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Paper Citation Record · LEDGER

Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2409.17401.

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

pith.paper-citation-record.v1
2409.17401 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:41:39.597198Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:08:43.686216Z

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 70bfd106-b668-4668-8b44-1f90a18f919f · inbound

ElasticZO: A Memory-Efficient On-Device Learning with Combined Zeroth- and First-Order Optimization cites this paper.

ElasticZO: A Memory-Efficient On-Device Learning with Combined Zeroth- and First-Order Optimization Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-10T21:41:39.597198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:41:39.597198Z digest=sha256:94be73ad98c426438654f831498dfb0689584f2f25187a4ba5f75256ec6ef7ee

Observation fd4d33d2-afbe-4190-931c-843e0bbb0e49 · inbound

Distributed primal-dual algorithm for constrained multi-agent reinforcement learning under coupled policies cites this paper.

Distributed primal-dual algorithm for constrained multi-agent reinforcement learning under coupled policies Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-03T21:36:01.483861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T21:36:01.483861Z digest=sha256:22655acc45b941f6c2213ad6b488b82e07da4ba84e34b459b09c9c95d8672707

Observation 6aa46c19-5430-4ba7-a8ed-971f3a9aba5a · inbound

AI-Driven Stabilization in Power Grids through Controlling Line Admittances cites this paper.

AI-Driven Stabilization in Power Grids through Controlling Line Admittances Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-03T12:46:01.287650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T12:46:01.287650Z digest=sha256:7571c4fbdafaf55bab4a2207bedcbe86562322df4eb3c761387e78793fa478a7

Observation f1fa882c-a460-4bbc-aeb4-701505604e1d · inbound

Policy Gradient Primal-Dual Method for Safe Reinforcement Learning from Human Feedback cites this paper.

Policy Gradient Primal-Dual Method for Safe Reinforcement Learning from Human Feedback Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference

Reference 11

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T13:06:03.935234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-10T02:23:20.208976Z digest=sha256:c09a8f3594d20ec00501f4c0db155ef19d91fb4cb221edd9c3f7457d0a0bdf7c

Observation bc84b6c3-59a8-48a0-8caa-ad1fa77ff559 · inbound

Distributed Zeroth-Order Policy Gradient for Networked Multi-agent Reinforcement Learning from Human Feedback cites this paper.

Distributed Zeroth-Order Policy Gradient for Networked Multi-agent Reinforcement Learning from Human Feedback Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-19T19:22:44.697738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-19T19:21:53.659764Z digest=sha256:e6bd41f5ef0f6452d591c44c20e2f1f6545a6c630d257a1792f60ae76b4e0dc5

Observation dbbfb27b-d55a-4fb3-971b-153a94fcef5c · inbound

Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning cites this paper.

Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference

Reference 18

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T17:08:43.687705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-06-27T04:33:10.554853Z digest=sha256:3bb7058b3d4db2e4aa649c129866beb45f0bc12365e1b83bd3eb2351fbde0da4