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Paper Citation Record · LEDGER

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training

As of 14 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2607.04574.

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

pith.paper-citation-record.v1
2607.04574 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T17:03:13.686066Z

measured 46 of 46 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T12:47:38.487160Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved44
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a4a6f1aa-9e60-421f-ac85-9843ab7cbd3c · outbound

This paper cites Intelligence per Watt: Measuring Intelligence Efficiency of Local AI.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

Reference 1

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:8dca98409aad8ff574f0620fe56b6f93f4fa3710f4ae0335315042fba5223c12

Observation 8007d496-32b3-4591-9926-e6da7ea2e3b4 · outbound

This paper cites and Zipser, David , journal=.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training and Zipser, David , journal=

Reference 2

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:2aa057b40950c5ed4b90af4a21a848cac24d09c0d2cb1b0363468c06d7e4409c

Observation 88a0abcf-61af-430d-8a42-75041dbfcbf4 · outbound

This paper cites an unresolved cited work.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:8ac63fd20978e2450723e5c37e8f4e3637e3d74454c2c80bc424e04a3cb5977e

Observation de0143eb-3404-4753-b9ef-b992d7665222 · outbound

This paper cites an unresolved cited work.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Unresolved cited work

Reference 4

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:83e69f8df59b223ab3808a423fb7b2aebbfd79c7e6997bde8da8cb8e0bf1d341

Observation 6fa9354c-facb-4729-935e-d860104701c5 · outbound

This paper cites an unresolved cited work.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:b79b69dc577134a1b6f7afa5fb9026bff364957c1bc378503eb654f7332d6cd3

Observation 02d6f244-0343-4cbd-a70c-83666b5ff433 · outbound

This paper cites Revisiting DAgger in the Era of LLM-Agents.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Revisiting DAgger in the Era of LLM-Agents

Reference 6

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:1da1b3ca300edce0edfa0ef895ec81f291f1ab13d68b0583a5d0d173a8f74d1b

Observation a7b4f5be-d21c-4bb6-9bb3-2a82f49e70c9 · outbound

This paper cites Reinforcement and Imitation Learning via Interactive No-Regret Learning.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Reinforcement and Imitation Learning via Interactive No-Regret Learning

Reference 7

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:b7756fb7beb84d670808770a170cf5dd8de1d15e8ce194c81e8a93d7a64e2aa7

Observation 503ce140-825b-4954-a783-06d5cebd2ade · outbound

This paper cites Decoupling.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Decoupling

Reference 8

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:c5a6b698855634f96ab54d9b351189ec0317bdbaaecb12e094af0b7a75ec6bd1

Observation 57fbe80e-f47c-450f-99f0-4207c8bb0187 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training ReAct: Synergizing Reasoning and Acting in Language Models

Reference 9

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:247484e63c25e8c421d2b6b3024d45a3080b8d65d1c27db9fccd9a91e2265b8d

Observation b1516430-12f0-4d32-85f5-a58d3e592763 · outbound

This paper cites Artificial Intelligence , volume=.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Artificial Intelligence , volume=

Reference 10

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:3e90700263217d0d14eef57bd06a9f0e8f3de1c465b6cca1858021f3109628ea

Observation a4cb4d53-42b6-423b-8fdf-d7055807ff9a · outbound

This paper cites Operations Research , volume=.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Operations Research , volume=

Reference 11

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:758fa87e3c5dcb22c9bf12df81038aa9631dea56590610b02f0647dbb8bbe099

Observation 7c85966e-e71f-43a4-9a14-5db49b224ac5 · outbound

This paper cites Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS) , pages=.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS) , pages=

Reference 12

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:d4ca911c772b45e0fbac5656762958199e3dfbe25db0ee25c3f87b17a14e891b

Observation 5b2f55a6-dc5f-4a99-bd2c-f6059e8492ad · outbound

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

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Proceedings of the Twentieth European Conference on Computer Systems , pages=

Reference 13

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:2b502ac2516e027c306c35c49861874c5aa5e7e201d79a700276ce7dd38b93a8

Observation 853ef247-2c29-4387-9220-961243b26dcd · outbound

This paper cites Second Conference on Language Modeling , year=.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Second Conference on Language Modeling , year=

Reference 14

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:796c2e5f2d267b5febbef8a204a76aa39b3ba2ea5c742fd3e408404d70e09943

Observation 0472669e-6763-4ff9-95eb-19038e9afd55 · outbound

This paper cites 2019 International Conference on Robotics and Automation (ICRA) , pages=.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training 2019 International Conference on Robotics and Automation (ICRA) , pages=

