Paper Citation Record · LEDGER

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

As of 19 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 69 inbound Pith citation observations for arXiv:2505.24864.

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

pith.paper-citation-record.v1
2505.24864 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T20:52:33.620041Z

measured 134 of 134 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 69 of 69 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:46:57.916405Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

65 of 65 outbound references displayed

verified exact10
verified fuzzy47
unresolved7
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation db55c592-fee7-489c-917f-8a715bd1b81f · outbound

This paper cites OpenAI o1 System Card.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models OpenAI o1 System Card

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:52:33.737148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0064d6c0-ef0b-4571-b920-68cd5c3407bf · outbound

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

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:52:33.724965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fe294fc9-23aa-471b-be50-bca517e8ecf6 · outbound

This paper cites Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.942017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 60023cbc-ca21-4ba3-8268-b2039fe12f5b · outbound

This paper cites Dapo: An open-source llm reinforcement learning system at scale.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Dapo: An open-source llm reinforcement learning system at scale

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.945910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 78dc3227-3d7b-4b4b-8625-fa8c2ce84328 · outbound

This paper cites Vapo: Efficient and reliable reinforcement learning for advanced reasoning tasks.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Vapo: Efficient and reliable reinforcement learning for advanced reasoning tasks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.949604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 26154e3d-2f9e-49bf-8b85-972a14d5fbd6 · outbound

This paper cites Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.953023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 32789a36-fa2d-4743-bda5-459f1daaa73e · outbound

This paper cites Deepcoder: A fully open-source 14b coder at o3-mini level.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Deepcoder: A fully open-source 14b coder at o3-mini level

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.956593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8da7fc17-dcab-46f2-9cdc-dd5cbbd752b0 · outbound

This paper cites Code-r1: Reproducing r1 for code with reliable rewards.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Code-r1: Reproducing r1 for code with reliable rewards

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.960415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 26f2d617-b911-4953-83c6-a852e7f74399 · outbound

This paper cites Concrete Problems in AI Safety.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Concrete Problems in AI Safety

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:52:33.694039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 778cbe5b-0bdc-498f-8034-e4b8237e28b7 · outbound

This paper cites Reward hacking in reinforcement learning.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Reward hacking in reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.963310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e03fd634-d6af-4fc2-bc8c-b5d0a3ede3ab · outbound

This paper cites Language Models Learn to Mislead Humans via RLHF.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Language Models Learn to Mislead Humans via RLHF

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.686569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6d433f32-0dac-46e1-92cc-be5f136bd599 · outbound

This paper cites Ai as humanity’s salieri: Quantifying linguistic creativity of language models via systematic attribution of machine text against web text.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Ai as humanity’s salieri: Quantifying linguistic creativity of language models via systematic attribution of machine text against web text

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.966236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5838bba3-2103-4dd2-84e2-86795e131750 · outbound

This paper cites Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model?.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model?

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.969536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 048a6162-14fa-4d30-b7fb-99493383e734 · outbound

This paper cites Zico Kolter, and Aditi Raghunathan.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Zico Kolter, and Aditi Raghunathan

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.742234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0a4f8708-77ed-4e95-a57e-adc1e70dad1f · outbound

This paper cites Echo chamber: Rl post-training amplifies behaviors learned in pretraining.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Echo chamber: Rl post-training amplifies behaviors learned in pretraining

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.746777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c014312b-3f37-474a-a862-3c91589e9c6a · outbound

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

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:52:33.705777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 32b866aa-1325-4416-9ff9-42a67780c45d · outbound

This paper cites Proximal policy optimization algorithms.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Proximal policy optimization algorithms

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.751463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c29eb7d5-5b95-4c6b-b1a7-ebf6863e36db · outbound

This paper cites Skywork open reasoner series.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Skywork open reasoner series

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.755535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a6e6dd55-2273-4630-9067-7ca35653cae1 · outbound

This paper cites Hybridflow: A flexible and efficient rlhf framework.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Hybridflow: A flexible and efficient rlhf framework

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.760509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c1356bb4-87b9-48a0-8078-329a91fad184 · outbound

This paper cites Decoupled weight decay regularization.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Decoupled weight decay regularization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.768334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a664540d-7487-4b8d-8502-cf019f896747 · outbound

This paper cites 7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models 7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.772425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 032fb386-a6fa-4320-b3e0-463613682f23 · outbound

This paper cites American invitational mathematics examination - aime.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models American invitational mathematics examination - aime

