Paper Citation Record · LEDGER

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

As of 20 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 5 inbound Pith citation observations for arXiv:2607.14777.

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

pith.paper-citation-record.v1
2607.14777 v1

Coverage vector

measured 74 of 74 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:10:21.309226Z

measured 79 of 79 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:53:25.906690Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T16:39:07.928445Z

Reference resolution

74 of 74 outbound references displayed

verified exact1
verified fuzzy0
unresolved73
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a03c48b1-cd15-4d44-8254-c88cd72f9b0b · outbound

This paper cites Frontiers of Computer Science , year =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Frontiers of Computer Science , year =

Reference 1

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Observation c72e6d0e-3cab-46ec-b6ee-40506578a36c · outbound

This paper cites Science China Information Sciences , year =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Science China Information Sciences , year =

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.060602Z

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Observation 8b2c3d6e-284e-48e1-9e3b-15e6679d8e84 · outbound

This paper cites 2023 , url =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2023 , url =

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.064601Z

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Observation 152a5d64-f8ea-47f1-9d85-d4258411fbe1 · outbound

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

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Advances in Neural Information Processing Systems , year =

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.067985Z

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Observation f6da2a51-3815-47c8-995e-353edaf50a2a · outbound

This paper cites and Zhang, Tianjun and Wang, Xin and Gonzalez, Joseph E.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning and Zhang, Tianjun and Wang, Xin and Gonzalez, Joseph E

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.071848Z

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Observation 735f7248-9106-4fb7-85d5-96a9e5dba52b · outbound

This paper cites AgentBench: Evaluating LLMs as Agents.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning AgentBench: Evaluating LLMs as Agents

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.075066Z

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Observation e3544c73-9a32-41bd-83c1-9a1c3fa04c38 · outbound

This paper cites International Conference on Learning Representations , year =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning International Conference on Learning Representations , year =

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.078656Z

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Observation 1314b44e-ecc8-456a-8ab7-a645069c1df4 · outbound

This paper cites 2022 , url =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2022 , url =

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.082487Z

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Observation fadb58b4-bc51-45b9-8d8f-3f92ea4a6d6d · outbound

This paper cites 2023 , url =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2023 , url =

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.085614Z

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Observation cdb03de1-827e-423b-a58d-9b64f9ff13b0 · outbound

This paper cites an unresolved cited work.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.089481Z

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Observation c2a3ee49-38d9-4e0c-be0b-8967527162ab · outbound

This paper cites and Yang, John and Wettig, Alexander and Yao, Shunyu and Pei, Kexin and Press, Ofir and Narasimhan, Karthik , booktitle =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning and Yang, John and Wettig, Alexander and Yao, Shunyu and Pei, Kexin and Press, Ofir and Narasimhan, Karthik , booktitle =

Reference 11

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Observation d635e683-85f3-4f99-a6ab-f7e55e92cf7b · outbound

This paper cites 2024 , url =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2024 , url =

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.096970Z

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Observation fb257b68-31f4-4103-85d1-ef3270f9456f · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 13

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Observation 03e31e2b-db01-49f5-a0d9-721d3bfd7ac2 · outbound

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

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Transactions of the Association for Computational Linguistics , volume =

Reference 14

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Observation aa2cffdd-89c4-4a7b-a48a-739fd4ef3717 · outbound

This paper cites 2017 , doi =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2017 , doi =

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.107292Z

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Observation 4e832734-d4b2-4633-83e7-b6dd533798ca · outbound

This paper cites Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics , pages =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics , pages =

Reference 16

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Observation e8c86843-8607-424a-8731-9d2d5dd9c9e4 · outbound

This paper cites , booktitle =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning , booktitle =

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.113579Z

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Observation 023c511b-a824-4c9a-bcac-9d2c93dbff43 · outbound

This paper cites Constructing A Multi-hop.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Constructing A Multi-hop

Reference 18

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Observation b56077fd-e7f8-424b-be3c-07d6e345a943 · outbound

This paper cites 2022 , doi =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2022 , doi =

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.121110Z

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Observation 6adbdda8-0d87-4ade-b897-d5e465d1cca4 · outbound

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

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Findings of the Association for Computational Linguistics: EMNLP 2023 , pages =

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.125187Z

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Observation 72da754a-404e-497f-b56c-6fda9e9ae218 · outbound

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

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Advances in Neural Information Processing Systems , year =

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.128541Z

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Observation f3ffc495-9d64-4fc6-84d8-312a3d99acb2 · outbound

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

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.131519Z

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Observation be013677-665f-491e-bb22-7ae8ab1adb42 · outbound

This paper cites RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.134716Z

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Observation 617abad4-a511-40ae-8edb-cad265016600 · outbound

