Paper Citation Record · LEDGER

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2505.23387.

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

pith.paper-citation-record.v1
2505.23387 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T19:27:35.889180Z

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

0
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 d24ad08c-0077-4fc8-bcb5-6bd98235db2a · inbound

A Survey of Reinforcement Learning for Large Reasoning Models cites this paper.

A Survey of Reinforcement Learning for Large Reasoning Models Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Reference 119

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:02:25.299885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 63ce1b29-3b42-47d7-9ac3-da77138d62bc · inbound

ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution cites this paper.

ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T19:27:35.889180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8091cb00-e0a4-413e-8745-630dca012d56 · inbound

An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code cites this paper.

An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:40:10.533028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0ab1904a-5441-4146-b456-9d111cebdfb7 · inbound

Paper Espresso: From Paper Overload to Research Insight cites this paper.

Paper Espresso: From Paper Overload to Research Insight Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:45:47.882211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f1c30b62-e7be-45ca-865c-b5e7fc4d2ce5 · inbound

AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code cites this paper.

AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-20T11:43:14.954223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cf593b53-fee9-4d4c-8b60-9aa8ef710dc3 · inbound

Extrapolative Weight Averaging Reveals Correctness-Efficiency Frontiers in Code RL cites this paper.

Extrapolative Weight Averaging Reveals Correctness-Efficiency Frontiers in Code RL Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:43:30.585461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 929a6b79-6e5d-4392-9ea1-d87fc9f941b7 · inbound

Chiseling Out Efficiency: Structured Skeleton Supervision for Efficient Code Generation cites this paper.

Chiseling Out Efficiency: Structured Skeleton Supervision for Efficient Code Generation Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-02T18:57:17.261414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 340d2a8d-ac0f-4077-82c6-24e66db3da73 · inbound

SkelDPO: A Skeleton-Guided Direct Preference Optimization Framework for Efficient Code Generation cites this paper.

SkelDPO: A Skeleton-Guided Direct Preference Optimization Framework for Efficient Code Generation Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:07:17.720297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4ab10d0a-cf07-46ae-bac0-8c78fb288242 · inbound

Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It cites this paper.

Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-06-27T13:10:55.920584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f75767b9-bece-4bef-92df-e6872303254f · inbound

From Trainee to Trainer: LLM-Designed Training Environment for RL with Multi-Agent Reasoning cites this paper.

From Trainee to Trainer: LLM-Designed Training Environment for RL with Multi-Agent Reasoning Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-06-27T01:00:19.847467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.