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.776150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5769f3b9-e674-44aa-b035-0d3a24e4ed02 · outbound

This paper cites American invitational mathematics examination - aime.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models American invitational mathematics examination - aime

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.779815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4c78e818-9456-4aad-89d9-c237e427d7da · outbound

This paper cites American mathematics competition - amc.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models American mathematics competition - amc

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.783256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ad0d9e4a-d37f-4c2d-8129-926b53fcc9fd · outbound

This paper cites Measuring mathematical problem solving with the math dataset.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Measuring mathematical problem solving with the math dataset

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.789102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 346425fb-271c-451b-b981-072f5b73c05a · outbound

This paper cites Solving quantitative reasoning problems with language models.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Solving quantitative reasoning problems with language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.794425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6f05626e-695f-4967-a5d4-1377e44d62e3 · outbound

This paper cites Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.800330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5c417ec9-007a-476d-bba7-1922037368d0 · outbound

This paper cites Process Reinforcement through Implicit Rewards.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Process Reinforcement through Implicit Rewards

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:52:33.699361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91521688-f5ce-4ac0-ae3b-4e9de7211fc7 · outbound

This paper cites Measuring coding challenge competence with apps.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Measuring coding challenge competence with apps

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.804471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 26835d4f-2536-4123-aad7-88ce73a16de3 · outbound

This paper cites Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.809575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a9b6e0cf-c930-4050-ab40-b9e3d51a231f · outbound

This paper cites Taco: Topics in algorithmic code generation dataset.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Taco: Topics in algorithmic code generation dataset

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.816463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2ffcc323-b873-41c4-9b99-42734d9af7e8 · outbound

This paper cites Is your code generated by chatGPT really correct? rigorous evaluation of large language models for code generation.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Is your code generated by chatGPT really correct? rigorous evaluation of large language models for code generation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.820615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 164d5c1f-a245-4616-ac35-257d945aac9b · outbound

This paper cites Livecodebench: Holistic and contamination free evaluation of large language models for code.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Livecodebench: Holistic and contamination free evaluation of large language models for code

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.824701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 530d317b-ed23-4f40-bb8d-e586549599df · outbound

This paper cites an unresolved cited work.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-05-18T20:52:33.829282Z

Source-reported events for the cited work

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Observation 10cc91c2-8100-410c-9168-3e433426bccf · outbound

This paper cites Online difficulty filtering for reasoning oriented reinforcement learning.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Online difficulty filtering for reasoning oriented reinforcement learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.833898Z

Source-reported events for the cited work

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Observation 2c2a71e3-91f5-4af9-a98a-ac712c3c4df7 · outbound

This paper cites Instruction-following evaluation for large language models.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Instruction-following evaluation for large language models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.840161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c77aede0-9205-417f-a444-6bc3afe13e97 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Gonzalez, Hao Zhang, and Ion Stoica

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.844846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d17720d-5fb7-42db-9826-95e81a245a76 · outbound

This paper cites The curious case of neural text degeneration.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models The curious case of neural text degeneration

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.848529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f67f247-b569-499c-a439-156277766e15 · outbound

This paper cites Stop overthinking: A survey on efficient reasoning for large language models.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Stop overthinking: A survey on efficient reasoning for large language models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.852649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 203275a8-477a-4007-8762-adad24320538 · outbound

This paper cites AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.719088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 76225005-8ccb-4468-b2d9-d0cf9f0ad41e · outbound

This paper cites Peters, Abhilasha Ravichander, Kyle Richardson, Zejiang Shen, Emma Strubell, Nishant Subramani, Oyvind 12 Tafjord, Pete Walsh, Luke Zettlemoyer, Noah A.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Peters, Abhilasha Ravichander, Kyle Richardson, Zejiang Shen, Emma Strubell, Nishant Subramani, Oyvind 12 Tafjord, Pete Walsh, Luke Zettlemoyer, Noah A

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.858216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aee5fc0f-31e1-4b00-b5e9-49dd4ef86a5d · outbound

This paper cites Learning to reason with llms, September 2024.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Learning to reason with llms, September 2024

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.864157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 45d5ab71-34b9-48c7-bf52-b7cd86a5e60d · outbound

This paper cites an unresolved cited work.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-05-18T20:52:33.868519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f63224ff-cf95-435d-b7b6-48b88c80fd25 · outbound

This paper cites an unresolved cited work.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-05-18T20:52:33.872232Z

Source-reported events for the cited work

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Observation 79d7094c-b39f-4277-a9de-180580feadcf · outbound