This paper cites Agent Lightning: Train ANY AI Agents with Reinforcement Learning.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Agent Lightning: Train ANY AI Agents with Reinforcement Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.137852Z

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Observation 59c1fd48-65c8-411c-87e9-d3d7961c1f04 · outbound

This paper cites 2017 , eprint =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2017 , eprint =

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.141619Z

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Observation 523e20dd-e4d8-4779-a714-f31c5c969389 · outbound

This paper cites International Conference on Learning Representations , year =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning International Conference on Learning Representations , year =

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.145229Z

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Observation 0e4eda5e-ab2f-4102-bd2e-8e91cb38d2a5 · outbound

This paper cites and Gillhofer, Michael and Widrich, Michael and Unterthiner, Thomas and Brandstetter, Johannes and Hochreiter, Sepp , booktitle =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning and Gillhofer, Michael and Widrich, Michael and Unterthiner, Thomas and Brandstetter, Johannes and Hochreiter, Sepp , booktitle =

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.148791Z

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Observation 84763708-eda6-4e65-b896-49058a11f9d2 · outbound

This paper cites The Landscape of Agentic Reinforcement Learning for.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning The Landscape of Agentic Reinforcement Learning for

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.152437Z

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Observation 80965d57-ab3a-4619-a89a-834df7f72f08 · outbound

This paper cites 2022 , eprint =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2022 , eprint =

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.156035Z

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Observation 3f07c628-a40b-4ac7-824d-f2385a44f6c0 · outbound

This paper cites International Conference on Learning Representations , year =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning International Conference on Learning Representations , year =

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.159439Z

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Observation 495aef61-8b5d-4f23-b167-ca8f305b7252 · outbound

This paper cites 2021 , eprint =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2021 , eprint =

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.162828Z

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Observation 0d8485aa-538a-43ba-92d7-ef68f0af247d · outbound

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

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Advances in Neural Information Processing Systems , year =

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.166830Z

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Observation 58e38d23-177f-4eed-8343-7b3981afaedc · outbound

This paper cites 2024 , url =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2024 , url =

Reference 33

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Observation decc5674-ad4b-488b-aacf-5882aa543028 · outbound

This paper cites 2023 , eprint =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2023 , eprint =

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.173954Z

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Observation 082d9a64-5642-4318-b68a-7d791e29e82d · outbound

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

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Advances in Neural Information Processing Systems , year =

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.176966Z

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Observation 69e6d170-8d97-4f06-88eb-24e6d293c2d4 · outbound

This paper cites 2015 , eprint =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2015 , eprint =

Reference 36

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Observation cfe4140c-27ae-42bc-a343-271b2e3f906a · outbound

This paper cites Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , pages =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , pages =

Reference 37

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Observation 2bab3260-8058-4a74-93df-f92f9a125470 · outbound

This paper cites Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics , pages =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics , pages =

Reference 38

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Observation c2aead90-2999-4aa2-9e5e-eb91185f490f · outbound

This paper cites International Conference on Learning Representations , year =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning International Conference on Learning Representations , year =

Reference 39

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Observation 1cc2aa2a-f8fb-40d1-a797-9df933f390df · outbound

This paper cites 2026 , eprint =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2026 , eprint =

Reference 40

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Observation 4343d417-ab8b-42df-bab0-c1bf13219df7 · outbound

This paper cites 2026 , eprint =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2026 , eprint =

Reference 41

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Observation b6331b0d-33fa-49ec-ba2d-8e864506e8f9 · outbound

This paper cites Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents

Reference 42

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Observation 37b037c6-1def-4f23-af13-511901105b87 · outbound

This paper cites Self-Distilled RLVR.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Self-Distilled RLVR

Reference 43

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Observation 7039d7d7-3d0f-41d0-9b33-6e6203180fe0 · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2026 , eprint =

Reference 44

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Observation 7a314ad0-18a8-488a-b9d8-b62c70a61dbb · outbound

This paper cites SOD: Step-wise On-policy Distillation for Small Language Model Agents.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning SOD: Step-wise On-policy Distillation for Small Language Model Agents

Reference 45

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Observation c7b042b2-5b7a-49ce-8015-990f5969e8e7 · outbound

This paper cites 2026 , eprint =.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2026 , eprint =

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.211611Z

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Observation bf122c46-a460-450d-bb6d-3e530775a299 · outbound

This paper cites OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.214520Z

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Observation c6532049-0386-47a5-b6be-eece2ac59a81 · outbound

This paper cites Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS

Reference 48

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Observation 27cdb7a9-3d91-4010-90bd-d8a0171ee47d · outbound

This paper cites Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles

Reference 49

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Observation d5d9fd56-88e4-4bad-a658-4d40cc437e50 · outbound

