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Paper Citation Record · LEDGER

Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 inbound Pith citation observations for arXiv:2005.12729.

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

pith.paper-citation-record.v1
2005.12729 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:15:46.288418Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T12:09:48.784830Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d1e461ff-bee6-4969-857b-70a087d36390 · inbound

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment cites this paper.

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 94

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arxiv_id, observed 2026-05-18T00:46:56.870644Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-18T00:46:56.664582Z digest=sha256:d6f4ab38629a08f87fe71d03ccc4ea3e4d7a290b89e3d2e235deef20fbcb15d2

Observation 6320458f-5f51-4b32-b4d5-70cbfc97563a · inbound

Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents cites this paper.

Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 208

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arxiv_id, observed 2026-05-20T09:42:04.375198Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-20T09:41:59.979595Z digest=sha256:dca2417fe5bf570acf4717de6091d0cfef4e59ac152c0e2d910761fc014638fd

Observation dc0ec6a0-0fe7-4e1f-8560-d83ff9f57534 · inbound

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? cites this paper.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 24

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source=pdf_text observed=2026-08-07T15:15:46.288418Z digest=sha256:979b0a938749dfab210e15ff14d522e182a2945bf5a995cb12f24b7c545d932c

Observation 19324a54-6076-42a7-9974-d420050cd73f · inbound

Thompson Sampling in Online RLHF with General Function Approximation cites this paper.

Thompson Sampling in Online RLHF with General Function Approximation Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 12

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source=pdf_text observed=2026-08-07T12:43:47.833667Z digest=sha256:0c9b929e42acbf39d1841d3369f4cced0de321e13f3899aae11d114eb7c20c7c

Observation ca9784f2-02ca-4061-ad86-be0546e474fc · inbound

On the Effect of Regularization in Policy Mirror Descent cites this paper.

On the Effect of Regularization in Policy Mirror Descent Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 11

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source=arxiv_source observed=2026-08-06T18:18:45.097844Z digest=sha256:7eea057009bff4c51bdeb308fd976e74c300e775c5780248db33020b92b17f55

Observation b798d612-57e9-4f25-9a09-a704bf31f366 · inbound

Shared Control of Holonomic Wheelchairs through Reinforcement Learning cites this paper.

Shared Control of Holonomic Wheelchairs through Reinforcement Learning Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 30

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source=pdf_text observed=2026-08-06T15:01:56.164569Z digest=sha256:6f0d81279745beb354f383d32400b20bcd1ec89ed3192cc1abe60c837e3922ac

Observation 96838120-0c29-41d2-b247-d6365188a7d3 · inbound

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges cites this paper.

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 216

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source=arxiv_source observed=2026-08-06T14:13:07.089075Z digest=sha256:a5cc0c1f9d27aac0830eccf64bbad99f05a984f73b5f57582b03270d222a694f

Observation 44e20981-aeb7-4aa2-b449-20b1aaf520ec · inbound

SERA: Soft-Verified Efficient Repository Agents cites this paper.

SERA: Soft-Verified Efficient Repository Agents Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 6

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source=pdf_text observed=2026-08-03T07:17:13.398189Z digest=sha256:d112d56313da3d13af64be9d85e0f760c89d0493a34752f6720630acd8b91ce6

Observation 43518c25-4e3d-4773-8eda-17dedb3c887c · 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 Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 13

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source=arxiv_source observed=2026-08-02T18:45:20.537597Z digest=sha256:b8d24d2b161df448d6deec2f596132419f6c66f7e3b130bf7a9ec60c656ee2b3

Observation c9d43959-881d-43c0-9145-44e658aec8e0 · inbound

Bounded Ratio Reinforcement Learning cites this paper.

Bounded Ratio Reinforcement Learning Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 5

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arxiv_id, observed 2026-05-10T11:35:19.053807Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T04:50:11.020901Z digest=sha256:6a41066c22444d9f7c9a502afdc0daca1258aab1344f2e695bac0b246c1610ed

Observation 27c12819-cd74-4918-8b56-c044b86bbd6f · inbound

Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems cites this paper.

Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 44

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arxiv_id, observed 2026-05-12T00:41:16.486953Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-07T14:51:30.263897Z digest=sha256:f528019c4f4f246493bf4ff040b730ec5633b59b58976cd40c36d659e96ca4ce

Observation 07e7e605-660a-40c8-beec-f7d10bbdfcb8 · inbound

Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems cites this paper.

Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 44

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arxiv_id, observed 2026-05-19T17:47:41.626886Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T17:46:20.907461Z digest=sha256:88cd45ed77fdb5ce2268e4c4aa3e837dd17278f87e92611fdb309cbb668440c9

Observation 4a49f367-01c8-4aa0-abed-8c07c071b344 · inbound

ANO: A Principled Approach to Robust Policy Optimization cites this paper.