This paper cites Mirror descent policy optimization.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Mirror descent policy optimization

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.876291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d466648d-2945-453f-b53e-4d6781f8629a · outbound

This paper cites Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.880257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 759eee3b-d102-43a2-b0bb-61c78a1f5e64 · outbound

This paper cites Scaling llm test-time compute optimally can be more effective than scaling model parameters.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Scaling llm test-time compute optimally can be more effective than scaling model parameters

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.884171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2b8cff30-f1a7-4cab-a1c9-4b1857646c86 · outbound

This paper cites Reinforcement learning enhanced llms: A survey.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Reinforcement learning enhanced llms: A survey

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.887556Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 19d77b62-eb97-41df-913c-99317297aae1 · outbound

This paper cites Playing atari with deep reinforcement learning.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Playing atari with deep reinforcement learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.890735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1477d07b-b4a8-4f43-8183-ee41aac0f9c4 · outbound

This paper cites Human-level control through deep reinforcement learning.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Human-level control through deep reinforcement learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.894533Z

Source-reported events for the cited work

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Observation 8bdf552e-43e1-41d6-b700-0c12848922cb · outbound

This paper cites Mastering the game of go without human knowledge.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Mastering the game of go without human knowledge

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.898429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9b6e9b70-3ad0-4aa2-8d87-7e3fd4ddb4c5 · outbound

This paper cites SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:52:33.711723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 50ec815a-887f-41b1-b2a4-faf5a47ed89e · outbound

This paper cites Llms are greedy agents: Effects of rl fine-tuning on decision-making abilities.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Llms are greedy agents: Effects of rl fine-tuning on decision-making abilities

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.901749Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e4515ad7-1ba7-4bd8-acd5-42d3be0fa1b1 · outbound

This paper cites Star: Bootstrapping reasoning with reasoning.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Star: Bootstrapping reasoning with reasoning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.905512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 261f2417-ad85-4c6d-be4f-b9651063f3b6 · outbound

This paper cites Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:52:33.730999Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ddfc39ac-8803-4be4-9a5a-f6fb9d0158f9 · outbound

This paper cites Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:52:33.673649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0e56cc4d-93a4-4104-9845-e4ea560b15bc · outbound

This paper cites Scp-116k: A high-quality problem-solution dataset and a generalized pipeline for automated extraction in the higher education science domain.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Scp-116k: A high-quality problem-solution dataset and a generalized pipeline for automated extraction in the higher education science domain

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.909671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3d9b0d6c-2395-4ac2-99f5-7f1d45d95b39 · outbound

This paper cites 0": 1, "1.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models 0": 1, "1

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.913331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 34f53166-71e9-43bf-85f6-fdcae4bbe61e · outbound

This paper cites However, the final answer shoule be a list of action plans for multiple steps.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models However, the final answer shoule be a list of action plans for multiple steps

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.917445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 30701deb-379d-4ed2-a5dd-683c7f9bb4bb · outbound

This paper cites Agent[x, y].

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Agent[x, y]

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:52:33.920950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 56225a52-a014-4aff-ad30-f681612009aa · outbound

This paper cites an unresolved cited work.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-05-18T20:52:33.924262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 453d596d-c12c-4d87-9695-564725174cea · outbound

This paper cites an unresolved cited work.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-05-18T20:52:33.928050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e5bd4e70-742b-4608-a5e1-f5b034d18afa · outbound

This paper cites an unresolved cited work.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-05-18T20:52:33.931597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 739902a1-7692-4b82-a9a8-7948808a1042 · outbound

This paper cites an unresolved cited work.

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-05-18T20:52:33.934957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c4baeffc-828d-43e0-b236-0b5f2df58d98 · outbound

This paper cites Agent[0.5, 0.5].

ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models Agent[0.5, 0.5]

Reference 65

Resolution: malformed identifier; raw_fallback, observed 2026-05-18T20:52:33.938690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 2992c4e0-dd2d-42da-8445-a7a0f67d9f0e · inbound

Flow-GRPO: Training Flow Matching Models via Online RL cites this paper.

Flow-GRPO: Training Flow Matching Models via Online RL ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6535f3c3-f3f3-410c-addb-de6dcf3795fe · inbound

Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions cites this paper.

Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:01:19.371511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfe38482-8f06-465a-97f4-0d92e1ae9e09 · inbound

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs cites this paper.

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:26.538629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9945ac94-99ee-4cd5-bc09-518bca2d4311 · inbound

AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy cites this paper.

AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:35.394606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb35d26b-a10e-49c6-a693-7e217cb88bdb · inbound

OMEGA: Can LLMs Reason Outside the Box in Math? Evaluating Exploratory, Compositional, and Transformative Generalization cites this paper.

OMEGA: Can LLMs Reason Outside the Box in Math? Evaluating Exploratory, Compositional, and Transformative Generalization ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T18:46:57.916405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f20fba18-42b1-4061-b4f8-a82e513065ec · inbound

DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation cites this paper.

DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T22:52:33.390202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eae0d52c-25d5-417a-9a7f-e699cecff859 · inbound

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling cites this paper.

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:40:46.416012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fd7fd3a8-3250-479c-9eee-d7e955d58426 · inbound

CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization cites this paper.

CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T19:14:17.317668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da5b0f7a-d27a-42ab-b494-5c6f70157773 · inbound

Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback cites this paper.

Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T15:36:05.421844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a4edfb0-8eae-4bc5-b7ee-22f942df262d · inbound

Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR cites this paper.

Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:24:26.257703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1cd65a90-dbba-4744-bbdb-cdc1931fba4c · inbound

Re:Form -- Reducing Human Annotations in Scalable Formal Software Verification with RL in LLMs: A Preliminary Study on Dafny cites this paper.

Re:Form -- Reducing Human Annotations in Scalable Formal Software Verification with RL in LLMs: A Preliminary Study on Dafny ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T15:20:14.585900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dd56042-bd00-4b39-9500-092af60b8646 · inbound

Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learning cites this paper.

Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T14:53:04.512694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a4b9eac-311d-45a1-96ca-8af558b20872 · inbound

Towards Hallucination-Free Music: A Reinforcement Learning Preference Optimization Framework for Reliable Song Generation cites this paper.

Towards Hallucination-Free Music: A Reinforcement Learning Preference Optimization Framework for Reliable Song Generation ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T23:43:04.044724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b894881-4190-409d-9d68-3950e4797a83 · inbound

From Trial-and-Error to Improvement: A Systematic Analysis of LLM Exploration Mechanisms in RLVR cites this paper.

From Trial-and-Error to Improvement: A Systematic Analysis of LLM Exploration Mechanisms in RLVR ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T22:03:28.116441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de686465-cf53-4b9e-89c3-2a3fdf6af3de · inbound

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models cites this paper.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:07.634233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71057d06-6966-4285-b979-283f21bb2a11 · inbound

CURE: Critical-Token-Guided Re-Concatenation for Entropy-Collapse Prevention cites this paper.

CURE: Critical-Token-Guided Re-Concatenation for Entropy-Collapse Prevention ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T17:36:21.460084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55cc0bf5-2d45-4161-b7db-c60155982b52 · inbound

Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning cites this paper.

Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aa9b1cef-8270-4200-9f95-350fa9bf2956 · inbound

rStar2-Agent: Agentic Reasoning Technical Report cites this paper.

rStar2-Agent: Agentic Reasoning Technical Report ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-05T14:57:39.432202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 185b89bf-3df2-45a3-ada4-b3de776752b5 · inbound

SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning cites this paper.

SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T11:39:23.530235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7031dc5b-c461-444e-a4c3-f9889dbcefcb · inbound

Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR cites this paper.

Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T16:45:52.833521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5a2fb91-77c4-41df-a9a1-7264959a1673 · inbound

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey cites this paper.

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b6ae3d25-f8a1-4a16-9eda-4c4e34c279a1 · inbound

Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers cites this paper.

Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T16:37:52.919834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5c41cfa-13ad-4851-be0c-ddde473625ce · inbound

SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning cites this paper.

SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ed1f0e17-23bb-40a1-9489-9665a333a24a · inbound

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle cites this paper.

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 107

Resolution: unresolved; no resolver link, observed 2026-08-04T16:07:34.887140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 753d33ce-83a0-434f-8b0c-52661879e75f · inbound

DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search cites this paper.

DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9619e9b8-149d-40fc-b8af-29a8728bebe8 · inbound

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

When Importance Sampling Misallocates Credit: Asymmetric Ratios for Outcome-Supervised RL ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-05-21T20:30:35.530451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 20c5a131-08e4-4437-a6d9-a166faa0df27 · inbound

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

Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8768ed37-c50e-4366-b06c-99eaf0815fb1 · inbound

Representation-Based Exploration for Language Models: From Test-Time to Post-Training cites this paper.

