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Paper Citation Record · LEDGER

EchoRL: Reinforcement Learning via Rollout Echoing

As of 23 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 2 inbound Pith citation observations for arXiv:2605.31228.

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

pith.paper-citation-record.v1
2605.31228 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T19:50:42.757895Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T09:07:47.405725Z

Reference resolution

32 of 32 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

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Outbound references

Observation 2302aa4c-2d59-462a-b0aa-4d1157930e8f · outbound

This paper cites Process Reinforcement through Implicit Rewards.

EchoRL: Reinforcement Learning via Rollout Echoing Process Reinforcement through Implicit Rewards

Reference 1

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local_arxiv, observed 2026-06-28T23:12:46.220546Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-07-11T11:50:26.030339Z digest=sha256:35833b3e7cec4c723f32fa20386dfef8ec46a336bdd0188b7566445577cbec2c

Observation 056bac2c-7df8-470f-a2cc-97dffe61c078 · outbound

This paper cites Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P.

EchoRL: Reinforcement Learning via Rollout Echoing Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P

Reference 2

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source=pdf_text observed=2026-06-28T23:08:19.084708Z digest=sha256:575c79418026459e8430f6e9715a0f590600263b0db03eeeb40cd68784fb95b8

Observation 64fdac0c-b326-4fdf-aef3-3d6dbb13befc · outbound

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

EchoRL: Reinforcement Learning via Rollout Echoing DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 3

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local_arxiv, observed 2026-06-28T23:12:46.217948Z

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source=pdf_text observed=2026-07-11T11:50:26.030339Z digest=sha256:33455289fda1248be96a3fec15f7f537d3e91b04bb27686d30b4ca4fc0adcaf5

Observation 483ecc1a-e794-4d2a-aeb2-a3fb23e32404 · outbound

This paper cites Method In-Distribution Performance Out-of-Distribution Performance AIME24 AIME25 AMC MATH-500 Minerva OlympiadAvg.

EchoRL: Reinforcement Learning via Rollout Echoing Method In-Distribution Performance Out-of-Distribution Performance AIME24 AIME25 AMC MATH-500 Minerva OlympiadAvg

Reference 4

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source=pdf_text observed=2026-06-28T23:08:19.084708Z digest=sha256:06bbb0421692b91ed8d34908de05c7eeb9447c024ca15962fe01b41874b9c355

Observation d4c1de3e-0f49-4268-aef4-1761696f16ca · outbound

This paper cites Actor Update Time.

EchoRL: Reinforcement Learning via Rollout Echoing Actor Update Time

Reference 5

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source=pdf_text observed=2026-06-28T23:08:19.084708Z digest=sha256:583b4ca1fedfe24614a4c50ba362d93ba0d285d3223bc418089acbd05902a604

Observation bd3ae559-313e-4d31-87e9-931b638c8cf9 · outbound

This paper cites Then the difference between the largest and smallest roots of $fˆ{\prime}(x)$ is $\qquad$ Q2: What are the four rollouts (R1–R4)? A2:We list the full trajectories (verbatim) below.

EchoRL: Reinforcement Learning via Rollout Echoing Then the difference between the largest and smallest roots of $fˆ{\prime}(x)$ is $\qquad$ Q2: What are the four rollouts (R1–R4)? A2:We list the full trajectories (verbatim) below

Reference 6

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Observation 0a941b97-a4bc-4818-86db-e1b34db77770 · outbound

This paper cites an unresolved cited work.

EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

Reference 7

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Observation a2e2cf9d-772d-43ab-b90e-9e2ee8431efd · outbound

This paper cites We need to find the difference between the largest and smallest roots of the derivative $fˆ{\ prime}(x)$.

EchoRL: Reinforcement Learning via Rollout Echoing We need to find the difference between the largest and smallest roots of the derivative $fˆ{\ prime}(x)$

Reference 8

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Observation 080decef-5ad1-429a-a4b2-ca2437799678 · outbound

This paper cites We can shift the polynomial to center the roots at the origin.

EchoRL: Reinforcement Learning via Rollout Echoing We can shift the polynomial to center the roots at the origin

Reference 9

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Observation 4492f3a0-b194-480d-92a1-9a4e8faf8311 · outbound

This paper cites The polynomial in the shifted variable $y$ is $g (y) = (y-3)(y-1)(y+1)(y+3)$.

EchoRL: Reinforcement Learning via Rollout Echoing The polynomial in the shifted variable $y$ is $g (y) = (y-3)(y-1)(y+1)(y+3)$

Reference 10

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Observation 1bf5d2d2-8350-479e-b5cf-85e889649063 · outbound

This paper cites an unresolved cited work.

EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

Reference 11

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Observation 0be60257-25a1-44f5-bcf0-71dde38d5970 · outbound

This paper cites an unresolved cited work.

EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

Reference 12

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Observation e07b9582-e346-44cd-91fc-6c34b94d51f3 · outbound

This paper cites This will be the final answer.

EchoRL: Reinforcement Learning via Rollout Echoing This will be the final answer

Reference 13

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Observation 9cb8966f-6268-434d-b06e-907d851ade8e · outbound

This paper cites an unresolved cited work.

EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

Reference 14

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Observation cd34062a-6a9a-4934-b5ed-45d77e19c0d8 · outbound

This paper cites Let’s map the roots to $\pm \frac{1}{2}, \pm \frac{3}{2}$.

EchoRL: Reinforcement Learning via Rollout Echoing Let’s map the roots to $\pm \frac{1}{2}, \pm \frac{3}{2}$

Reference 15

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Observation 55c8f786-a2a2-4d5a-8851-945767870ea4 · outbound

This paper cites an unresolved cited work.

EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

Reference 16

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Observation b777a1ba-b483-4dfa-b7ca-06ef8d61cb7c · outbound

This paper cites an unresolved cited work.

EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

Reference 17

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Observation e5487271-2967-4fbe-b9f9-697e5a70fc38 · outbound

This paper cites Since we scaled the coordinates by $1/2$, the distances in the $z$- domain are half the distances in the $x$-domain.

EchoRL: Reinforcement Learning via Rollout Echoing Since we scaled the coordinates by $1/2$, the distances in the $z$- domain are half the distances in the $x$-domain

Reference 18

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Observation 8b16f37f-bd0e-406c-9ef0-843c0b12740e · outbound

This paper cites Centering them at 0 yields the set $\{-3, -1, 1, 3\}$.

EchoRL: Reinforcement Learning via Rollout Echoing Centering them at 0 yields the set $\{-3, -1, 1, 3\}$

Reference 19

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Observation ec1fbbac-2603-46a6-8e82-e23e80ef2b76 · outbound

This paper cites an unresolved cited work.

EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

Reference 20

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Observation ccaf6248-c933-43ec-887b-268ecc50a41c · outbound

This paper cites This immediately implies that $gˆ{\prime}(0) = 0$, so $y=0$ is one critical point.

EchoRL: Reinforcement Learning via Rollout Echoing This immediately implies that $gˆ{\prime}(0) = 0$, so $y=0$ is one critical point

Reference 21

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Observation add22cf0-c8f2-4f1e-80fc-01e610a52872 · outbound

This paper cites an unresolved cited work.

EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

Reference 22

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Observation 3138149a-a03f-4ec5-bb8d-1fcef383797e · outbound

This paper cites The difference between the largest and smallest roots is $c - (-c) = 2c$.

EchoRL: Reinforcement Learning via Rollout Echoing The difference between the largest and smallest roots is $c - (-c) = 2c$

Reference 23

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Observation 8a352652-7b9e-47a2-acfa-ff874234c73e · outbound

This paper cites Let the shifted variable be $y$.

EchoRL: Reinforcement Learning via Rollout Echoing Let the shifted variable be $y$

Reference 24

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Observation bfc54b28-f004-4e32-b9d5-af6e66b4ecae · outbound

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EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

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Observation ee7ab65e-eddf-444c-a392-97b20fd8e09e · outbound

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EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

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Observation f488eec7-34ca-4f50-beba-cb567abdd6d2 · outbound

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EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

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Observation edf9124d-0542-402d-8a06-eca0ca640e3c · outbound

This paper cites an unresolved cited work.

EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

Reference 28

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Observation 83da192c-eb51-401a-b863-4e4841c6a0a2 · outbound

This paper cites Let’s solve using this method.

EchoRL: Reinforcement Learning via Rollout Echoing Let’s solve using this method

Reference 29

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Observation ded55c7d-fabc-47de-8082-8c83617c42c1 · outbound

This paper cites an unresolved cited work.

EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

Reference 30

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Observation 554a3841-e588-40ed-8c48-21329c553ae1 · outbound

This paper cites an unresolved cited work.

EchoRL: Reinforcement Learning via Rollout Echoing Unresolved cited work

Reference 31

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Observation a47eb624-3441-4af2-a2ae-32888c3ecd5b · outbound

This paper cites <think>\n thoughts </think>\n.

EchoRL: Reinforcement Learning via Rollout Echoing <think>\n thoughts </think>\n

Reference 32

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Pith citing papers

Observation 9721f396-6a7d-421c-94d3-feff44fec044 · inbound

IMAGINE: Adaptive Schema-Imagery Enhanced Composition for Composed Video Retrieval cites this paper.

IMAGINE: Adaptive Schema-Imagery Enhanced Composition for Composed Video Retrieval EchoRL: Reinforcement Learning via Rollout Echoing

Reference 52

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source=pdf_text observed=2026-06-27T19:50:42.757895Z digest=sha256:58e5eb6dfa3e17a2081cd1871d883809279af5828a1c0a17efa1dbebc78e8637

Observation cff150e1-95b5-4e44-8333-41f44a1f5abb · inbound

RankVR: Low-Rank Structure Perception and Value Recalibration for Robust Composed Image Retrieval cites this paper.

RankVR: Low-Rank Structure Perception and Value Recalibration for Robust Composed Image Retrieval EchoRL: Reinforcement Learning via Rollout Echoing

Reference 16

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source=pdf_text observed=2026-06-27T10:35:28.866038Z digest=sha256:047a1dc8ce971fc1dfc5441ccc0982013efa65574869b3c3e65d63f331566660