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Paper Citation Record · LEDGER

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning

As of 23 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 2 inbound Pith citation observations for arXiv:2602.02192.

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

pith.paper-citation-record.v1
2602.02192 v5

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T05:32:43.300084Z

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 2026-08-02T11:14:02.042304Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T14:20:29.515746Z

Reference resolution

32 of 32 outbound references displayed

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  • verified fuzzy0
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fd332074-d05c-4168-a0d0-555cfd9ae0fc · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 1

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source=pdf_text observed=2026-08-03T05:32:40.373432Z digest=sha256:793bfbb29e2dfc3d310fab70905449408c6bca614fdc29068d964f25e14135c4

Observation b736313e-0f23-4fbc-a511-49330b90fc59 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2

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source=pdf_text observed=2026-08-03T05:32:40.415345Z digest=sha256:8cca7ee507d2d105a90ee3d5a82c4f0dafc4fcbac916ce035a107e3e447ced5f

Observation aaf7e38d-3e5e-4cb7-8a64-a84d9bd2f09f · outbound

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

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 3

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source=pdf_text observed=2026-08-03T05:32:40.495376Z digest=sha256:1ed6731d2b0987487344801ea945d2f8ab445eb1313a0e218fb5aaf3c6eecca8

Observation 5769c27c-b16c-4a2a-8016-ed45413e7fe7 · outbound

This paper cites Proximal policy optimization algorithms, 2017.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Proximal policy optimization algorithms, 2017

Reference 4

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source=pdf_text observed=2026-08-03T05:32:40.591203Z digest=sha256:7765b62f712acdd0601c96b0e7a490b6566cae2d8d8c780c0c6cafbd75611d02

Observation 94293c0e-e9fd-4b58-9554-6c3bb9a181ae · outbound

This paper cites an unresolved cited work.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Unresolved cited work

Reference 5

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source=pdf_text observed=2026-08-03T05:32:40.701006Z digest=sha256:6dadf74a6a39ac2cc18300dcf8bdbeec2a1d3f542ef4a45636b0a2b9c6da7472

Observation 0108285f-5b65-4507-b5eb-b96e32f72cf8 · outbound

This paper cites Hybridflow: A flexible and efficient rlhf framework.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Hybridflow: A flexible and efficient rlhf framework

Reference 6

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source=pdf_text observed=2026-08-03T05:32:40.776914Z digest=sha256:0541d7a187175533d28a239ff38d027335fec511288a8f80c4d972820720b06f

Observation af19c22a-1aa9-4c5b-87fb-42df5799b123 · outbound

This paper cites AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning

Reference 7

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source=pdf_text observed=2026-08-03T05:32:40.837833Z digest=sha256:927e44e27d21b52a289b23d2207864291f481c850b12fafe9b5bef2d7c6617af

Observation 96d1c965-bc1d-46f9-bcf6-a69040c52b5e · outbound

This paper cites History Rhymes: Accelerating LLM Reinforcement Learning with RhymeRL.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning History Rhymes: Accelerating LLM Reinforcement Learning with RhymeRL

Reference 8

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source=pdf_text observed=2026-08-03T05:32:40.894045Z digest=sha256:78b50f79d9264f04591348b2acb3d196789a5561f6dfe8effa2f36fa2d33ace8

Observation eb6b8965-7c31-4ef1-a1bd-ef3c6fc643e8 · outbound

This paper cites Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models

Reference 9

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source=pdf_text observed=2026-08-03T05:32:40.971370Z digest=sha256:2d132179c502408182273c5b8f1d109d41f384866eb1ef2467ff2467b833812a

Observation 850b703d-a73a-4f39-aed9-60976c229ce6 · outbound

This paper cites StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation

Reference 10

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source=pdf_text observed=2026-08-03T05:32:41.063975Z digest=sha256:498f72d768990a7d92506c55fc382bb31c77336d38c355d1ad3dcf1b57045e50

Observation fae02c87-3c7d-444a-9f1d-ee03fb58aea1 · outbound

This paper cites Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms

Reference 11

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source=pdf_text observed=2026-08-03T05:32:41.151927Z digest=sha256:213041768e5507e40b0066022eecf170f4dfcd601bcae01406dd450bcacafb73

Observation 4df1d50b-d04f-4206-b4a9-fc9988bf9375 · outbound

This paper cites Petals: Collaborative inference and fine-tuning of large models.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Petals: Collaborative inference and fine-tuning of large models

Reference 12

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source=pdf_text observed=2026-08-03T05:32:41.253596Z digest=sha256:15487d95a4c4e9c9a5f2376a4970e7e92697d3192a56ccf9ce1597389350ba0d