Representation-Based Exploration for Language Models: From Test-Time to Post-Training ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:02.979065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 150fcd04-9d9e-427d-9dba-c88f8f72ac38 · inbound

The Art of Scaling Reinforcement Learning Compute for LLMs cites this paper.

The Art of Scaling Reinforcement Learning Compute for LLMs ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bac8f873-8339-4a8f-9c0d-b90c6164c2da · inbound

Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models cites this paper.

Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T08:15:53.349181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8b28bf1-c0ac-4f36-805a-8d86fc864981 · inbound

RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments cites this paper.

RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T23:08:07.481047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd087267-0ab3-4580-ab85-1469ec6bfc2b · inbound

Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning cites this paper.

Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 19cb6ca4-e3c7-42a9-886a-29c14fdec193 · inbound

Mimic Human Cognition, Master Multi-Image Reasoning: A Meta-Action Framework for Enhanced Visual Understanding cites this paper.

Mimic Human Cognition, Master Multi-Image Reasoning: A Meta-Action Framework for Enhanced Visual Understanding ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T11:13:16.861548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f81db7c7-8ba2-4c8a-910f-01f72ebd2662 · inbound

The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models cites this paper.

The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T09:03:18.768478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 839e7f3d-c551-46b8-96f9-79f52ad59d89 · inbound

Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning cites this paper.

Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 23075704-eeea-47cc-b239-ed2782eeeeb0 · inbound

Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models cites this paper.

Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-21T14:10:13.046565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91d121ed-b446-499c-8dbd-dc90fbd3dc0c · inbound

On the Emergence of Implicit Curriculum in RLVR Learning Dynamics cites this paper.

On the Emergence of Implicit Curriculum in RLVR Learning Dynamics ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T23:11:55.011109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bfa553f-d1b8-4998-8295-0797b2c3c779 · inbound

TiCo: Time-Controllable Spoken Dialogue Model cites this paper.

TiCo: Time-Controllable Spoken Dialogue Model ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b34124ea-dc9e-4db3-9eb5-0a692f4ec903 · inbound

Numerically Optimizing Shortcuts to Adiabaticity: A Hybrid Control Strategy cites this paper.

Numerically Optimizing Shortcuts to Adiabaticity: A Hybrid Control Strategy ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-13T14:28:35.916911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bd4f56d-4dc1-463d-9567-a72a04affa89 · inbound

SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models cites this paper.

SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 03a8463b-9a94-4055-8605-f1a3cbbc8500 · inbound

Characterizing Model-Native Skills cites this paper.

Characterizing Model-Native Skills ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 90

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d5663aad-3b44-4559-b8d2-59f0324274cf · inbound

HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment cites this paper.

HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 67539e43-f4b6-4bd8-a62c-aea2828bf0e6 · inbound

LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent cites this paper.

LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-05T15:11:10.854096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f0b0da62-cbde-4ef9-a153-fc8902bd412d · inbound

Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs cites this paper.

Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 849fb43e-80fd-4b73-9eb0-8242fbb32d33 · inbound

Breaking $\textit{Winner-Takes-All}$: Cooperative Policy Optimization Improves Diverse LLM Reasoning cites this paper.

Breaking $\textit{Winner-Takes-All}$: Cooperative Policy Optimization Improves Diverse LLM Reasoning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 844b31fa-83ab-44a1-8b2e-f7d5ebd5d7fd · inbound

Breaking $\textit{Winner-Takes-All}$: Cooperative Policy Optimization Improves Diverse LLM Reasoning cites this paper.

Breaking $\textit{Winner-Takes-All}$: Cooperative Policy Optimization Improves Diverse LLM Reasoning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-20T22:49:10.664024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 79c5031c-f0ec-4fe3-9a85-60c213abefb0 · inbound

Understanding and Preventing Entropy Collapse in RLVR with On-Policy Entropy Flow Optimization cites this paper.

Understanding and Preventing Entropy Collapse in RLVR with On-Policy Entropy Flow Optimization ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 44

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8046a357-0c77-4e27-bfa6-a6774ffc35ed · inbound

Seir\^enes: Adversarial Self-Play with Evolving Distractions for LLM Reasoning cites this paper.

Seir\^enes: Adversarial Self-Play with Evolving Distractions for LLM Reasoning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 407e4e2c-5068-4de7-ae40-e55547c2d641 · inbound

When Policy Entropy Constraint Fails: Preserving Diversity in Flow-based RLHF via Perceptual Entropy cites this paper.

