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Paper Citation Record · LEDGER

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning

As of 21 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 6 inbound Pith citation observations for arXiv:2603.13319.

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

pith.paper-citation-record.v1
2603.13319 v2

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T02:38:07.792710Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T19:05:59.651008Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T19:07:17.248530Z

Reference resolution

22 of 22 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved22
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5c47cb0a-1340-4f7d-82ac-388e4fa4af60 · outbound

This paper cites Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H

Reference 2

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source=pdf_text observed=2026-08-03T02:38:06.677290Z digest=sha256:f10d3d56cc48020912161b9d5d6ef54bb5c693f1f5b82e2286c81571e1c81efa

Observation 3941abfc-c6e8-4f92-9a74-a512fc5d6fc2 · outbound

This paper cites an unresolved cited work.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-03T02:38:06.860909Z digest=sha256:dde123718b6a649cc11bf15cfd83b3a53beca6196beb155be46e1ae951499ae5

Observation 63497513-2a0f-4229-b29f-efd07cf2549e · outbound

This paper cites DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation

Reference 5

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source=pdf_text observed=2026-08-03T02:38:06.994951Z digest=sha256:0a5838ec1305b8273658736b0815a7e1c030f0391d281eca5c3347f75b0c4391

Observation 4b9709a4-caf6-41ee-92c6-b18fdc339f1d · outbound

This paper cites Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J

Reference 7

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source=pdf_text observed=2026-08-03T02:38:07.141752Z digest=sha256:90981757282fbb79e516dc3cb100769712faccb503cf6c3ad10e6dd8f62aa8d8

Observation 0fffe2e3-612c-49df-ba4c-d8e2ca2a7132 · outbound

This paper cites Kou, S., Hu, L., He, Z., Deng, Z., and Zhang, H.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Kou, S., Hu, L., He, Z., Deng, Z., and Zhang, H

Reference 8

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no resolver link, observed 2026-08-03T02:38:07.171455Z

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source=pdf_text observed=2026-08-03T02:38:07.171455Z digest=sha256:8a607ebddb8fd4293ccff11b4ac741868e59a2c1001dd21fbba3bdc0dc99d0c0

Observation 665a3857-097b-4d99-a28c-60edd1d6e172 · outbound

This paper cites EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test

Reference 10

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source=pdf_text observed=2026-08-03T02:38:07.303471Z digest=sha256:bc29adb243b87c70f0a3d2d93640588016efb5d28d5c64599a83e1f71e025929

Observation 642cb122-0f95-4d18-bfd3-1eed2d9d1f47 · outbound

This paper cites Large Language Diffusion Models.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Large Language Diffusion Models

Reference 12

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source=pdf_text observed=2026-08-03T02:38:07.376925Z digest=sha256:e1747eda821b97db60be579d0bf1d6add5d4dfde92c952e0a150377dffb82ed7

Observation 30ed7448-ff04-4149-a2ae-a03ceff90ced · outbound

This paper cites Qian, Y .-Y ., Su, J., Hu, L., Zhang, P., Deng, Z., Zhao, P., and Zhang, H.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Qian, Y .-Y ., Su, J., Hu, L., Zhang, P., Deng, Z., Zhao, P., and Zhang, H

Reference 13

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source=pdf_text observed=2026-08-03T02:38:07.404372Z digest=sha256:64e8e57662d8d8dd8f94df9f9e9ce9f6981f0527a0c0c62d3c983526a90c8273

Observation 08312bbe-f7f8-4812-b3fc-6b4b35b2a898 · outbound

This paper cites Qwen2.5 Technical Report.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Qwen2.5 Technical Report

Reference 15

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source=pdf_text observed=2026-08-03T02:38:07.456254Z digest=sha256:155c9c7210992b41f84ee9f95239ffb00e3fd194e88605340f9d9c737e7f0740

Observation 24ca1cbf-1358-462b-8703-20735188147a · outbound

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

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 16

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source=pdf_text observed=2026-08-03T02:38:07.490906Z digest=sha256:85b1fd1b90daf0023ccbf6fab104750fca2f3f78f604ccc86da5e6500bd6b935