Observation d687caa2-4372-4667-9d84-e19ce5eb68f9 · outbound

This paper cites Swarm parallelism: Training large models can be surprisingly communication-efficient.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Swarm parallelism: Training large models can be surprisingly communication-efficient

Reference 13

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source=pdf_text observed=2026-08-03T05:32:41.401706Z digest=sha256:a79a3c4cb646a5f4238984c1141e7d5cbf639699c352c3308e277bc5cbce754b

Observation 2ecfd94a-47e0-4d25-9fa2-aba0a051f621 · outbound

This paper cites Parallax: Efficient llm inference service over decentralized environment.arXiv preprint arXiv:2509.26182, 2025.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Parallax: Efficient llm inference service over decentralized environment.arXiv preprint arXiv:2509.26182, 2025

Reference 14

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source=pdf_text observed=2026-08-03T05:32:41.464947Z digest=sha256:3a97a98c617e7b01143ac3efaac87879ccb607b9cc60206dec53b9653b99a83a

Observation 455bd1ca-a0a2-495c-afcd-1c5a8499f237 · outbound

This paper cites Rlax: Large-scale, distributed reinforcement learning for large language models on tpus.arXiv preprint arXiv:2512.06392, 2025.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Rlax: Large-scale, distributed reinforcement learning for large language models on tpus.arXiv preprint arXiv:2512.06392, 2025

Reference 15

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source=pdf_text observed=2026-08-03T05:32:41.584396Z digest=sha256:9db11008a143bddbec2e0f9d18daba7e3d4189bc078708f421fe978d66f7392a

Observation 1f870cc5-b633-4e0b-94a4-aec29766f9b4 · outbound

This paper cites an unresolved cited work.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Unresolved cited work

Reference 16

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source=pdf_text observed=2026-08-03T05:32:41.689686Z digest=sha256:9d8c599de56f969a37771095d03182f0f37f73ac140df50804abc64be1184349

Observation 72d94e30-d90c-45a2-8518-98893edff2fe · outbound

This paper cites A survey of reinforcement learning from human feedback, 2024.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning A survey of reinforcement learning from human feedback, 2024

Reference 17

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source=pdf_text observed=2026-08-03T05:32:41.805004Z digest=sha256:625180ac1a2a47e1ee1c06b97683110eefa45476ac0a47ed112cf1da56dc3c34

Observation 2836fbd5-05ce-498c-a907-273c8b6042fd · outbound

This paper cites Areal-hex: Accommodating asynchronous rl training over heterogeneous gpus.arXiv preprint arXiv:2511.00796, 2025.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Areal-hex: Accommodating asynchronous rl training over heterogeneous gpus.arXiv preprint arXiv:2511.00796, 2025

Reference 18

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source=pdf_text observed=2026-08-03T05:32:41.886782Z digest=sha256:807bb912d8024b2b7c12a468329f4712a66695e117fe3be92721590c36ab6a83

Observation 8421379f-61e6-4d87-86cd-a33c9be4cd00 · outbound

This paper cites INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning

Reference 19

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source=pdf_text observed=2026-08-03T05:32:41.992143Z digest=sha256:76d83409d47260d21f6509eb37f23c4fda21c6c35973b1ea9102e62fbece4542

Observation f317fd13-8f3d-41f4-9670-5dc1e9ab3e3e · outbound

This paper cites Qiu, and Yuqing Yang.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Qiu, and Yuqing Yang

Reference 20

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source=pdf_text observed=2026-08-03T05:32:42.231201Z digest=sha256:5962292f7727688147ff9c9310f713027bd2aeae70612d801f5a4e0f96c50e4c

Observation ac433fdb-c1fc-421c-bb75-796b546a0a9c · outbound

This paper cites Prosperity before collapse: How far can off-policy rl reach with stale data on llms?arXiv preprint arXiv:2510.01161, 2025.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Prosperity before collapse: How far can off-policy rl reach with stale data on llms?arXiv preprint arXiv:2510.01161, 2025

Reference 21

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source=pdf_text observed=2026-08-03T05:32:42.345375Z digest=sha256:2e87da4437ce22cc2a26cdc1246ca9a92dfc4a8183b213bca3865331ac16ea6f

Observation b6b64a8b-f840-41b8-b7ae-3142e11314c9 · outbound

This paper cites Qwen3 Technical Report.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Qwen3 Technical Report

Reference 22

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Observation d228c218-06aa-42f9-949b-aaa8edfe2962 · outbound

