Pith. sign in

Paper Citation Record · LEDGER

Execution-based Code Generation using Deep Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 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 27 of 27 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 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:10:38.922030Z

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

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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

Resolution
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T20:18:06.304134Z digest=sha256:7b9b9da2f8dcf4f00902c6c439832edb5529c29ea2e5b1ff8f0deaeb81f9d501

Observation 3e62896b-58e7-403e-bc94-5d8f11da6227 · inbound

Process-Supervised Reinforcement Learning for Code Generation cites this paper.

Process-Supervised Reinforcement Learning for Code Generation Execution-based Code Generation using Deep Reinforcement Learning

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-09T15:10:38.922030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T15:10:38.922030Z digest=sha256:18b6c91a1ded240aa666ecc39c18ea99436dba197b0b80711847095fc6348444

Observation 10b6842c-cc98-48fa-9cd1-a51d18664eae · inbound

ACECODER: Acing Coder RL via Automated Test-Case Synthesis cites this paper.

ACECODER: Acing Coder RL via Automated Test-Case Synthesis Execution-based Code Generation using Deep Reinforcement Learning

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-09T14:53:49.855955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T14:53:49.855955Z digest=sha256:322af81e5a171b7f0f2eb0ba46d9e1057b08ffe69461fa0d783c22298fabd517

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

Resolution
unresolved
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=sha256:30935a4e94694f0a7bc0eed4b86f0e5004e289599f3cbb3e5dac0ad346835b24

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

Resolution
unresolved
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=sha256:750af809315e54bd10a25956929bc8a404483ba14d3e304b75aa9eb4545ec209

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

Resolution
unresolved
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

Resolution
unresolved
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=sha256:409114adcbf3db9ba48e4dee52b8e5de97e93fa23b762910af72ad895cc84f7a

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

Resolution
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=sha256:233ddbfa728e01e4e70f043c2017e47f496d381297b95ea1bea586ee556eb84d

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

Resolution
unresolved
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=sha256:67baa188088b3be4b830977df448ea0af27f1bbf846292f9ee44f7f8a362668e

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

Resolution
unresolved
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

Resolution
verified exact
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T19:44:32.950977Z digest=sha256:43db2049dad9bfc1de37475c3545a76f43cafb43f94d0eec7db066eac52d861e

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

Resolution
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

Resolution
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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T06:39:29.822371Z digest=sha256:1b0ddf8a15d5ec8add9905507884c8b666dac689327a0808f707635ee4ade4a6

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

Resolution
verified exact
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T04:59:44.880102Z digest=sha256:027c7c7e090a391003eed38da22f635cbe198c0c0b5bbe561f6c651a658d3a6c

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

Resolution
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T19:21:30.956682Z digest=sha256:4f6ae12ce60a45e5513c2ef9f22a47485300d9b0484b61580897f86a9a23c873

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

Resolution
metadata mismatch
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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-12T03:09:40.321500Z digest=sha256:5ded3a35bf74f46e1776a1643c148835185e6a3f019854220bcb3693d039ed3d

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

Resolution
metadata mismatch
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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-13T06:03:32.270553Z digest=sha256:e7b80bea510367a970a4febe64356f2056bd549433c6a2f8e89563b09d6fbe66

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

Resolution
verified exact
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T14:03:45.869373Z digest=sha256:b91f2dbec17da560540fa2c1739295201197f22aa940c60e4afb14c79d61ce24

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

Resolution
verified exact
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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-21T05:24:46.545570Z digest=sha256:c02d8cfbf9bc66f45b5e4a3c252d1f8c0935c6973662b62810e109c648dce0fa

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

Resolution
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T21:20:32.039699Z digest=sha256:ca37cda4b2e5c184e47a17cc825d7ea88dda28d97cc05ec4e3c5bf9df60c654a

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

Resolution
metadata mismatch
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T11:34:44.365819Z digest=sha256:3d31d2db17b1a2819241b667388da33108ead914340c69e987e10b5cfa477e70

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

Resolution
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T12:15:08.304150Z digest=sha256:af4da6784bbddcdacc38509f213227b839242bf86ffdd7d8f039e46bb87fb817

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

Resolution
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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-30T06:09:43.645011Z digest=sha256:41e3843f1061c114acf4e0ac342db946b4e98e1d3bc8b1028bc07fc08b05ef0a

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

Resolution
verified exact
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-03T09:10:26.900479Z digest=sha256:a59aead8bd7041c659df2a9a63d30d7994214a096e71e99ac4506fd65b3c15fc

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T19:29:32.509575Z digest=sha256:52b22397f5da07ec67e389def55d5083abfbcc968cc43861ae7544526369ea74

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

Resolution
unresolved
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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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