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Paper Citation Record · LEDGER

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

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 34 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

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Source: paper_references, paper_reference_links

measured 34 of 34 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 34 of 34 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:13:53.530577Z

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

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0 of 0 outbound references displayed

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Outbound references

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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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-18T00:46:56.664582Z digest=sha256:95e860b2518f7b81f26b0aa644ee9527a57f46aaeb06e8f12aa76f5e19dc6672

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-20T09:41:59.979595Z digest=sha256:c224b80921755999e19048f47603d330716f155b70cd5797228e235fb9983e5c

Observation 976af50a-b1ce-43e3-ae7c-3b3b41d7c76b · inbound

DSTC: Direct Preference Learning with Only Self-Generated Tests and Code to Improve Code LMs cites this paper.

DSTC: Direct Preference Learning with Only Self-Generated Tests and Code to Improve Code LMs Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 11

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source=arxiv_source observed=2026-08-12T17:05:15.103259Z digest=sha256:09e05b457612c57d23674f4e27e4bd979c2bd475da7e733ba358baa4b5d5a41a

Observation de21805e-f23c-4299-8bff-fc4190a4dbd9 · inbound

Self-reconfiguration Strategies for Space-distributed Spacecraft cites this paper.

Self-reconfiguration Strategies for Space-distributed Spacecraft Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 19

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source=pdf_text observed=2026-08-12T12:33:32.145779Z digest=sha256:ff1338a6d44712cde92c51fc637992863367d1a9bc541c944aeb8f2499a4aa79

Observation c7ca3801-c8ff-4f8e-a3be-145a2f85703a · inbound

Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps cites this paper.

Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 5

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source=pdf_text observed=2026-08-11T05:51:10.143723Z digest=sha256:97f6851e0eb6dfa66061eedf6d0b8f926cdc5bc35883c68c7a8ffc06c3f31bc6

Observation 32c84664-64b6-4e43-9ace-e2025aed1d76 · inbound

PPO-Q: Proximal Policy Optimization with Parametrized Quantum Policies or Values cites this paper.

PPO-Q: Proximal Policy Optimization with Parametrized Quantum Policies or Values Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 65

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source=pdf_text observed=2026-08-10T20:53:52.596341Z digest=sha256:ab0a15338b7f558c24693464335e75a382f8961530037d7d55a1b428ee5e01b7

Observation 3522272c-5d93-4a4e-b9e6-77d9c2c39948 · inbound

Evolution and The Knightian Blindspot of Machine Learning cites this paper.

Evolution and The Knightian Blindspot of Machine Learning Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 48

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source=pdf_text observed=2026-08-10T16:30:09.580796Z digest=sha256:aab54734809868237cfab68b9ceb3efac7a282097125f66718b8d018cf437cd1

Observation 8161ca46-facc-4825-bec4-0d5aa31e7eed · inbound

Learning Explainable Dense Reward Shapes via Bayesian Optimization cites this paper.

Learning Explainable Dense Reward Shapes via Bayesian Optimization Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 19

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source=arxiv_source observed=2026-08-16T11:13:53.530577Z digest=sha256:e61c7de756bb80fccfea7dc58f2c5c364691f61ac851e8bd06be2bc004b65cb2

Observation 76df687c-b747-47bb-a911-6ea3f7a8c79d · inbound

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance cites this paper.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 32

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source=pdf_text observed=2026-08-16T10:14:54.414297Z digest=sha256:4bf3105b9cad35521f1b4b2306f5533b862f095460fcd95f9dbf24703985b577

Observation a5693dd3-b148-488b-8ab7-57553632e80f · inbound

A Survey on Progress in LLM Alignment from the Perspective of Reward Design cites this paper.

