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Paper Citation Record · LEDGER

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes

As of 13 July 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2605.28421.

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

pith.paper-citation-record.v1
2605.28421 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T11:51:01.769882Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

42 of 42 outbound references displayed

  • verified exact27
  • verified fuzzy0
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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No source-named external measurement is stored.

Outbound references

Observation 37e14206-a075-475f-ae9a-460573103097 · outbound

This paper cites Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

Reference 1

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local_arxiv, observed 2026-06-29T11:53:23.681551Z

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:5ee610961d6494fe9dca0cf62db6e484ff50cd5cbe9d15cb4cf306415b6026d2

Observation bec034d0-20ff-47f8-a0c3-62ebb9ab9c50 · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

Reference 2

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:e7939cadcff501d7738cfb8551cb1309286434c22832452db1f83cfbe909e4d0

Observation f6b7c844-a0e7-4c8f-95e7-faa5ce9ee977 · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

Reference 3

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:b37885e96c3ba838b5bdd91378f1c574a4df98af3541e6db77ee6d41e7f65c0e

Observation a0e04240-5782-4147-90e3-08a2d48c1c32 · outbound

This paper cites Burns, P.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Burns, P

Reference 4

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arxiv_id, observed 2026-06-29T11:53:23.662416Z

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:80bf93541d6f3a98e357bd095bbabd4a8226d323b6f10e8c5f1ab9a3cf58f1f9

Observation d092d25e-94da-4b7a-bcdd-e913d5d9d672 · outbound

This paper cites Training-free group relative policy optimization, October 2025.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Training-free group relative policy optimization, October 2025

Reference 5

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arxiv_id, observed 2026-06-29T11:53:23.684505Z

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:73c40d51ddb260412a52a45456f2448b24d320ba7601e626a9f096fb3f1d3fed

Observation 617b1317-457d-444b-afd8-1775a422db2b · outbound

This paper cites Do LLMs signal when they’re right? evidence from neuron agreement, 2025.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Do LLMs signal when they’re right? evidence from neuron agreement, 2025

Reference 6

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arxiv_id, observed 2026-06-29T11:53:23.669549Z

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:a5341e2248bd1bc5d9a727bb09d55e46c157760c221cbdfaf763c96ae75409cb

Observation c3af9b88-12f8-4561-a053-5c6a4dbb51d7 · outbound

This paper cites Weak-to-Strong Generalization is Nearly Inevitable (in Linear Models).

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Weak-to-Strong Generalization is Nearly Inevitable (in Linear Models)

Reference 7

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:3419e82ea93cc873e6b1438cdf04db96852ee83d7d12891efc9f7a24cf8de0b6

Observation ee8bc249-2756-4881-9ac8-131095b16c32 · outbound

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

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 8

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:316f5493276d2eff94aee2859665dac680d81cc2c7e1e55c4676bc50553c178a

Observation 9bee5b38-0f96-4678-a3e3-9faac621eb5c · outbound

This paper cites DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

Reference 9

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:53e3b574765cf6bbe69350ac36a9262c62212cd36055f8c9401c4a02a93e152e

Observation 2a5b6baa-ef87-4718-b6af-dd058bf0a6b3 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Measuring Mathematical Problem Solving With the MATH Dataset

Reference 10

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:c71b334f891798849295cb4a61b34857ad19b846ed75a01f2c63177b0e3fc0f8

Observation 02abb5a9-9cce-43d8-9681-20c7560d5002 · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

Reference 11

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:7a26dfe762938c63bb3d6b5a976103264eec493ac3793799d401501655d6f4fc

Observation 4ee058bb-acd1-484c-a80e-5b5a31989c8c · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

Reference 12

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:e791695642ffeb0ab30404f83fc262293a7d4d674be05a3c53e5576178b7ab5e

Observation 1993e84a-6c8b-4fca-9cc6-f0781a897f26 · outbound

This paper cites Process reward models that think.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Process reward models that think

Reference 13

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:1551d882b607e6f097afcf28e3bdd7b64051ac194407f4c41e61339adfe14c85

Observation d0ec393c-dffe-4757-af64-54b9d339b4d5 · outbound

This paper cites Training-free Uncertainty Guidance for Complex Visual Tasks with MLLMs.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Training-free Uncertainty Guidance for Complex Visual Tasks with MLLMs