Reference 15

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:ddc372904c01ef8fb5122d35ecdd24f24bc331ac8a943e84f4370af39cbd0002

Observation 588f8a8e-d5b2-4120-9478-fa66f9ffd247 · outbound

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

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , pages=

Reference 16

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:07efdc33ed9ea072209f272dcee5d164d1a4d2e9fb8e3737c9cd8b777db2b310

Observation d07c2bb6-61dc-4a3f-914b-16cde9dc33bb · outbound

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

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=

Reference 17

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:7cd9c0afa6245e339e9ddb844a8f1f9fe46209aec1664b43be3e336754b199c7

Observation cb3d2ddf-bffe-4685-8983-a26f6cd09e2f · outbound

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

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Understanding R1-Zero-Like Training: A Critical Perspective

Reference 18

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:3d75c1a3a9d1b4105e5af2ef1b72fc64476703aa7bb15405a547844cd863cf21

Observation a225ac26-aac3-4622-83d2-e868b72d787c · outbound

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

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 19

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:0fc80f136836fec1b4b9306e7c9bfb8db17f6319978d0ff03f72f98dbbb67565

Observation f9a07e87-650f-462e-82a2-7644e02e69cd · outbound

This paper cites Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers) , pages=.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers) , pages=

Reference 20

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:76e146f7116e53d0271d55f721aa35fcf925b304f794c13a1576e855fc8285e4

Observation aeaca245-5a14-49a8-b05b-a12418aa4150 · outbound

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

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Transactions of the Association for Computational Linguistics , volume=

Reference 21

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:d641079243600d6a546167bb268fc3d17d98d99fe087c5fb3d4d01a0ede9824c

Observation 248d78b7-0775-4a04-97a5-d4f1c87411ee · outbound

This paper cites Proceedings of the 28th International Conference on Computational Linguistics , pages=.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Proceedings of the 28th International Conference on Computational Linguistics , pages=

Reference 22

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:d2596ea89d045d4b30a3c864de63adba1681283356df8231dd8a0282ef3f0f76

Observation 2a5aa2f9-40a9-43ea-b933-e5fdaada0a3d · outbound

This paper cites , booktitle =.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training , booktitle =

Reference 23

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:8fb379e1674f1e0d7c7d804953284d2a2e93441d0444dcf7ade880888a675e12

Observation 1636ccea-4aba-48e6-8343-5fdf78133542 · outbound

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

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training International Conference on Learning Representations , year =

Reference 24

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:045db9b1df867870dd1f20b9df4a9df6501c958d4cd7f9a52d06901c53190f84

Observation 09314f96-f9dd-4848-895b-6b7cc54ef882 · outbound

This paper cites 2024 , journal =.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training 2024 , journal =

Reference 25

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:e481c83a6b935fccf2202c2b262a0e2a666fa1c8e4573930787c6a6dbbc6d286

Observation f3f41295-1951-4ec3-8171-08b844327783 · outbound

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

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics , series =

Reference 26

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:f5079dd3f7751e7eb0d82147d8b85ec6eec5dc73ddc4b5ab87b74dad6f4249ba

Observation e907ab96-05b5-4dcf-91c1-32fa797487d2 · outbound

This paper cites NeurIPS 2024 Workshop on Open-World Agents , year =.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training NeurIPS 2024 Workshop on Open-World Agents , year =

Reference 27

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:f54d727e53105f2f0aea4a5cca42d889f21097ca388322a667b53fe4217b9a18

Observation 47f6c88c-8c9f-4753-b4ff-868fb9103e9d · outbound

This paper cites Better than Your Teacher:.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Better than Your Teacher:

Reference 28

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:5700df6b3b2ee7a5d7fa294d223d1884a84a4a6bd0737a33581966b1f0d16fc4

Observation 1aa0584e-d499-4442-89e9-c32061890140 · outbound

This paper cites Imitation Learning for Multi-turn.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Imitation Learning for Multi-turn

Reference 29

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:7f14e306bbf109f461f1b8744460dbd9a81c44e2e06d08285f284c0a9a9d5f18

Observation f5e889cd-5754-4c01-930d-a39b34d02dd7 · outbound

This paper cites arXiv preprint arXiv:2509.14257 , year =.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training arXiv preprint arXiv:2509.14257 , year =

Reference 30

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:bbcd808e78063a1a6bfb09441385cf3333259d43b2adbffbb3e93c0292459bc8

Observation 7566ed59-c623-4f54-a6e2-63510dd04df1 · outbound

This paper cites Training.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Training

Reference 31

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:0f227c0ea636a256d61facaf869148995db7674251ad3d024bc9957dcff63b5f

