Paper Citation Record · LEDGER

RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning

As of 6 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 1 inbound Pith citation observation for arXiv:2601.23075.

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

pith.paper-citation-record.v1
2601.23075 v2

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T06:26:50.470669Z

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T02:55:22.577012Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T12:46:27.898232Z

Reference resolution

6 of 6 outbound references displayed

verified exact0
verified fuzzy0
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 96a2db2e-3d93-434d-9532-4c0d803f1999 · outbound

This paper cites mean).Let πc(a|s) =N(µ,Σ) with fixed diagonal Σ = Diag(σ2) and parameter µ∈R m.

RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning mean).Let πc(a|s) =N(µ,Σ) with fixed diagonal Σ = Diag(σ2) and parameter µ∈R m

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T06:26:50.403340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c458954-539e-488f-a63b-79c5db8e1de3 · outbound

This paper cites logits).Consider one action dimension i with logits zi ∈R K and softmax probabilities pi = softmax(zi)∈∆ K−1.

RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning logits).Consider one action dimension i with logits zi ∈R K and softmax probabilities pi = softmax(zi)∈∆ K−1

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T06:26:50.470669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 789c6ba2-c50b-4d84-b762-c08c2fed956e · outbound

This paper cites SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning.

RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

Reference 456

Resolution: unresolved; no resolver link, observed 2026-08-03T06:26:49.906939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e14a882-24c4-442f-8c64-ba290c397f12 · outbound

This paper cites Stop Regressing: Training Value Functions via Classification for Scalable Deep RL.

RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Reference 843

Resolution: unresolved; no resolver link, observed 2026-08-03T06:26:49.758326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b865dce-dccd-424b-8539-56b73b144627 · outbound

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

RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners

Reference 1937

Resolution: unresolved; no resolver link, observed 2026-08-03T06:26:50.060301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e0da62e-0158-4e00-bb8b-2e0aa0c9eeb8 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T06:26:50.223064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 4cd33387-6be2-4374-aaf0-7d4ee76a0ec4 · inbound

Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning cites this paper.

Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-25T01:17:49.776384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.