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Paper Citation Record · LEDGER

Execution-based Code Generation using Deep Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 inbound Pith citation observations for arXiv:2301.13816.

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

pith.paper-citation-record.v1
2301.13816 v4

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 2026-08-08T06:32:00.761636+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:13:31.122727Z

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

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External citation measurements

8
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 770f8f53-8550-4e54-ab2a-1e2ccf728be0 · inbound

A Survey on Large Language Models for Code Generation cites this paper.

A Survey on Large Language Models for Code Generation Execution-based Code Generation using Deep Reinforcement Learning

Reference 243

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verified exact
arxiv_id, observed 2026-05-13T20:18:06.673514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-13T20:18:06.304134Z digest=sha256:4401ac6b62a6c6c5b6d4532b8861735af8ab4cdd758d54c915548a20b71a33bd

Observation e5205a5a-af31-4ef0-8d55-44c38a1114d2 · inbound

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review cites this paper.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Execution-based Code Generation using Deep Reinforcement Learning

Reference 35

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no resolver link, observed 2026-08-07T12:13:31.122727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:13:31.122727Z digest=sha256:696351c20937001d638f4c5d56b52ede212beb84fee0b7e8d3ed41fd0f07a68a

Observation fc237243-3fcd-422c-b51e-f8f869279c90 · inbound

D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning cites this paper.

D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning Execution-based Code Generation using Deep Reinforcement Learning

Reference 55

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no resolver link, observed 2026-08-07T04:41:57.111117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:41:57.111117Z digest=sha256:45237e0b2c4e5b2eca04fbab1f3fe923a680d73878eed52260af41b096c9c2b7

Observation 411b2356-07ca-4d3d-b597-eea5e69c93d7 · inbound

Rethinking Verification for LLM Code Generation: From Generation to Testing cites this paper.

Rethinking Verification for LLM Code Generation: From Generation to Testing Execution-based Code Generation using Deep Reinforcement Learning

Reference 40

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no resolver link, observed 2026-08-06T18:58:21.176876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:58:21.176876Z digest=sha256:fc3c85676df26b0d21ff615b75885a71ef6ad320301bbdf2085349bf0dc1eb24

Observation b94e44d0-cd79-4cb6-81e1-2f80ecefb0b1 · inbound

Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing cites this paper.

Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing Execution-based Code Generation using Deep Reinforcement Learning

Reference 35

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no resolver link, observed 2026-08-06T15:52:20.435501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T15:52:20.435501Z digest=sha256:9f08d154e8874b0e33f62ab47e772a4cf6ea0c33f9dfa7cc0901e2c6e0ddc864

Observation e519399b-bcb4-4b9c-acfd-5337f0b79127 · inbound

Efficiency of turbulence cites this paper.

Efficiency of turbulence Execution-based Code Generation using Deep Reinforcement Learning

Reference 25

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unresolved
no resolver link, observed 2026-08-06T00:58:29.223975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:58:29.223975Z digest=sha256:7172745a75f5f99705f49b2a42ed092a9cb632a3cb341003c00636b535fd9277

Observation 826bf715-dc69-4193-92a9-815a966ded12 · inbound

AR$^2$: Adversarial Reinforcement Learning for Abstract Reasoning in Large Language Models cites this paper.

AR$^2$: Adversarial Reinforcement Learning for Abstract Reasoning in Large Language Models Execution-based Code Generation using Deep Reinforcement Learning

Reference 14

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no resolver link, observed 2026-08-05T15:17:13.801965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:17:13.801965Z digest=sha256:47ccf43f3e8a0361a2572337e6f4126ecdf4747dc9d9c87b59d5d54b3841a6ee

Observation d9519710-254c-45f8-b4cb-2693fccc5204 · inbound

Beyond Binary: Turning Partial Success into Dense Verifiable Rewards for Reinforcement Learning in Code Generation cites this paper.

Beyond Binary: Turning Partial Success into Dense Verifiable Rewards for Reinforcement Learning in Code Generation Execution-based Code Generation using Deep Reinforcement Learning

Reference 23

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no resolver link, observed 2026-08-03T12:19:34.174744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T12:19:34.174744Z digest=sha256:b5c9e7ce89955852bce65ec7e99dbabfae301d63aa6ce71dac14d34273ea4c29

Observation bfcc3ed0-e974-4be0-b717-b61b7ab8a0e2 · inbound

An Iterative Test-and-Repair Framework for Competitive Code Generation cites this paper.

