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Paper Citation Record · LEDGER

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

As of 14 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 2026-08-14T06:32:32.682623+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

  • verified exact0
  • verified fuzzy10
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

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

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source=arxiv_source observed=2026-08-11T23:11:38.073068Z digest=sha256:2b4167a3fa5492f91d10d93200ad77dc8face6d9c9ead13979919e0fd55e8a51

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

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source=arxiv_source observed=2026-08-11T23:11:38.080415Z digest=sha256:765fa4be169651ea35df93c254e208d6767f3af74f9558f9795cfc4fa31b234a

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

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source=arxiv_source observed=2026-08-11T23:11:38.085238Z digest=sha256:c03190557bee3540683e52a5dbb52059d164ce86326170a88139d7529117b020

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

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source=arxiv_source observed=2026-08-11T23:11:38.089807Z digest=sha256:1170aea29492557fba0d64b68006fe7412dac39813cb06d51f12284bdc037bb7

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

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raw_fallback, observed 2026-08-11T23:11:38.921467Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T23:11:38.095300Z digest=sha256:a6b911f42ddb6b4c5864227a5d7e16a265cfc38994ab9aa4345a89d342c985a2

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

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source=arxiv_source observed=2026-08-11T23:11:38.100222Z digest=sha256:f863d2ba6a28293d5b06f730154743c2e78741d98569727cff901ee012d974c6

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

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source=arxiv_source observed=2026-08-11T23:11:38.105966Z digest=sha256:b0c78707f8adfed94341e441ba2b2c86849e052bb4edf0625468b9496edb3357

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

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source=arxiv_source observed=2026-08-11T23:11:38.110587Z digest=sha256:87776ef4f183934c80b5bad45665d8917548744fc2ed2377a76caba736da3bf5

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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local_arxiv, observed 2026-08-11T23:11:38.568943Z

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source=arxiv_source observed=2026-08-11T23:11:38.115837Z digest=sha256:83b4bd39010d57112f6195807771f15ada76fa91081a7b3369faee4ffcf66900

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

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source=arxiv_source observed=2026-08-11T23:11:38.121096Z digest=sha256:1c20c56939169e71483f7c605e95034e4d74e3ccdbd006f2d8b95a625ccbfb69

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

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source=arxiv_source observed=2026-08-11T23:11:38.126401Z digest=sha256:c0f3138872d8b9df736fd22e2d3f6ed435fe9689ee32b642cb8b6ae61d29fb20

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

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source=arxiv_source observed=2026-08-11T23:11:38.132503Z digest=sha256:e89d70d7633ff6edbbb462408d2735efbda35a7e4164190852b084932497a757

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

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raw_fallback, observed 2026-08-11T23:11:38.897530Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T23:11:38.137627Z digest=sha256:54aa198aa0f12dbb2ba6bbc4b9e21e5e84fc7fee0b919df6f96ba3311929b3ab

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

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source=arxiv_source observed=2026-08-11T23:11:38.142183Z digest=sha256:70533390a1e2cfcf64a79f7f4599d7b8358904217fa5d1dd18b0c1acfb6aa5ee

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

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source=arxiv_source observed=2026-08-11T23:11:38.148136Z digest=sha256:271d4fec4008cef4d488982820616453536af4f5281c420cb9d601c159428ed1

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

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source=arxiv_source observed=2026-08-11T23:11:38.153305Z digest=sha256:0e95c2d8af072e4060f28a94735b02bff66b888c2b8393dc9d15c12a5ef5b022

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

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source=arxiv_source observed=2026-08-11T23:11:38.158924Z digest=sha256:c6b60a1e0cc4cfe9a6086cca52f1e2fec86c30cd3fe66ef6329b3bbd0f20fbf8

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

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raw_fallback, observed 2026-08-11T23:11:38.871668Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T23:11:38.164677Z digest=sha256:e7f661966ddaf2940bfc45656bb974fe681981cbcc57649a7f36f8dd300fc4ad

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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source=arxiv_source observed=2026-08-11T23:11:38.170181Z digest=sha256:6c94d10efec4666fbbf43384baec4c28e6d5e93eed24be0e384fe54994997e40

