4fd04 · inbound

Plasticity Loss in Deep Reinforcement Learning: A Survey cites this paper.

Plasticity Loss in Deep Reinforcement Learning: A Survey Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-23T18:03:18.143673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 56e4cccc-23a8-45ef-814b-01cbfdb222ba · inbound

Hadamax Encoding: Elevating Performance in Model-Free Atari cites this paper.

Hadamax Encoding: Elevating Performance in Model-Free Atari Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T15:23:18.124920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b79eba20-6d7e-4ddb-b599-54112b534780 · 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 Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T12:58:51.287792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d1d9e27-5aed-4b82-a953-88d50f1a3aeb · inbound

The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning cites this paper.

The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:51.919314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06aacba1-9960-461d-af50-c9385de58593 · inbound

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control cites this paper.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.833263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b5582cd-c7f1-41cc-98de-d5323d930bd3 · inbound

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning cites this paper.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T15:55:35.778737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64d31f03-dd55-440d-a529-5d9f1229d620 · inbound

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? cites this paper.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T05:44:15.127132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9206bf9e-eece-4d1f-bee0-19b664cfdc70 · 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 Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:49.961728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ad519fa5-9a53-4e6c-a75f-0e60c6853a49 · 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 Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-19T17:12:41.353864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0023601c-df6c-4044-8a74-e34949ed78c3 · 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 Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:13:59.939901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.