When Policy Entropy Constraint Fails: Preserving Diversity in Flow-based RLHF via Perceptual Entropy ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 16cd8fdc-8158-4c29-9b74-d5e80f31b0cc · inbound

Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR cites this paper.

Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-20T19:18:54.379011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c1d5640c-0a04-4079-85cc-f7cb910bdb2b · inbound

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning cites this paper.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-21T08:14:03.312265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 05bf0cfb-0094-4d53-977e-58f0dab65e35 · inbound

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning cites this paper.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.267554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 649240f5-9b95-4ceb-a52d-9f92302ee866 · inbound

Combinatorial Synthesis: Scaling Code RLVR via Atomic Decomposition and Recombination cites this paper.

Combinatorial Synthesis: Scaling Code RLVR via Atomic Decomposition and Recombination ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-06-28T23:12:47.100067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 818e8dfd-a375-4be8-b94c-906bad8d0842 · inbound

On the Generalization Gap in Self-Evolving Language Model Reasoning cites this paper.

On the Generalization Gap in Self-Evolving Language Model Reasoning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-06-28T17:22:24.820455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 74d0f968-bad8-41f0-ae77-f77721cb68cd · inbound

Trust Region On-Policy Distillation cites this paper.

Trust Region On-Policy Distillation ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 225

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T20:56:13.555191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 92d3cf5e-fff5-4c56-872e-5a3bdd08238e · inbound

RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning cites this paper.

RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-07-01T21:16:13.376818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ee6a4c90-954b-4c34-bb64-172387919d13 · inbound

Robots Need More than VLA and World Models cites this paper.

Robots Need More than VLA and World Models ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 163

Resolution: verified exact; local_arxiv, observed 2026-06-28T01:11:28.844134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 84a3e069-37aa-4a20-9957-5255bebcc66f · inbound

Teacher-Free Self-Training Amplifies but Does Not Compound: A Pass@$K$ Crossover on a Free-Verifier Domain cites this paper.

Teacher-Free Self-Training Amplifies but Does Not Compound: A Pass@$K$ Crossover on a Free-Verifier Domain ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-07-02T16:47:09.127737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2b9d8924-e10e-4c4d-885e-763294d3745a · inbound

TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning cites this paper.

TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 39

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T04:27:36.689469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 940785f6-1537-4491-92c1-fa81b0913bec · inbound

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning cites this paper.

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-06-27T01:20:20.555477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a8904ec3-a755-437d-a82f-547e68bfb96d · inbound

Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients cites this paper.

Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:56.207246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5793ee26-189c-44b3-8447-eb8716704650 · inbound

Breaking the Solver Bottleneck: Training Task Generators at the Learnable Frontier cites this paper.

Breaking the Solver Bottleneck: Training Task Generators at the Learnable Frontier ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 127

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T08:57:48.118568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f5c25b2d-c968-4333-b469-e58d2bcee014 · inbound

RL Post-Training Builds Compositional Reasoning Strategies cites this paper.

RL Post-Training Builds Compositional Reasoning Strategies ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-09T04:05:55.490553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 99c812be-c174-4e18-a448-0094ca043f57 · inbound

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning cites this paper.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:38.160357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 486c41ab-4829-414a-898a-1eb672dc75eb · inbound

ISO: An RLVR-Native Optimization Stack cites this paper.

ISO: An RLVR-Native Optimization Stack ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T12:52:00.138146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03a342d7-a398-48f5-8ca1-385ab2de3150 · inbound

Early Verdicts, Better Budgets: Sequential Adaptive Rollout Allocation for Compute-Efficient RLVR cites this paper.

Early Verdicts, Better Budgets: Sequential Adaptive Rollout Allocation for Compute-Efficient RLVR ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T00:28:59.796151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7df0f2db-156f-4f71-9816-5ef0710f6e4a · inbound

Kalman Meets Curriculum: Efficient Dynamic Prompt Selection for Adaptive RL Finetuning cites this paper.

Kalman Meets Curriculum: Efficient Dynamic Prompt Selection for Adaptive RL Finetuning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-07-31T01:31:11.492967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4dcbe759-1785-4b01-93f2-7865b7afdc9f · inbound

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics cites this paper.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:25.365379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 469e5597-9969-4364-bdd8-aefef327d7dd · inbound

Parameter Exploration for RLVR via Variational Learning cites this paper.

Parameter Exploration for RLVR via Variational Learning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T10:32:12.916702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.