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

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source=arxiv_source observed=2026-08-11T23:11:38.175090Z digest=sha256:c3b5bc2d4ee018a4be26b4d5310738653bfbeee77ea29256954c52d1ff8385d0

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

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source=arxiv_source observed=2026-08-11T23:11:38.180318Z digest=sha256:c088f40029ceed4bda658311302cdb8ac64a54dbe23518c379d694a236a63447

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

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source=arxiv_source observed=2026-08-11T23:11:38.186179Z digest=sha256:233e85e0f7ed7748ae64645812cd9fe4cbce1541c477b0c76bd9bd4d1e97cb9a

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

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source=arxiv_source observed=2026-08-11T23:11:38.192972Z digest=sha256:58dafe0c854e19e5cf4e051d1dd6dbf69022cdb189a60655543f07451989c665

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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source=arxiv_source observed=2026-08-11T23:11:38.197504Z digest=sha256:301437d888e9230419449efed321124ad95246b05132112c9e3794cf092f4dab

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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source=arxiv_source observed=2026-08-11T23:11:38.204640Z digest=sha256:a0019d25bbc726eb753764d6ba5ff1ad2b603f5dd07270750cebbdc372838677

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

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T23:11:38.210105Z digest=sha256:73a8e73dedda0eb918aa2d43c215ed9b3f29295575652518cf90f9e510f1e020

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

This paper cites On the Creativity of Large Language Models.

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

Reference 27

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source=arxiv_source observed=2026-08-11T23:11:38.214884Z digest=sha256:e9e5056b3bec27e1f9994d3d8b179c37b8fc1efa4e20ee3a62487cfbe7a6fa04

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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source=arxiv_source observed=2026-08-11T23:11:38.219627Z digest=sha256:f1b6ab870d1445e76aa9821ad8e6c93b429969c8047cd30e8be5d5f40707b1be

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

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raw_fallback, observed 2026-08-11T23:11:38.815571Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T23:11:38.225075Z digest=sha256:77eadafd6dfe9d872ab97dd06b5b1e57280e0005e80c00abc4748d8523be3eec

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

This paper cites Nature medicine , volume=.

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

Reference 30

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source=arxiv_source observed=2026-08-11T23:11:38.229833Z digest=sha256:66dfcd72b709d37c0f33b6e88d00ed3617a199eb9940d9e1c6a96608b2a59214

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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source=arxiv_source observed=2026-08-11T23:11:38.236628Z digest=sha256:f5b2abfc0de379f43868eea0a26f28e4aa61da22da099fe391c9749a3d73eb6c

Observation 58c720c6-929e-4461-896f-464740834da5 · outbound

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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raw_fallback, observed 2026-08-11T23:11:38.787405Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T23:11:38.243254Z digest=sha256:5205e5fbfd69c619778c2abcf2155d891ed9c9ec1a185c9d3d4716b6af38af0b

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

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raw_fallback, observed 2026-08-11T23:11:38.773222Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T23:11:38.249500Z digest=sha256:cd092e27327d6d881afba638d8e163da0e9b14a297e575d13096debd216023c3

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

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raw_fallback, observed 2026-08-11T23:11:38.758328Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T23:11:38.259339Z digest=sha256:90a9f9b0274f885c11731a3ff7cb1316bc896a1f083197076dd085870dba5d08

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T23:11:38.266959Z digest=sha256:400b325e407c0bca1eec2a185543d579a3f3a206617a157be06076d6690740f8

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T23:11:38.273675Z digest=sha256:5a46e180b3975cd7247b39a469ee884827d1403299caa68b97242192e2d0599a

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

Resolution
unresolved
no resolver link, observed 2026-08-11T23:11:38.279670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T23:11:38.279670Z digest=sha256:a6d0d9bca1a61e12a94e34db51d286eb80a0e29c0a8582eef1164ff55968ff60

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T23:11:38.284922Z digest=sha256:9553986526f319b3d30dcd5666542992db33a03b398a45482830944c06da5e68

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T23:11:38.289892Z digest=sha256:aeb7d30362519f21335cd1a420107499396f4d90f9ac2d1ec42710adced7cfd3

Pith citing papers

No inbound Pith citation observations are available.