Paper Citation Record · LEDGER

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning

As of 20 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2608.09123.

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

pith.paper-citation-record.v1
2608.09123 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:11:38.289892Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

39 of 39 outbound references displayed

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verified fuzzy10
unresolved28
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External citation measurements

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Outbound references

Observation 4e9b37a6-534e-43c0-a234-3ec5e4e92e13 · outbound

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

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.073068Z

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Observation 2889924e-d62e-4057-9eb2-c97356e32dfe · outbound

This paper cites Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.080415Z

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Observation 0a6f03e2-8876-42b4-b54c-3710f18f9f07 · outbound

This paper cites arXiv preprint arXiv:2511.12344 , year=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning arXiv preprint arXiv:2511.12344 , year=

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.085238Z

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Observation e25f382d-7726-4dcd-8f98-3f52600eb963 · outbound

This paper cites Qwen3 Technical Report.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Qwen3 Technical Report

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.089807Z

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Observation 72b2f941-3755-4bb3-a9a8-6261975bf125 · outbound

This paper cites Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:11:38.921467Z

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Observation 08cc7842-2721-4c75-a21f-adc11d070f92 · outbound

This paper cites Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.100222Z

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Observation 0b4fe6c1-b781-4a5f-a769-ec0739941a65 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.105966Z

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Observation fe2f8e82-8e4d-4241-9d0c-09b7c8812357 · outbound

This paper cites Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.110587Z

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Observation 6d5fcef0-a15f-44e5-8771-6266d75b7602 · outbound

This paper cites Rubric-Guided Self-Distillation: Post-Training Without Rubric Verifiers.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Rubric-Guided Self-Distillation: Post-Training Without Rubric Verifiers

Reference 9

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Source-reported events for the cited work

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Observation ae9b5fbc-19c2-4f56-a2a5-01ccf9ddf461 · outbound

This paper cites Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.121096Z

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Observation 0bed4ef3-626f-49c7-a965-fa73d374b097 · outbound

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

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.126401Z

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Observation 84dfdcd7-c8fb-47c0-b765-55322c1a2eed · outbound

This paper cites Proximal Policy Optimization Algorithms.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.132503Z

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Observation 28322207-e553-462d-91b6-485a2295f2c9 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:11:38.897530Z

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Observation 3bdef624-99cb-46d0-9025-8d16377d591f · outbound

This paper cites HealthBench: Evaluating Large Language Models Towards Improved Human Health.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning HealthBench: Evaluating Large Language Models Towards Improved Human Health

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.142183Z

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Observation 64de9da0-61a2-4473-8bb0-e97d61f24011 · outbound

This paper cites LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.148136Z

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Observation 83fa3cc0-f6ed-4374-a9c0-f610deb970b2 · outbound

This paper cites Applied Sciences , volume=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Applied Sciences , volume=

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.153305Z

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Observation 0b5f20ae-92ad-4554-980c-951c20179e7e · outbound

This paper cites From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.158924Z

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Observation 99bee403-91c7-4697-b25d-9935a7208b85 · outbound

This paper cites Transactions of the Association for Computational Linguistics , volume=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Transactions of the Association for Computational Linguistics , volume=

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:11:38.871668Z

Source-reported events for the cited work

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Observation fc121aa3-0bed-4f75-af57-979f14a1cf01 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 19

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Observation 5cef6ef6-5384-43ba-a09c-4d0c8b7a577e · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.175090Z

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Observation 3de14e40-347c-4d9a-9978-5e867bbe0913 · outbound

This paper cites Proceedings of the Twentieth European Conference on Computer Systems , pages=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Proceedings of the Twentieth European Conference on Computer Systems , pages=

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.180318Z

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Observation 04377bda-206d-4a30-8fd3-fabdb4c3d6b6 · outbound

This paper cites DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.186179Z

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Unavailable: canonical work link unavailable.

Observation e2b52015-9197-4f6a-948a-1c52fc66f927 · outbound

This paper cites Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.192972Z

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Observation 7258defc-00ed-4389-b722-b44d0ab08d68 · outbound

This paper cites Qwen3-Coder-Next Technical Report.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Qwen3-Coder-Next Technical Report

Reference 24

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Observation aee86933-812c-4885-8a8b-4da80ce8a7f7 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 25

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Observation 83a8fd66-4828-47ab-8e32-3506cff0841e · outbound

This paper cites Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:11:38.828544Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8e66481c-668f-48c6-b8d5-3e6499a81165 · outbound

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RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning On the Creativity of Large Language Models

Reference 27

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Observation 1c96373c-fbe9-4cd6-a317-02282b3da307 · outbound

This paper cites HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation

Reference 28

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Observation 636e7c9e-4193-418b-8f5e-8bee23424b18 · outbound

This paper cites Nature , volume=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Nature , volume=

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:11:38.815571Z

Source-reported events for the cited work

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Observation 91db8d45-85b1-4afa-a212-92576f8c04a9 · outbound

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RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Nature medicine , volume=

Reference 30

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Observation 241a1351-f8bc-4b7d-aa6a-a1e5294f9b31 · outbound

This paper cites SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation

Reference 31

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This paper cites Proceedings of the 34th ACM International Conference on Information and Knowledge Management , pages=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Proceedings of the 34th ACM International Conference on Information and Knowledge Management , pages=

Reference 32

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Observation 6adbf349-02cd-4418-84e7-31ccbcb8747b · outbound

This paper cites Proceedings of the 58th annual meeting of the association for computational linguistics: system demonstrations , pages=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Proceedings of the 58th annual meeting of the association for computational linguistics: system demonstrations , pages=

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:11:38.773222Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c57b886f-9d57-441b-9441-3167ad8120cf · outbound

This paper cites Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume , pages=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume , pages=

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:11:38.758328Z

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Observation cb1e5e7e-dcdf-4ff3-86db-55733153b85e · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning LaMDA: Language Models for Dialog Applications

Reference 35

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Observation fdf34d31-bf96-4e2e-a42f-05c2132e88ae · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:11:38.742637Z

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Observation ff5e8cbd-2f24-4256-ab36-2b320446ab19 · outbound

This paper cites Reinforcement Learning via Self-Distillation.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Reinforcement Learning via Self-Distillation

Reference 37

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Observation 7e05d968-6086-4871-b0ca-baa86e11f4bd · outbound

This paper cites ROSD: Reflective On-Policy Self-Distillation for Language Model Reasoning across Domains.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning ROSD: Reflective On-Policy Self-Distillation for Language Model Reasoning across Domains

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T23:11:38.284922Z

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Observation 37745c5f-89e8-4a9d-a9f0-a24d32e33a31 · outbound

This paper cites Findings of the Association for Computational Linguistics: ACL 2026 , pages=.

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning Findings of the Association for Computational Linguistics: ACL 2026 , pages=

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:11:38.729881Z

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Pith citing papers

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