An Iterative Test-and-Repair Framework for Competitive Code Generation Execution-based Code Generation using Deep Reinforcement Learning

Reference 48

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arxiv_id, observed 2026-05-10T22:35:48.674187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T19:44:32.950977Z digest=sha256:a726ec3d81146bab96933667a7fb25f9aeef284bfbbed2ddbf56154598a5d21f

Observation f9df5fac-b9e6-4448-b030-bbaeb0786ebf · inbound

An Iterative Test-and-Repair Framework for Competitive Code Generation cites this paper.

An Iterative Test-and-Repair Framework for Competitive Code Generation Execution-based Code Generation using Deep Reinforcement Learning

Reference 11

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unresolved
no resolver link, observed 2026-07-13T09:22:44.557413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T09:22:44.557413Z digest=sha256:b9a28a4d4adbc379ac83acc988b403071d72e10369052c352667261c2b06be9d

Observation 32c3972a-6f25-4c74-a360-76b88eec7f6f · inbound

SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair cites this paper.

SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair Execution-based Code Generation using Deep Reinforcement Learning

Reference 41

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metadata mismatch
arxiv_id, observed 2026-05-10T06:41:36.589768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-10T06:39:29.822371Z digest=sha256:9305137d38afab381b222b892d968298bd56add0b4a1792a62728afb79e0cd28

Observation e5ee8f35-ee5a-4537-bf95-5879c833a6ce · inbound

CodePivot: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning without Parallel Corpora cites this paper.

CodePivot: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning without Parallel Corpora Execution-based Code Generation using Deep Reinforcement Learning

Reference 59

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arxiv_id, observed 2026-05-10T10:29:25.202134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T04:59:44.880102Z digest=sha256:0a541adedf394f30c11052f3bca4ae5ab8c7dadbf0df4b90f149e82f1415c610

Observation 97683048-d442-4d5b-8616-b1213aa3da67 · inbound

Improving LLM Code Generation via Requirement-Aware Curriculum Reinforcement Learning cites this paper.

Improving LLM Code Generation via Requirement-Aware Curriculum Reinforcement Learning Execution-based Code Generation using Deep Reinforcement Learning

Reference 46

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verified exact
arxiv_id, observed 2026-05-11T15:46:23.555623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-09T19:21:30.956682Z digest=sha256:69b6b879428421dd3945577e8b4f4be57d5c8354b33a4cbc7910fe7de492f43b

Observation 7275ce09-69bf-4d0e-94fc-18389746d35d · inbound

BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models cites this paper.

BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models Execution-based Code Generation using Deep Reinforcement Learning

Reference 30

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arxiv_id, observed 2026-05-12T03:11:18.935457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-12T03:09:40.321500Z digest=sha256:8def6cba0cb6138e2a996f9a3ac4374cf91650b746c377196b549b1d40475379

Observation a8fb9521-3747-4586-a52a-7b665b6432c1 · inbound

BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models cites this paper.

BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models Execution-based Code Generation using Deep Reinforcement Learning

Reference 34

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arxiv_id, observed 2026-05-13T06:07:22.406169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-13T06:03:32.270553Z digest=sha256:5066ac13847b6d3aeece5386e4ec17be94fd1a2b962d99db5012b8063a457887

Observation 0801974b-1c8a-4b0f-b053-e0b3bb2dd518 · inbound

Beyond Execution: Static-Analysis Rewards and Hint-Conditioned Diffusion RL for Code Generation cites this paper.

Beyond Execution: Static-Analysis Rewards and Hint-Conditioned Diffusion RL for Code Generation Execution-based Code Generation using Deep Reinforcement Learning

Reference 19

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arxiv_id, observed 2026-05-20T14:08:21.231501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-20T14:03:45.869373Z digest=sha256:8690c231843e80c34d045985a7013ae0e1b65c70fd3d21d1bae0bb35da62d613

Observation 9781e361-2e6b-42f8-b428-273a3046c160 · inbound

DelTA: Discriminative Token Credit Assignment for Reinforcement Learning from Verifiable Rewards cites this paper.

