Paper Citation Record · LEDGER

Scaling laws for single-agent reinforcement learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2301.13442.

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

pith.paper-citation-record.v1
2301.13442 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:29:11.556414Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 40b1fb08-4f1f-42ab-9041-889536ce8130 · inbound

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution cites this paper.

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Scaling laws for single-agent reinforcement learning

Reference 148

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T08:12:31.592293Z

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 5aac830d-c546-4b4f-ac10-3be925a83dfc · inbound

Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions cites this paper.

Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions Scaling laws for single-agent reinforcement learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T20:29:11.556414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60cc0364-9d78-44e9-bb00-880a11a28b55 · 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 Scaling laws for single-agent reinforcement learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T04:39:02.461192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 566d06b2-756d-4ddd-b01d-287f561868cf · inbound

Model Merging Scaling Laws in Large Language Models cites this paper.

Model Merging Scaling Laws in Large Language Models Scaling laws for single-agent reinforcement learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:32:37.794249Z

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 23e6dcbc-c46c-4c9a-bfac-ee5c337af806 · 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 Scaling laws for single-agent reinforcement learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T18:45:21.203626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5179eea0-893f-434c-acd1-06dd1f40570e · inbound

On Training in Imagination cites this paper.

On Training in Imagination Scaling laws for single-agent reinforcement learning

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T04:55:56.705882Z

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 d6b00741-3339-4602-9ad4-4559c4a0e127 · inbound

On Training in Imagination cites this paper.

On Training in Imagination Scaling laws for single-agent reinforcement learning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:37:03.484971Z

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 719437c9-6b3a-4e62-b126-ab6bf2194b83 · inbound

Unified Neural Scaling Laws cites this paper.

Unified Neural Scaling Laws Scaling laws for single-agent reinforcement learning

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:44:03.368224Z

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 60471cb9-54fe-406e-90a7-7dbd116c19a2 · inbound

Scaling Laws for Neural-Network Quantum States cites this paper.

Scaling Laws for Neural-Network Quantum States Scaling laws for single-agent reinforcement learning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:46:27.086532Z

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 e7add9ab-605a-4f42-9e41-d0937d3fd7a8 · inbound

Two AI Metrics Diverged: Will it Make All the Difference? cites this paper.

Two AI Metrics Diverged: Will it Make All the Difference? Scaling laws for single-agent reinforcement learning

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:36:56.160695Z

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 f0b87a0e-a477-4d46-9509-6158d096558e · inbound

Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RL cites this paper.

Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RL Scaling laws for single-agent reinforcement learning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:08:57.779100Z

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 7e2a44c4-6fe0-4a55-b6d8-c27c0d7967b2 · inbound

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments cites this paper.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Scaling laws for single-agent reinforcement learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.