Observation 8c9038e8-082d-4646-a1e2-b759ed86e4af · outbound

This paper cites Wang, X., Xu, C., Jin, Y ., Jin, J., Zhang, H., and Deng, Z.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Wang, X., Xu, C., Jin, Y ., Jin, J., Zhang, H., and Deng, Z

Reference 17

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source=pdf_text observed=2026-08-03T02:38:07.545572Z digest=sha256:8cabb2b6d97d90b07c2cd2e3e6c5026c71ecdbd77b602863078b8d53da56f04d

Observation 6c6f10af-c110-41dc-97f7-198d53a0046a · outbound

This paper cites Ye, J., Xie, Z., Zheng, L., Gao, J., Wu, Z., Jiang, X., Li, Z., and Kong, L.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Ye, J., Xie, Z., Zheng, L., Gao, J., Wu, Z., Jiang, X., Li, Z., and Kong, L

Reference 18

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source=pdf_text observed=2026-08-03T02:38:07.603621Z digest=sha256:7dc8db29f222e74bd099a358db49fe76521212b100d127d4fee3d318f3c9cd75

Observation 6cbe0675-721d-4b0f-9d84-b9fc1fab8da3 · outbound

This paper cites d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning

Reference 19

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source=pdf_text observed=2026-08-03T02:38:07.646818Z digest=sha256:0b832deb65e773a8e52ee5a3f38c99a1adf5b84ebf8b76f39baeb977bc2f1752

Observation 3c07cb2b-3121-4236-a156-102f8ee78344 · outbound

This paper cites Zhu, Y ., Wan, J., Liu, X., He, S., Wang, Q., Guo, X., Liang, T., Huang, Z., He, Z., and Qiu, X.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Zhu, Y ., Wan, J., Liu, X., He, S., Wang, Q., Guo, X., Liang, T., Huang, Z., He, Z., and Qiu, X

Reference 20

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source=pdf_text observed=2026-08-03T02:38:07.710297Z digest=sha256:057173e7084df9f0ee273eeeaf38f07592ceda69add565bccd651ef3bccc1b07

Observation 52184a1d-bc60-4a33-92f9-152df3cb409e · outbound

This paper cites 11 LightningRL: Breaking the Accuracy–Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning A.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning 11 LightningRL: Breaking the Accuracy–Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning A

Reference 21

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source=pdf_text observed=2026-08-03T02:38:07.753774Z digest=sha256:aa9c5f44769d6e849acb0c15891a6ecb215a04969e0f1d804a5326bafc73d0ff

Observation c0f34ff7-4f5f-4bb9-9951-755ad1f7b349 · outbound

This paper cites Notably, LightningRL achieves exceptional inference speed, significantly outperforming the baselines.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Notably, LightningRL achieves exceptional inference speed, significantly outperforming the baselines

Reference 22

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source=pdf_text observed=2026-08-03T02:38:07.792710Z digest=sha256:9a9a1abec5c2d25788c0d88f761da840ff81820781dc2d70983ffb39705c407a

Observation 2ad29a45-8315-4a6d-a37a-bd2cbb45667d · outbound

This paper cites dparallel: Learnable parallel decoding for dllms.arXiv preprint arXiv:2509.26488,.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning dparallel: Learnable parallel decoding for dllms.arXiv preprint arXiv:2509.26488,

Reference 2021

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source=pdf_text observed=2026-08-03T02:38:06.784739Z digest=sha256:d04c8964d3295361938e3a0b8957acaea08590302cfd21e4338cb432f0530ba1

Observation e50a3b0c-40b5-4d15-affa-ed18d5e79363 · outbound

This paper cites Diffusion-LM Improves Controllable Text Generation.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Diffusion-LM Improves Controllable Text Generation

Reference 2022

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source=pdf_text observed=2026-08-03T02:38:07.250100Z digest=sha256:39fd7fdc9f220865438a1c0ab9549f5617a2f7e573d89b59a87bf2ec5b186fbe

Observation 9f17ac21-ece7-456e-97e1-c746dce835db · outbound

This paper cites Likelihood-Based Diffusion Language Models.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Likelihood-Based Diffusion Language Models