Reference 14

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:7a36f9b1c54321e63bdeb3c8f9df9d61534847b0206ce7dff8a45157957a8af2

Observation 2d59e6bc-6b99-4350-8fdc-a5e4edce4d91 · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:9dcbe02dbd270d2ed7273487ad4ede02cb086823ce83a5f24328a5d1e2e3fd3b

Observation 9149339f-63a5-4e9d-92b1-e79fcab7d694 · outbound

This paper cites arXiv preprint arXiv:2601.22718 , year=.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes arXiv preprint arXiv:2601.22718 , year=

Reference 16

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:9f60314ddf7e27e8ccfe16b6d5e37d2116c1809ec4aec540859f63cf10dd848d

Observation fcdeebe8-0569-4bde-89ea-8cb96c50e3ea · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:cada8a6f424596547dc6ba1baff90d425fee06d5fa6b916b793f30e85d2e0e4b

Observation cacb1f9c-d9b2-4846-8426-53f881f94825 · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

Reference 18

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:2539ddee856e7375690d6b36a21d81a933e721576e139c6efa6b1deffa6730cc

Observation c682f812-c459-439d-8c41-b594f9ddd14a · outbound

This paper cites EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning

Reference 19

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local_arxiv, observed 2026-06-29T11:53:23.698614Z

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:eb8b82c61b6b230535f5a8eb67fda8adb75fa4822e006261ca5a84cdf23e2482

Observation 09397fe9-541f-4b71-a434-e42ba757d11a · outbound

This paper cites Synlogic: Synthesizing verifiable reasoning data at scale for learning logical reasoning and beyond.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Synlogic: Synthesizing verifiable reasoning data at scale for learning logical reasoning and beyond

Reference 20

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:4f915d1206b2be85d52e4140b1ea06f81bb1423c084e4f16697ff03bfe05547d

Observation 802b7182-ffa2-446d-8122-166375d722d5 · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:a00faaa8e6d7a86430301ef8518e18a100072ced73968476d516ebe9ad1b4c13

Observation c0dd154d-c180-490e-8dfa-1389a00e51c1 · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

Reference 22

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:f8b8d922b50ad26573f71c725455354d2f9f58d6c2e762aed79d754745aa25b9

Observation 7c49f55d-bd89-413c-8cdb-09ad2e4a4af6 · outbound

This paper cites Pope: Learning to reason on hard problems via privileged on-policy exploration.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Pope: Learning to reason on hard problems via privileged on-policy exploration

Reference 23

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:45e8f84a5d7c34f38bcedf5007c0c2818930a7a646d76447e1ccd315a30f5078

Observation d918def2-29c6-4970-9c8b-d88b4fe8c921 · outbound

This paper cites Proximal Policy Optimization Algorithms.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Proximal Policy Optimization Algorithms

Reference 24

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Observation fb0250b3-8437-417d-ac4a-7d68a45c6eef · outbound

This paper cites Reuse your flops: Scaling rl on hard problems by conditioning on very off-policy prefixes.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Reuse your flops: Scaling rl on hard problems by conditioning on very off-policy prefixes

Reference 26

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arxiv_id, observed 2026-06-29T11:53:23.689537Z

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:dd49c1421079345a2d6f9c9d741ce3922745898b9f4ced8bb2ebe5992918f790

Observation 0bab13af-6ac6-42bc-af36-8a382921f3b6 · outbound

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

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 27

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:7a625a32450fc9387da2834f7fae63e3cc9668e56f94544ec46c1c35c2b96c18

Observation 049b37ea-e7c2-45e3-be6c-5938937028bb · outbound

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DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

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Observation cdc7c0b7-22be-446d-bcd0-3d83e69cbc0d · outbound

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DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:e77aeb314e96fedf93011a542701ece8a7fa1bfa605ac55dbea945a74a76d7d0

Observation a1de8982-be7b-40bf-8bce-f93b70684c22 · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

Reference 30

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:ac8c6406b05c94af040611402eef54a2cc91fb9f3ae42881d732f4d471c434ff

Observation 60c4c9ce-e9fa-40da-a4af-6a89c7a9703a · outbound

This paper cites Qwen3 Technical Report.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Qwen3 Technical Report

Reference 31

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local_arxiv, observed 2026-06-29T11:53:23.672403Z

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:3e9d8f5862d35553e6eb9ee9783f97fd0c9ba7898ede5b2ab9bd4772b68a2334

