Paper Citation Record · LEDGER

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation

As of 21 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2608.01804.

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

pith.paper-citation-record.v1
2608.01804 v2

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:14:40.383156Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

verified exact0
verified fuzzy2
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4b0e5296-46c0-4996-9f78-40f3af77103c · outbound

This paper cites RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:38.260775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2564e9df-8411-4ba3-a91f-e2cfd227ce72 · outbound

This paper cites Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:38.347899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ef81299-f809-4b3c-8eaf-dc99e5af92e2 · outbound

This paper cites Reinforcement Learning with Rubric Anchors.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Reinforcement Learning with Rubric Anchors

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:38.445310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56f4a5a0-6596-492e-81d2-500366102545 · outbound

This paper cites Reveal: Self-evolving code agents via reliable self-verification.arXiv preprint arXiv:2506.11442,.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Reveal: Self-evolving code agents via reliable self-verification.arXiv preprint arXiv:2506.11442,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:38.546266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39c2a086-d479-403e-9c5a-37aa61e6dba7 · outbound

This paper cites CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:38.778592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4234a704-7a43-4fcb-a77e-432e94646b85 · outbound

This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Understanding R1-Zero-Like Training: A Critical Perspective

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:39.097324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0006dbf-00a7-4acc-ab6f-df68a9b7958c · outbound

This paper cites StarCoder 2 and The Stack v2: The Next Generation.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation StarCoder 2 and The Stack v2: The Next Generation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:39.265466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b2cde1c-1b24-413f-a027-d432e7ec604e · outbound

This paper cites KernelBench: Can LLMs Write Efficient GPU Kernels?.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation KernelBench: Can LLMs Write Efficient GPU Kernels?

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:39.414794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54b62edc-d276-476e-89d1-a7de19a0f35e · outbound

This paper cites Proximal Policy Optimization Algorithms.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Proximal Policy Optimization Algorithms

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:39.532200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a58342c-d794-4fbf-bb55-52ea57917e14 · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Kimi K2: Open Agentic Intelligence

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:39.834527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32bfd783-1301-4b4f-9eee-22a65c346839 · outbound

This paper cites Magicoder: Empowering Code Generation with OSS-Instruct.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Magicoder: Empowering Code Generation with OSS-Instruct

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:39.998379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0d68600-e5c4-403b-a434-0cc001641b6b · outbound

This paper cites MiMo-V2-Flash Technical Report.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation MiMo-V2-Flash Technical Report

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:40.119134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bf33520-9fc8-41e6-8c90-0ba4146f3059 · outbound

This paper cites Kodcode: A diverse, challenging, and verifiable synthetic dataset for coding.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Kodcode: A diverse, challenging, and verifiable synthetic dataset for coding

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:14:41.196047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8718100a-9498-49fc-9ecf-19a4602aad3e · outbound

This paper cites Qwen3 Technical Report.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Qwen3 Technical Report

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:40.175694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a14fd13b-69db-4100-8de4-068ed528d308 · outbound

This paper cites VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:40.240344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b85acec-c6e0-4e02-9496-c7c4004362dc · outbound

This paper cites GLM-5: from Vibe Coding to Agentic Engineering.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation GLM-5: from Vibe Coding to Agentic Engineering

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:40.307397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea9a0ebb-dcb4-4436-856d-c7534c2aee98 · outbound

This paper cites Group Sequence Policy Optimization.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Group Sequence Policy Optimization

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:40.383156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e3f3bbe-4b24-453e-a60f-871f4f1975d5 · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation HybridFlow: A Flexible and Efficient RLHF Framework

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:39.656558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88217968-cbb4-4fbc-802a-1e4e6990bbe8 · outbound

This paper cites MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU

Reference 2021

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T00:14:41.018986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 214ad3cc-1331-461f-be12-d4f35a935a5b · outbound

This paper cites an unresolved cited work.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Unresolved cited work

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:38.925443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af69c148-4d9f-48a6-8c5b-992d3c69eff3 · outbound

This paper cites Program Synthesis with Large Language Models.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Program Synthesis with Large Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:37.907102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d6e8044-d512-486e-ae7a-42d856a596ea · outbound

This paper cites Cuda agent: Large-scale agentic rl for high-performance cuda kernel generation.arXiv preprint arXiv:2602.24286,.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Cuda agent: Large-scale agentic rl for high-performance cuda kernel generation.arXiv preprint arXiv:2602.24286,

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:38.071821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f6446a1-34a0-4d75-a3f3-3ac18298b60d · outbound

This paper cites Chanakya Ekbote, Vijay Lingam, Behrooz Omidvar Tehrani, Jun Huan, Sujay Sanghavi, Anoop Deoras, and Stefano Soatto.

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation Chanakya Ekbote, Vijay Lingam, Behrooz Omidvar Tehrani, Jun Huan, Sujay Sanghavi, Anoop Deoras, and Stefano Soatto

Reference 2026

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:14:41.363450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.