Reference 2023

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source=pdf_text observed=2026-08-03T02:38:07.094506Z digest=sha256:6065f6e417e6bd022fe5ac8ae63bb51c5ea7f7ed3f766f5bba8e040d72c0339b

Observation a91a15a5-c597-4a4f-b983-e534698bd366 · outbound

This paper cites DiffPO: A causal diffusion model for learning distributions of potential outcomes.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning DiffPO: A causal diffusion model for learning distributions of potential outcomes

Reference 2024

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source=pdf_text observed=2026-08-03T02:38:07.338027Z digest=sha256:8e6c5460d93f344e7ce930c584e3b4c5d68072c644b0d3268fd7e723ccbeed10

Observation b8f02295-77d3-4147-9ac6-0c5cb5d0258f · outbound

This paper cites Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models

Reference 2025

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source=pdf_text observed=2026-08-03T02:38:06.591593Z digest=sha256:4596ca674fbe7ce573a149133c8d0058f69eb8bd8201e3c247f1683eb35bec86

Observation 52f0c2b9-843d-43b9-ab85-7cd6d9d53ed4 · outbound

This paper cites org/abs/2601.07568.

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning org/abs/2601.07568

Reference 2026

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source=pdf_text observed=2026-08-03T02:38:07.430036Z digest=sha256:429f9ceeaf890d32d5a6a8fa56fd05c19e12751cc4284aed8744a8316ffb1570

Pith citing papers

Observation b27d0e8e-9d66-4651-b8a6-a507b627b1c6 · inbound

DMax: Aggressive Parallel Decoding for dLLMs cites this paper.

DMax: Aggressive Parallel Decoding for dLLMs LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning

Reference 33

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arxiv_id, observed 2026-08-03T02:15:22.563508Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-10T17:58:17.880199Z digest=sha256:13230d28a1da989339bdb2883299d79d03cceb4893ea13fca7574732db35e6c8

Observation 54612009-22a3-4466-b8e2-a04bc12cfa8a · inbound

DMax: Aggressive Parallel Decoding for dLLMs cites this paper.

DMax: Aggressive Parallel Decoding for dLLMs LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning

Reference 33

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arxiv_id, observed 2026-08-03T02:15:22.563508Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-19T16:46:56.743268Z digest=sha256:c97f2b94a01551f49357e182e1d05abb14300d3667c823a6a2187e9e5aeca6f3

Observation 18c0fe33-981c-4c7b-92fb-15d85d583c93 · inbound

TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM cites this paper.

TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning

Reference 53

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arxiv_id, observed 2026-08-03T02:15:22.563508Z

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source=pdf_text observed=2026-05-12T05:01:03.570848Z digest=sha256:a742ba3f3611dc215695a89581a3b029a92f8378730d87c7103b0e31cdd9b33f

Observation 4e5c39db-ecb3-4f93-95a0-fd972894b91a · inbound

Multi-Block Diffusion Language Models cites this paper.

Multi-Block Diffusion Language Models LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning

Reference 35

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arxiv_id, observed 2026-08-03T02:15:22.563508Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-30T08:53:31.494892Z digest=sha256:6a8f9939cb7dae5c3b8f88790990ad37a310200feca9a154ae444904d6a49f78

Observation 7397b7b8-4bff-4691-b33f-164251f386e2 · inbound

Multi-Block Diffusion Language Models cites this paper.

Multi-Block Diffusion Language Models LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning

Reference 35

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arxiv_id, observed 2026-08-03T02:15:22.563508Z

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source=arxiv_source observed=2026-07-01T07:04:06.161855Z digest=sha256:0a7c94acf9a124331b1a9918fa69b0eea9fc13174b061bdea238c92fda115fd0

Observation b6828c79-8b48-4cd2-b437-515298ed7d5a · inbound

SLIM-RL: Risk-Budgeted Random-Masking RL for Diffusion LLMs Without Trajectory Slicing cites this paper.

SLIM-RL: Risk-Budgeted Random-Masking RL for Diffusion LLMs Without Trajectory Slicing LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning

Reference 11

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arxiv_id, observed 2026-08-03T02:15:22.563508Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-02T19:05:59.651008Z digest=sha256:3e4c115235fc68812dc7c05f44be00657a75005191728d1c4d1c068c50ad6134