Paper Citation Record · LEDGER

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals

As of 19 July 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2605.22703.

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

pith.paper-citation-record.v1
2605.22703 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T07:50:44.907952Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-19T06:30:13.599613+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

29 of 29 outbound references displayed

verified exact29
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5e0859d6-e3d1-4d7d-a264-7c97c2c886c0 · outbound

This paper cites MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.796005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 9292c4f1-2023-41b6-81e4-7881c07a53be · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.639176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation f6cd9c29-5cdb-4e8d-ae4c-2dec22cf888e · outbound

This paper cites The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.814142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation dd45ef2d-a0f7-4346-b921-f5831caf6140 · outbound

This paper cites It’s not you, it’s clipping: A soft trust-region via probability smoothing for llm rl.arXiv preprint arXiv:2509.21282.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals It’s not you, it’s clipping: A soft trust-region via probability smoothing for llm rl.arXiv preprint arXiv:2509.21282

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:15.802276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 91d8d387-b458-4cba-807a-ae2c77f70342 · outbound

This paper cites Soft Adaptive Policy Optimization.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Soft Adaptive Policy Optimization

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.774784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 301622b1-4422-425a-9431-d2af0c33e505 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.620123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 46c30d97-08a2-4b44-838a-3b6b05063a80 · outbound

This paper cites Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.697768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 90ec24fa-4f83-425a-b3e6-b7970b52808e · outbound

This paper cites A sober look at progress in language model reasoning: Pitfalls and paths to repro- ducibility.arXiv preprint arXiv:2504.07086.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals A sober look at progress in language model reasoning: Pitfalls and paths to repro- ducibility.arXiv preprint arXiv:2504.07086

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:15.692240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 367b4e4c-f50f-4cfa-8dd7-a39443bb77fa · outbound

This paper cites Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.685603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 238b9426-d4a4-4965-89ce-ade9d984a77c · outbound

This paper cites Low-probability tokens sustain exploration in reinforcement learning with verifiable reward.arXiv preprint arXiv:2510.03222.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Low-probability tokens sustain exploration in reinforcement learning with verifiable reward.arXiv preprint arXiv:2510.03222

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:15.753818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 2517d983-0c8b-4c14-8a63-86309674861c · outbound

This paper cites On the direction of rlvr updates for llm reasoning: Identification and exploitation.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals On the direction of rlvr updates for llm reasoning: Identification and exploitation

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:15.727460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 23bd93be-4664-4847-bccb-d7a5f9cb55bd · outbound

This paper cites OpenAI o1 System Card.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals OpenAI o1 System Card

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.704046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation e2abea4e-c79c-4959-a195-af7f96679c08 · outbound

This paper cites How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:15.808337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation db52912b-150e-466b-98c2-0ac890713bdd · outbound

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

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Understanding R1-Zero-Like Training: A Critical Perspective

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.662587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation fe4addce-31ae-4ed4-bad0-6f99371cbeaf · outbound

This paper cites Clip your sequences fairly: Enforcing length fairness for sequence-level rl.arXiv preprint arXiv:2509.09177.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Clip your sequences fairly: Enforcing length fairness for sequence-level rl.arXiv preprint arXiv:2509.09177

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:15.651338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation fc1b55e9-9549-4a18-8b38-e370881089db · outbound

This paper cites Sparse but critical: A token-level analysis of distributional shifts in rlvr fine-tuning of llms.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Sparse but critical: A token-level analysis of distributional shifts in rlvr fine-tuning of llms

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:15.645752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation e46ca483-3cdd-43b8-bb15-28ce04760131 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Proximal Policy Optimization Algorithms

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.709378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 97f8db89-096a-487c-bf6a-422aff7092e3 · outbound

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

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.714286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 5c4918be-f31a-4c04-84c3-8f75c3fd042e · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Kimi K2: Open Agentic Intelligence

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.732570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation b91137a3-0dbd-4d08-a95c-7173221044fe · outbound

This paper cites When Importance Sampling Misallocates Credit: Asymmetric Ratios for Outcome-Supervised RL.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals When Importance Sampling Misallocates Credit: Asymmetric Ratios for Outcome-Supervised RL

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.747619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 8ac5d033-92d9-4fa0-b496-0b31101b0133 · outbound

This paper cites Quantile advantage estimation for entropy-safe reasoning.arXiv preprint arXiv:2509.22611.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Quantile advantage estimation for entropy-safe reasoning.arXiv preprint arXiv:2509.22611

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:15.790336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 4c899392-4280-44b4-8a6a-eff6296f39fb · outbound

This paper cites Bapo: Stabilizing off-policy reinforcement learning for llms via balanced policy optimization with adaptive clipping.arXiv preprint arXiv:2510.18927.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Bapo: Stabilizing off-policy reinforcement learning for llms via balanced policy optimization with adaptive clipping.arXiv preprint arXiv:2510.18927

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:15.669534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 9cb69628-45fa-4210-bb7e-79c3b70c4d13 · outbound

This paper cites Single-stream policy optimization.arXiv preprint arXiv:2509.13232.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Single-stream policy optimization.arXiv preprint arXiv:2509.13232

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:15.679598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation d65a0d37-bca9-44b1-92e7-76738fb95fc4 · outbound

This paper cites Qwen3 Technical Report.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Qwen3 Technical Report

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.780109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 131054e5-b69f-4763-ab6b-3bc8dd993145 · outbound

This paper cites Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMs.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMs

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:15.632516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 5aead835-031d-4dfa-9f94-aac20548c730 · outbound

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

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.656925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 1e6d9905-7329-4639-aa7d-6711f79f0fdf · outbound

This paper cites SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.625672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 319eccbc-5a9e-4908-bb6b-ed66cf03d02d · outbound

This paper cites Group Sequence Policy Optimization.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals Group Sequence Policy Optimization

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:51:15.739709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation ac259059-5106-49e2-ade4-755b0239ff58 · outbound

This paper cites The surprising effectiveness of negative reinforcement in llm reasoning.arXiv preprint arXiv:2506.01347.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals The surprising effectiveness of negative reinforcement in llm reasoning.arXiv preprint arXiv:2506.01347

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:51:15.721002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Pith citing papers

No inbound Pith citation observations are available.