This paper cites American invitational mathematics examination (AIME), 2024.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning American invitational mathematics examination (AIME), 2024

Reference 23

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source=pdf_text observed=2026-08-03T05:32:42.483348Z digest=sha256:8dbb9b7778956f28b46d540790fef8b2058dda6142701108cd35782a98ef78ef

Observation e0d579c9-2615-42c2-8439-a081d552c22b · outbound

This paper cites Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models

Reference 24

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source=pdf_text observed=2026-08-03T05:32:42.552709Z digest=sha256:e913cd2a8f039320ccc436fce597b532f352a4fb2eabd0a4f63252f356eee3fa

Observation 116157ac-edb2-424b-8894-81aeb5d6d64d · outbound

This paper cites Have llms advanced enough? a challenging problem solving benchmark for large language models.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Have llms advanced enough? a challenging problem solving benchmark for large language models

Reference 25

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source=pdf_text observed=2026-08-03T05:32:42.620970Z digest=sha256:4f8e3551d3413a68ad012f61b880713ec1a8ddd44f18c9517e203d4223bbd357

Observation 4dd2a08d-e0c7-4bf7-b6cd-a2ab1774ba98 · outbound

This paper cites HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics

Reference 26

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source=pdf_text observed=2026-08-03T05:32:42.714144Z digest=sha256:b2928758b2fe20472b54f3cd616f6da294ff6747bd642006e5861846eb075cc2

Observation d7034725-cdcd-4ec5-b007-245f53c7c885 · outbound

This paper cites Towards robust mathematical reasoning.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Towards robust mathematical reasoning

Reference 27

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source=pdf_text observed=2026-08-03T05:32:42.814718Z digest=sha256:f367322d3b40e83dd8aade8a8b8f86cc9a7c3a21ec075aa58185d26f85bb761a

Observation 1f678e05-4b38-44b7-89b7-87586fe4b4fc · outbound

This paper cites Qwen3 technical report, 2025.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Qwen3 technical report, 2025

Reference 28

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source=pdf_text observed=2026-08-03T05:32:42.880251Z digest=sha256:6fee623f359203cc7c8842492c9c807000cb4685059a96633c226094e9bee827

Observation 4cb84c57-40e0-4467-abfb-42345349719a · outbound

This paper cites Openai gpt-5 system card, 2025.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Openai gpt-5 system card, 2025

Reference 29

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source=pdf_text observed=2026-08-03T05:32:42.969166Z digest=sha256:1c012c2d7bf4f6826e4c6d5d595a239fb0cbe1d5ca96838e77dd32ff88887748

Observation d7554fe2-e003-4df1-851b-73403727d48c · outbound

This paper cites Grok 4 model card.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Grok 4 model card

Reference 30

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source=pdf_text observed=2026-08-03T05:32:43.038027Z digest=sha256:1391af6df8b34f388f22f7e528adeb6f8966e2639b797a2f221146151e18f876

Observation 623f8f9c-b9da-4be0-95a2-5d9ac5067be4 · outbound

This paper cites Claude sonnet 4.5 system card.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning Claude sonnet 4.5 system card

Reference 31

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source=pdf_text observed=2026-08-03T05:32:43.169909Z digest=sha256:69940415196b4b6e4b7ebb98a36c5b57a06efc2ed9bf6069923218a09e0d25fb

Observation 93bdf701-1019-4e37-b5f2-86740f778b31 · outbound

This paper cites moba://hok_v2.

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning moba://hok_v2

Reference 32

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source=pdf_text observed=2026-08-03T05:32:43.300084Z digest=sha256:13bec96320b07da50acc9403b97d7c193943dc8b04109df5799eabb596aad300

Pith citing papers

Observation f336f62a-0335-4939-a84d-3dd93dec191b · inbound

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction cites this paper.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning

Reference 38

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arxiv_id, observed 2026-05-27T02:04:33.896984Z

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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=2026-05-10T14:20:21.472989Z digest=sha256:647cd99867dd4a71252e17e94804de6c28154cf15861c9eb9f888f1a631b84e0

Observation 73182606-3436-4471-b46d-db063113c303 · inbound

DynaResize: Runtime GPU Reallocation for Disaggregated LLM Post-Training cites this paper.

DynaResize: Runtime GPU Reallocation for Disaggregated LLM Post-Training ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning

Reference 33

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source=pdf_text observed=2026-08-02T11:14:02.042304Z digest=sha256:a895a8bef80ec226f3f9837c391ab4b1673f0afe4b4b49929aa89526062136ae