Observation 8887fa81-7399-464e-9b82-1e1030f2cfef · outbound

This paper cites Embodied Multi-Modal Agent trained by an.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Embodied Multi-Modal Agent trained by an

Reference 32

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:44d4e021d8ba50c40e8f6439746b9df2ac4275329f08ce6d309d2fe07d5720bb

Observation c12213ec-23f7-4cf3-a4ae-86a95516436e · outbound

This paper cites 2025 , url =.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training 2025 , url =

Reference 33

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:988706982821f0bd5873b263df893938ae460176c7e01b0290eab28f81280dcf

Observation a7b871b0-ba42-4796-bc99-7bd3c9962462 · outbound

This paper cites an unresolved cited work.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Unresolved cited work

Reference 34

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:d2caa39ee0a717483f6867bfc11d962ad642d7db605372bd812845e4626c0102

Observation f2a8967d-58be-4095-805c-67f9ddc5b2e2 · outbound

This paper cites 2025 , url =.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training 2025 , url =

Reference 35

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:e86deedccb218d2b8c9640669f70b520a49bdec556ed7b7665bcc39d202084e1

Observation b55981c8-268b-4ea0-9946-f82fc2f18af4 · outbound

This paper cites Exploring Expert Failures Improves.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Exploring Expert Failures Improves

Reference 36

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:469cb92ac9f5be30b060252a589c8b74b96a11ebb08a177e9b432d5601a4e105

Observation a77dc542-0136-4b45-a7e5-72b153350cd4 · outbound

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

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training International Conference on Learning Representations , year =

Reference 37

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:174e231ae7d14527d9d47a223edaf3f6cb1912ad348ae0edc18f68b4c7e2523a

Observation c3df3476-918e-41df-b903-4bfb0dda6760 · outbound

This paper cites 2024 , url =.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training 2024 , url =

Reference 38

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:439f5be6ae11ebd7486ddfddfe53f7663f2ccbb7d5a4a90205c800a9df7adc87

Observation 6ce9c6f4-a653-4776-9a20-937823b2a816 · outbound

This paper cites On-Policy Context Distillation for Language Models.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training On-Policy Context Distillation for Language Models

Reference 39

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:b2cbe69ff47280d5e1e32d7af30ee807ac26da2800a3873df5fb400e6eae0e6b

Observation d0602a50-ce15-414b-93b8-a369437ef6fd · outbound

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

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

Reference 40

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:ef5ea49b01c90c77b6aa6400f28928bee85ef3402075269220e7498010b8a5b6

Observation 0fd401ac-c066-45cc-8213-88020f972d53 · outbound

This paper cites 2026 , url =.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training 2026 , url =

Reference 41

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:094911010cee7308423b8f5c18edf99378fcc892684e8f7508f20d1c6b7ab53d

Observation c18eead2-63eb-4b81-a884-24cc6190f3c9 · outbound

This paper cites arXiv preprint arXiv:2511.10643 , year =.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training arXiv preprint arXiv:2511.10643 , year =

Reference 42

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:59bb137940e66b22d75776b72a4bc83d797129cd13209d8d9eb9cfc489aa2a65

Observation 0cedad13-6618-49ef-875e-d3d4b4adc21c · outbound

This paper cites NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System

Reference 43

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:10c32c5a3ba862c98ee3d2cd28d04b73f1da51cf312df8f7e60dfe33d207b2df

Observation 8f73cbfe-8871-4aba-ac4b-c7fe3dfa51cf · outbound

This paper cites 2023 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2023) , year=.

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training 2023 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2023) , year=

Reference 44

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:b8b6bad0212a5d60ffc0ad74ae88eeee5ee96f39a874d32e74a30f1b3234812a

Observation 6f56a063-4987-42b8-8411-20189f8b2049 · outbound

This paper cites Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved).

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)

Reference 45

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source=arxiv_source observed=2026-07-11T17:03:13.686066Z digest=sha256:8504a5c6fb4921aabd3a43bce48d341374b52a9b3f9376b077dbf8a8e403c719

Pith citing papers

Observation 8a7730c7-d11b-4168-b77c-e229d5970a63 · inbound

CodeRescue: Budget-Calibrated Recovery Routing for Coding Agents cites this paper.

CodeRescue: Budget-Calibrated Recovery Routing for Coding Agents A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training

Reference 19

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source=pdf_text observed=2026-08-01T12:47:38.487160Z digest=sha256:ccca3079e50f5a7aa214e89523d9e7efc9d8c7d3adeedc705cf5aca4b5e18cd9