DelTA: Discriminative Token Credit Assignment for Reinforcement Learning from Verifiable Rewards Execution-based Code Generation using Deep Reinforcement Learning

Reference 70

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arxiv_id, observed 2026-05-21T05:29:40.123870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-21T05:24:46.545570Z digest=sha256:289a38f16d663eccb027c46188039bcebc9bcf1a490e7975d2a709d159353788

Observation c56f6a71-cc21-4e23-87bf-15c21b2192aa · inbound

Reinforcement Learning from Denoising Feedback cites this paper.

Reinforcement Learning from Denoising Feedback Execution-based Code Generation using Deep Reinforcement Learning

Reference 21

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verified exact
arxiv_id, observed 2026-06-29T21:23:58.888723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-29T21:20:32.039699Z digest=sha256:3714d633b54df846f33aca42ef317b2f1bec53adb2d4a00b5211a30a04612e96

Observation f4446bc3-3b2d-49c4-b44e-ba5c0b6f9dce · inbound

Synthetic Hallucinations, Real Gains: Hard Negatives from Frontier Models for FIM Hallucination Mitigation cites this paper.

Synthetic Hallucinations, Real Gains: Hard Negatives from Frontier Models for FIM Hallucination Mitigation Execution-based Code Generation using Deep Reinforcement Learning

Reference 13

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arxiv_id, observed 2026-06-28T11:42:04.450619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-28T11:34:44.365819Z digest=sha256:11726e707acf6224c1853f7e34e1fb622d9e1f614347faa189dae4dec9a5d959

Observation 5fb00bd8-d457-48c6-90f3-3da3e351272c · inbound

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning cites this paper.

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning Execution-based Code Generation using Deep Reinforcement Learning

Reference 176

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verified exact
arxiv_id, observed 2026-07-04T07:59:40.693474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T12:15:08.304150Z digest=sha256:ae16fed4ab6828d67d0e4a133c1f4068aae32984a23636775740d64818e3e001

Observation f20d8b71-b141-40fe-b9d2-6eb2e1c30b12 · inbound

AlgoSkill: Learning to Design Algorithms by Scheduling Human-Like Skills cites this paper.

AlgoSkill: Learning to Design Algorithms by Scheduling Human-Like Skills Execution-based Code Generation using Deep Reinforcement Learning

Reference 26

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metadata mismatch
arxiv_id, observed 2026-06-30T08:14:26.472700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-30T06:09:43.645011Z digest=sha256:3ca456dac993e577125f29c77c67a1d136077215f84d22f21f9de6eddc6089c9

Observation 3cdc84bc-60cb-429c-8821-8f14a8213061 · inbound

An Exploratory Study on LLM-Generated Code and Comments in Code Repositories cites this paper.

An Exploratory Study on LLM-Generated Code and Comments in Code Repositories Execution-based Code Generation using Deep Reinforcement Learning

Reference 6

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arxiv_id, observed 2026-07-03T09:17:48.252475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-03T09:10:26.900479Z digest=sha256:c87723fdfdbb8df25848282c605779eda92b77ce3ee1d560976de7db94a1ae0f

Observation a09e0bde-c90e-4bab-9dd2-25c9bd7725dd · inbound

NKI-Agent: Domain-Specific Fine-Tuning and Agentic Tool Use for Neuron Kernel Generation cites this paper.

NKI-Agent: Domain-Specific Fine-Tuning and Agentic Tool Use for Neuron Kernel Generation Execution-based Code Generation using Deep Reinforcement Learning

Reference 13

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no resolver link, observed 2026-07-11T19:29:32.509575Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T19:29:32.509575Z digest=sha256:515bd13fd768fa1f9808377b432c64fb0c567238ae3c42ef3ba495ffd9f9530c

Observation b8371a96-f3d5-42a9-b6ae-49d9c2218f85 · inbound

Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning cites this paper.

Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning Execution-based Code Generation using Deep Reinforcement Learning

Reference 56

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no resolver link, observed 2026-07-11T17:00:48.664985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T17:00:48.664985Z digest=sha256:1b79a37ff3421965b5b893b3ea38dfebf89942ca9a09f3412eafdcf0e324b349

Observation 80022d76-fdfa-4315-a37c-8b63187432a6 · inbound

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python cites this paper.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Execution-based Code Generation using Deep Reinforcement Learning

Reference 22

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no resolver link, observed 2026-08-01T08:38:06.831366Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T08:38:06.831366Z digest=sha256:1a951682b2a43f66293e61c75c13be4de1b2f48a3a23a2fcc4abf680fc5a211b