Paper Citation Record · LEDGER

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training

As of 5 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2607.01232.

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

pith.paper-citation-record.v1
2607.01232 v2

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-03T21:31:31.831806Z

measured 15 of 15 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 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

15 of 15 outbound references displayed

verified exact5
verified fuzzy0
unresolved1
parse uncertain0
malformed identifier1
metadata mismatch8

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 08e4578a-29a2-4a68-869d-b50b05ecec62 · outbound

This paper cites Group-in-Group Policy Optimization for LLM Agent Training.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Group-in-Group Policy Optimization for LLM Agent Training

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T21:38:58.218916Z

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 87e7d526-e2d1-4913-ae10-4e5456e6b594 · outbound

This paper cites arXiv preprint arXiv:2603.12228 , year=.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training arXiv preprint arXiv:2603.12228 , year=

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T21:38:58.225176Z

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 388bc0d0-f8e6-4930-8b8e-daa21d6ba12c · outbound

This paper cites doi: 10.1038/s41586-025-09422-z.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training doi: 10.1038/s41586-025-09422-z

Reference 3

Resolution: verified exact; doi, observed 2026-07-03T21:38:58.143642Z

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 5f6cbcb8-cba1-474f-83df-b75394535779 · outbound

This paper cites Skywork Open Reasoner 1 Technical Report.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Skywork Open Reasoner 1 Technical Report

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T21:38:58.231652Z

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 15dc8aef-0ff7-47d8-898c-b591865c5069 · outbound

This paper cites an unresolved cited work.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Unresolved cited work

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:38:58.233859Z

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 2536fceb-2d62-4ee3-9e29-945a7be7f1a9 · outbound

This paper cites Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:38:58.241686Z

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 62079cd2-02a8-44bf-9a90-cab3dea7f5fb · outbound

This paper cites Layer importance for mathematical reasoning is forged in pre-training and invariant after post-training.arXiv preprint arXiv:2506.22638.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Layer importance for mathematical reasoning is forged in pre-training and invariant after post-training.arXiv preprint arXiv:2506.22638

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T21:38:58.239083Z

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 95811a6e-b862-4222-9363-0b890c83df91 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T21:38:58.209283Z

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 8e9ae075-ee0c-4bb8-b5e5-a9566785ca00 · outbound

This paper cites Understanding Layer Significance in LLM Alignment.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Understanding Layer Significance in LLM Alignment

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T21:38:58.239487Z

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 9c18fd4d-62d5-431e-88ff-a5ba265b9917 · outbound

This paper cites an unresolved cited work.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-07-05T01:00:30.250735Z

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 4e855d53-d7e1-4176-9827-72c390d4802b · outbound

This paper cites arXiv preprint arXiv:2510.02091 , year=.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training arXiv preprint arXiv:2510.02091 , year=

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T21:38:58.225399Z

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 22579516-6af9-4851-aa97-2cc6017ab972 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-03T21:38:58.236761Z

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 f5022f28-e4a1-489c-8732-60ba8d263ba7 · outbound

This paper cites Qwen3 Technical Report.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Qwen3 Technical Report

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-03T21:38:58.234321Z

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 774f8104-9ddf-4b0a-9b99-0bf396d4da7b · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 14

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T21:38:58.236451Z

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 863f83a1-83b8-42c2-a51a-3190a8161e1d · outbound

This paper cites Investigating Layer Importance in Large Language Models.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Investigating Layer Importance in Large Language Models

Reference 15

Resolution: malformed identifier; arxiv_id, observed 2026-07-03T21:38:58.228463Z

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.

Pith citing papers

No inbound Pith citation observations are available.