A Survey on Progress in LLM Alignment from the Perspective of Reward Design Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 73

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source=pdf_text observed=2026-08-16T00:52:06.789472Z digest=sha256:23fd6be728b236bec2b7e6cb93976fe1cdf102f354646c0a2dfa096c35a797ac

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:f89930239afec25931b65c6d7d5139e18cf615d5ac53c9768aa23024c66e9207

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=sha256:6b3fa7bd943fc05bfea92c562c7a14f97dd0c3ef9c436272d9796076addbc3b2

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=sha256:40bdf55008f8ff954165c590680d9de9cc6a991506c978fee59caf39f3ec0508

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=sha256:36133060e1065f2fb55aa01ca05a7165d683af39d785c10716283b5104a3d2d4

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:fa0294492579bdfe3db6cf5f67d6b783cfc8ec814737931daf83de94058f1ed8

Observation 3cc5cde8-e828-4b8c-a869-f4ff5772a7b0 · inbound

Greener Deep Reinforcement Learning: Analysis of Energy and Carbon Efficiency Across Atari Benchmarks cites this paper.

Greener Deep Reinforcement Learning: Analysis of Energy and Carbon Efficiency Across Atari Benchmarks Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 15

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source=pdf_text observed=2026-08-15T16:30:00.976208Z digest=sha256:6287e2e4f89556567b763714d992a784509b99463ea8f3352231890a26db5b20

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=sha256:9fc5b3e26688fee5f1cf89ac72dc4115fd6a1df37ef8a260885a0aca912c75f2

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=sha256:4a359555722aa66e69c851bfd7195809ed83586056ea526c4791607d766f9d23

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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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-10T04:50:11.020901Z digest=sha256:2dd6d7268f4466618160c4c8312bb756aa595de24a9964a57e35432c51912702

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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source=pdf_text observed=2026-05-07T14:51:30.263897Z digest=sha256:d75683ba7a32618e57f0ec9d1a3c0b20ea73cc8ddeab30e94b2a9ffb3d2a2a64

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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source=pdf_text observed=2026-05-19T17:46:20.907461Z digest=sha256:3773072883ad27661a6adf3c6fdf419252d782161bf657c18f9885eab988153c

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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source=pdf_text observed=2026-05-08T19:34:12.002351Z digest=sha256:897bcfcc7b2dbf514fb91951b3c594f08332e15dee012fa9fde1fd7ef52934c8

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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source=arxiv_source observed=2026-05-08T14:16:34.235992Z digest=sha256:06e19b0bf3ac4d177eb483d1ea5b4043a1f3f937f275e4e22dd76d190d26e89c

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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source=arxiv_source observed=2026-05-11T01:04:26.288913Z digest=sha256:4d5034e5855f076e2224a519b4a3771924ae847adb938d932dc4bbeaa3df0979

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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source=pdf_text observed=2026-05-13T01:48:13.679862Z digest=sha256:351f91ea8653e58e7a516a5478a27eb78e027b7a69e25d294f2917de3f7547da

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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source=pdf_text observed=2026-06-29T19:44:02.317303Z digest=sha256:1ea3e929f35e760bb074f261adcf31675f28e1550491c0929bca5aa1b4d255a5

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=sha256:7c59a15fd10364e71cba038d965d6b84cd758bd360c0c2e4b11080b6765d9e5d

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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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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source=pdf_text observed=2026-06-27T05:42:37.031721Z digest=sha256:a30dbd3886238a5b3db0bf1732339bd2e5badd95fabcb6d36577c2069bb0064a

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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source=pdf_text observed=2026-06-26T07:14:40.706395Z digest=sha256:ace093a5bbdfc6a588c1b59fb39ea001a43a481432ba8c24cbe678167276f7f6

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:d7bbb2e8d8515cfcd256db027a7188910cfadc6c434f7b372eefbc87ea4e2e5e

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:b5b715120f0e16af367ad0247ef38018a635c87912fff9ceda90f9fd97d91942

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:ade52d0e4d27d6ebbc0af197d094158e3c0e67e3d44f132fb48c1c7c33fcea2f

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=sha256:94681b5b65380da1859f27b96746b5e0f87ca586911af610a661d7907a704a8f