ANO: A Principled Approach to Robust Policy Optimization Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 6

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arxiv_id, observed 2026-05-09T05:45:23.303091Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T19:34:12.002351Z digest=sha256:2da24bf42e8418ccb363cc37d471bba36da0d3a9ed93795d18952dbc033e8649

Observation 52dd2441-8816-4fb8-af6a-aacbb3fc3bea · inbound

Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters cites this paper.

Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 254

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arxiv_id, observed 2026-05-11T18:41:12.454247Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-08T14:16:34.235992Z digest=sha256:86a2da5549434b00c42c32dc675c8c11e2a8cf7d2de518ae592a2fbad5128e81

Observation bb983171-0d8d-45a4-be46-b146e61802ae · inbound

Response Time Enhances Alignment with Heterogeneous Preferences cites this paper.

Response Time Enhances Alignment with Heterogeneous Preferences Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 66

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arxiv_id, observed 2026-05-11T04:46:00.141867Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-11T01:04:26.288913Z digest=sha256:9d2bbd0caf8f2dbb07c82eaa41c5065a3dc0547b2990b0b75488311da16b9ea4

Observation 5cdcc69b-7f56-4ea8-828b-6657dc5f04f9 · inbound

TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing cites this paper.

TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 4

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arxiv_id, observed 2026-05-13T01:52:05.738887Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T01:48:13.679862Z digest=sha256:82a6bf01dce63fb03fbf8eb39a1fe5d84f2611e2fd210348cea2ec875875efcf

Observation 34485d47-4743-4400-a09d-9c895fef8c0d · inbound

Ratio-Variance Regularized Policy Optimization cites this paper.

Ratio-Variance Regularized Policy Optimization Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 3

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arxiv_id, observed 2026-06-29T19:53:55.985787Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T19:44:02.317303Z digest=sha256:1b38554bd9d30c6cecd3eb36c1d10379cba52cbab4ad24b5edd6963f0a1a31e9

Observation f6af7c43-22eb-45ff-a1b1-9f8337fb0d18 · inbound

Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity cites this paper.

Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 25

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source=pdf_text observed=2026-07-12T23:02:52.079919Z digest=sha256:d304362beeb97e00c2c9a6ead2ad18698d1ec3c9f3368152b12018203100a990

Observation cf90a442-801c-4282-9a38-7337e7bc4607 · inbound

Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning cites this paper.

Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 13

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arxiv_id, observed 2026-07-02T06:06:41.426638Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T07:38:03.222413Z digest=sha256:39852be11aa18dddb891d1297c4a0ae894c1785e2f974cf319a80ad547cfd280

Observation 316ce026-771d-4e4e-b3c9-692453b5f899 · inbound

Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning cites this paper.

Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 34

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arxiv_id, observed 2026-07-03T16:28:38.903131Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T05:42:37.031721Z digest=sha256:72b679bfafda7431c7573e32a30fb7eebfff3d52a06d9177dfa3708c1b294f2d

Observation c43b365a-919c-42ee-9c54-d17a1c1fdd92 · inbound

LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN cites this paper.

LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 45

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arxiv_id, observed 2026-07-04T12:09:48.786465Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T07:14:40.706395Z digest=sha256:1d669bfdbc709604d9ff1805d441dac1aa2a0e2644b98d9150bdab27442a9174

Observation fe19167d-f4a3-4b44-9437-b41ed7f7b555 · inbound

Understanding electricity consumption behaviour through Inverse Reinforcement Learning cites this paper.

Understanding electricity consumption behaviour through Inverse Reinforcement Learning Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 59

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source=pdf_text observed=2026-07-12T04:21:03.464973Z digest=sha256:be2942c5ba0bb99a1d28a9bf58f2e133a610e6f59dfed725e72773719ac72e30

Observation 47e40277-b8b8-483d-a3a0-2a33dbbe64d8 · inbound

Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 147

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source=arxiv_source observed=2026-07-11T13:53:36.775836Z digest=sha256:04ceb6d56e0ad55d0d218e0ee89ee0c3399be22d1ca3de7b45a5edf95a31ec40

Observation 0253a4f8-8e1d-4aab-85cf-946b64d8577d · inbound

Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 148

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source=arxiv_source observed=2026-08-02T08:40:48.552061Z digest=sha256:2e0668c87a23ad931873aa3ea9fe5db87664d368cbf86bffa74987d534857ef0

Observation 40ce625e-4d6b-4884-b8bc-3a84d544f609 · inbound

ODYSSE: Episode-wise Policy Optimization for Personalized Agentic Reasoning cites this paper.

ODYSSE: Episode-wise Policy Optimization for Personalized Agentic Reasoning Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 10

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source=pdf_text observed=2026-08-01T02:44:30.092180Z digest=sha256:c349ba54fa6de949ba4e6ac9fca137f5635f2515a0fea1836e265ca11840ff1b