This paper cites Advances in neural information processing systems , volume=.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Advances in neural information processing systems , volume=

Reference 50

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Observation 735c0b4b-579e-4418-be6a-02ab0827423d · outbound

This paper cites Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism

Reference 51

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Observation ae2fdea5-7636-4866-84c8-4c760c4c676c · outbound

This paper cites Large Language Model Agent: A Survey on Methodology, Applications and Challenges.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Large Language Model Agent: A Survey on Methodology, Applications and Challenges

Reference 52

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Observation 4bffe0ec-6079-4833-881a-2253446f9b02 · outbound

This paper cites RobotEQ: Transitioning from Passive Intelligence to Active Intelligence in Embodied AI.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning RobotEQ: Transitioning from Passive Intelligence to Active Intelligence in Embodied AI

Reference 53

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Observation ea0eca4e-ef45-4e0c-b91f-86b8bb820826 · outbound

This paper cites SPARK : Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning SPARK : Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning

Reference 54

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Observation af3179df-4eb4-4276-ac8f-2bcd261284e6 · outbound

This paper cites OdysseyArena: Benchmarking Large Language Models For Long-Horizon, Active and Inductive Interactions.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning OdysseyArena: Benchmarking Large Language Models For Long-Horizon, Active and Inductive Interactions

Reference 55

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Observation ad8dcda3-0ce8-4684-ba55-2e60184c5923 · outbound

This paper cites DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation

Reference 56

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Observation 9639b6e7-7ac7-43e9-b175-c810cb57eae4 · outbound

This paper cites ATLAS : Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning ATLAS : Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning

Reference 57

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 2026 , eprint=

Reference 58

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Observation 35a0467d-beb6-4029-969f-688446922386 · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning arXiv preprint arXiv:2601.18137 , year=

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.259062Z

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Observation 84746ed0-21e8-473f-a199-62eb1b6413ca · outbound

This paper cites VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks

Reference 60

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Observation 592a29e5-73d6-4c40-a5c4-d799c0ae4241 · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization

Reference 61

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Observation e75e843f-7333-4a22-a0dc-f831cc737ecb · outbound

This paper cites Qwen2.5 Technical Report.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Qwen2.5 Technical Report

Reference 62

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Observation 6ad18bb9-7b1f-4b0f-a28f-e6014469903d · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Qwen3 Technical Report

Reference 63

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Observation f0432ea4-cd50-4567-9c96-6c3044859257 · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Qwen2.5-VL Technical Report

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.275184Z

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Observation 4e0c3948-de58-4b2e-871b-193bf04ddd72 · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning , url =

Reference 65

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Observation 6bb645eb-5377-4df9-9c3f-228674e0260e · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning , title =

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.282994Z

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Observation 83b1f7b7-314b-44b2-9291-4dc98d2fdcae · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.285839Z

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Observation 8f44b57f-241f-41ad-897d-649b179f1636 · outbound

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Reference 68

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Observation a1e6bcff-1dcd-412e-b21a-1df5ac5fae9a · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Forty-third International Conference on Machine Learning , year=

Reference 69

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Observation ff752704-dcd9-4159-8923-4750dd7bee3d · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.296070Z

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Observation f65b1c8a-57e0-4454-8180-1a46979f5148 · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning International Conference on Learning Representations , year=

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.299566Z

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Observation 82147def-5fc7-4604-aecb-5efb3a05916b · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-02T01:10:21.302473Z

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Observation fd0a1e02-7863-4bd0-aa72-a64d88f4abbf · outbound

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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 73

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Observation 8aafa1ea-e4aa-4d18-9d27-de137671f082 · outbound

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Reference 74

Resolution: verified exact; doi, observed 2026-08-02T01:13:58.398667Z

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Pith citing papers

Observation 2530b23c-e47a-438c-ba50-134125a446bb · inbound

EvoReason: Self-Evolving Reasoning Primitive-Guided On-Policy Distillation for Latent Reasoning in Generative Recommendation cites this paper.

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Reference 44

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Observation ab03c17b-e6c7-45cb-9dca-f1fb3855e2d1 · inbound

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MAGA: Multi-Platform Self-Fusion of GUI Agents via Structured Action Distillation SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

Reference 82

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Observation ae6bd321-e27e-454b-8cf6-342a6b55124e · inbound

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PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

Reference 17

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Observation a54fa22c-d4a9-4e5c-b091-7fcec56b98e6 · inbound

Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation cites this paper.

Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

Reference 14

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Source-reported events for the cited work

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

Observation 0c69cbd7-d25b-445e-a4ff-0cc00bd9f76b · inbound

AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning cites this paper.

AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T19:53:25.906690Z

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