Observation f46631b7-1aec-4f9d-aaec-10b5d9e639b6 · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:fe7518b17c631f1b19664b3c95f62b2bd92b627f0355fcfb8e44b2dc5206db86

Observation 7d992c4d-6284-4b5b-b08d-3b4105e8a474 · outbound

This paper cites Generating Sequences by Learning to Self-Correct.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Generating Sequences by Learning to Self-Correct

Reference 33

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arxiv_id, observed 2026-06-29T11:53:23.676256Z

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:1517c8359a3de488cbe9ace96b4f9960c242506df37789d5d29b712ceb436525

Observation 64639df8-7838-4fb5-8962-e9ea3bb461ae · outbound

This paper cites arXiv preprint arXiv:2506.02864 , year=.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes arXiv preprint arXiv:2506.02864 , year=

Reference 34

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arxiv_id, observed 2026-06-29T11:53:23.678846Z

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:8714bdadd1aa6e2d98ea8739a620fc5e0215f513c16d63879185535c3278cd84

Observation dd996933-3890-41ae-9507-12d77df4d16f · outbound

This paper cites SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning

Reference 35

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:571b7ab05b94e6d5e59d13ac4d7b91bcd536ccc59a5e914b86e077bbd446155e

Observation 0d33a8be-bd11-4930-a06d-c1b7dc0a01b1 · outbound

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DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

Reference 36

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:fc4bc0087ac6502b3b5ece6436c8d5082fdcfff8722d3e2d31727932423f52bf

Observation 34a259c7-bc2a-4585-8edb-a0efa7bb0702 · outbound

This paper cites Qwen2 Technical Report.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Qwen2 Technical Report

Reference 37

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local_arxiv, observed 2026-06-29T11:53:23.676192Z

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:7a3688e5e5ba10d7462f34a04bdc01e7f688c5b711cb34e2fab125a1ce4e145a

Observation b351e235-5754-48d3-b6f4-adda2fbbee5a · outbound

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DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

Reference 38

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source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:cec1f1446acef4816b640a7d2103baec4f80ad04ad6c75498d0049e9510ed8b9

Observation e0abe62f-3451-4811-b91e-5eb208bee888 · outbound

This paper cites KnowRL: Boosting LLM Reasoning via Reinforcement Learning with Minimal-Sufficient Knowledge Guidance.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes KnowRL: Boosting LLM Reasoning via Reinforcement Learning with Minimal-Sufficient Knowledge Guidance

Reference 39

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Source-reported events for the cited work

Unavailable: named source frontier unavailable.

source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:44a8fcc824ab4ece0336c33e6d505aa55a4d3b057e823ea1f19152d143db064b

Observation e14d4db1-9649-4d22-a45d-e35d04018528 · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

Reference 40

Resolution
unresolved
no resolver link, observed 2026-06-29T11:51:01.769882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:48d5976ef707088add4207770a60f683cbf0ddec9abd16f472f503c6a4ccadf9

Observation 5faf7014-cab4-4e1e-8d0c-92bbb22d437f · outbound

This paper cites an unresolved cited work.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Unresolved cited work

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-06-29T11:53:23.669676Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:95d46d5cc8b2315ff3e2313adf01704d7ab76185df38f2f29ead84f63019fb3c

Observation 570f1cdf-1701-4847-b222-8dd6d85b8c23 · outbound

This paper cites Mathsmith: Towards extremely hard mathematical reasoning by forging synthetic problems with a reinforced policy.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Mathsmith: Towards extremely hard mathematical reasoning by forging synthetic problems with a reinforced policy

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-06-29T11:53:23.679110Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:20b4144ff50c1d9e06f53903c0e33d9aba56b8d46645f15e26512b67cc36c5a1

Observation a06f4f99-00cc-44dc-a733-1b7c78396f7c · outbound

This paper cites Reinforcement-aware Knowledge Distillation for LLM Reasoning.

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes Reinforcement-aware Knowledge Distillation for LLM Reasoning

Reference 43

Resolution
verified exact
local_arxiv, observed 2026-06-29T11:53:23.660779Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

source=pdf_text observed=2026-06-29T11:51:01.769882Z digest=sha256:8d99675bc385ad5262189ab8fb698b6efc1b300c8a7e7fbbf45cfebeae2dafc9

Pith citing papers

No inbound Pith citation observations are available.