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Paper Citation Record · LEDGER

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem

As of 7 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2506.03295.

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

pith.paper-citation-record.v1
2506.03295 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:13:55.402176Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T05:23:08.478393Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T09:23:37.521970Z

Reference resolution

34 of 34 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f61a3d36-b194-4a19-80e4-5fe8042ad77e · outbound

This paper cites online" 'onlinestring :=.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem online" 'onlinestring :=

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:52.240891Z digest=sha256:d15aa2bedfc28ceabf50e2ec3963f974faba37f1c340398c334112a688fb96bd

Observation 3bddc807-a88e-4a84-8e28-f448c411a530 · outbound

This paper cites write newline.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem write newline

Reference 2

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no resolver link, observed 2026-08-07T11:13:52.323699Z

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source=arxiv_source observed=2026-08-07T11:13:52.323699Z digest=sha256:16a4b69a31f545216b36168c4e6afd4956daef3a77b03ad5b7fb5f6559f3b849

Observation 032176ae-7252-468b-9ff9-fb94657eb39b · outbound

This paper cites Phi-4-reasoning Technical Report.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Phi-4-reasoning Technical Report

Reference 3

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:52.400441Z digest=sha256:a38a4773fefa1eb13e351b07b3b8828cd1359572fe03b21ade9b74dd940eb85a

Observation 64dc34c3-5951-4705-aec2-9f1d5c9b3859 · outbound

This paper cites GPT-4 Technical Report.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem GPT-4 Technical Report

Reference 4

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no resolver link, observed 2026-08-07T11:13:52.506651Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:52.506651Z digest=sha256:c73e01594acc284ff7580349ecd4807998a3a9d89f45015fbce4a5466ea50637

Observation 0ceb413f-2a7b-4c3b-b255-b662ea55ec38 · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 5

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raw_fallback, observed 2026-08-07T11:13:56.812661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:13:52.589772Z digest=sha256:52033b856a249ae3c143625f52c9ed4c858f748b475b9b86a405f357724d95db

Observation f5a9ef3e-62e5-40ab-b996-0d4c84c0436d · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 6

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unresolved
no resolver link, observed 2026-08-07T11:13:52.676931Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:52.676931Z digest=sha256:8705947992b1d4e42f781757852579871d92000f83a391e9cd647013dd3010cd

Observation e8d4d64b-3532-482b-a0a8-9cd360d41574 · outbound

This paper cites SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Reference 7

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source=arxiv_source observed=2026-08-07T11:13:52.791271Z digest=sha256:ec8ec4869e07bda9a46fd16491e00b07b20d03e0b76eafc1bdb6250177c8e481

Observation 2c3c6318-4650-477a-826d-0a6ef184648c · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:13:52.878596Z digest=sha256:b68c7949d747e18a234a90333aee8e7e1b2bb30fbc65fbc3a5c2a01246ed68ee

Observation ad00419f-80f7-4192-9c27-d8b928d75df3 · outbound

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

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:52.980675Z digest=sha256:1571d26d68c9568c7109e57499b99da988a410c4fb25f3b48c5a47a3d39f49bd

Observation e35f8357-73d7-4ba2-a547-0b006bb08109 · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 10

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:53.074826Z digest=sha256:acba5366b02343ceb5b270e5a9491d36b88bf6f440b69f9f8fda2a4384abd9b3

Observation f36f3d9b-9820-40ef-b053-f8c2ce170c51 · outbound

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

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Measuring Mathematical Problem Solving With the MATH Dataset

Reference 11

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:53.146158Z digest=sha256:a0a1a8a9af7f648f75b50885b22d2543ef4b9d3207bc096d02b0e8c61ecfd2e1

Observation 1b47a0b5-9e12-4b7c-92b3-386e12b15de2 · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 12

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no resolver link, observed 2026-08-07T11:13:53.242475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:53.242475Z digest=sha256:f5ef26d06b3144f29836021d13320fd3c4dbd098f9d9de133c9a70853027d522

Observation ef3b0a0a-d011-4cc8-8d46-32c24501447a · outbound

This paper cites Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning

Reference 13

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:53.326869Z digest=sha256:e4709f5f944ede0de62f68c3c0cec95aa1405a52e346dac5fd0f0e0d75bf9c79

Observation 95815a1c-d9b2-4c25-ac55-c5ec47921bb3 · outbound

This paper cites OpenAI o1 System Card.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem OpenAI o1 System Card

Reference 14

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:53.423566Z digest=sha256:bf452013a6f9d4281bc6eb8a68726465c1c06d15a70d7abecee375b17bb773a1

Observation dab77a8c-5779-48ce-9821-740a9524f26c · outbound

This paper cites BIG-Bench Extra Hard.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem BIG-Bench Extra Hard

Reference 15

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:53.511955Z digest=sha256:cadfee7ead55e31c4f7aaab62a719bdfde44a90d9e0a1e111e9495ea7ded2422

Observation 18c74c37-490f-495c-8640-0c7dd3b64025 · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 16

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no resolver link, observed 2026-08-07T11:13:53.610370Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:53.610370Z digest=sha256:59d9355accc3a1ef06f5fecec9a2153ba505bda58c7391824f4a06731ee38862

Observation 0be86bb8-dca2-4b9f-9e6b-4ee186768982 · outbound

This paper cites AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling

Reference 17

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no resolver link, observed 2026-08-07T11:13:53.714891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:53.714891Z digest=sha256:2eb88a9e24a098e999b58eb82ff58158dd4bcb92f5e710392e1eb902c0e5ff87

Observation c458e86f-bf83-4911-8d45-be01a33a7d17 · outbound

This paper cites General-Reasoner: Advancing LLM Reasoning Across All Domains.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem General-Reasoner: Advancing LLM Reasoning Across All Domains

Reference 18

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:53.817051Z digest=sha256:59249f09268f9c3746cd92fb1828d6d9bd77da492b8c16ae0d7d2155137e7684

Observation a8850b35-548f-4380-a5f3-600086c3753c · outbound

This paper cites s1: Simple test-time scaling.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem s1: Simple test-time scaling

Reference 19

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no resolver link, observed 2026-08-07T11:13:53.902869Z

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source=arxiv_source observed=2026-08-07T11:13:53.902869Z digest=sha256:0c5175cc44b35a4d28e5ab70388968afc022d43b4c2b7a703877da4ac5724f72

Observation a2e15b19-515e-4dd6-ad16-7b64c773c0b9 · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:13:54.034196Z digest=sha256:4673a58e31791dbb9c97c6b61bc12de4977fcc190cc23ec696b3b3d817dc29c5

Observation be07fda1-f257-458c-b527-5313acd2bbc2 · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 21

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raw_fallback, observed 2026-08-07T11:13:56.315205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:13:54.134662Z digest=sha256:d4197a043d4bded474ff8fbb8c02bfd94e5e2a497ab381b2d15707b286f404d5

Observation fe1384b7-7f29-4423-85e6-839528e25287 · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 22

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:54.250085Z digest=sha256:7b9a00907bb15112849d117756dd70f03c2b47a4dc1fb36872b72344a5098cfc

Observation 8cb61302-0316-49ef-8f2a-b4ef49a9bdfe · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 23

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raw_fallback, observed 2026-08-07T11:13:56.076603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:13:54.352917Z digest=sha256:2e8ae2a34c0157a6433e26d4db31d73c9a9a061f4e99a811bd96e52f2dd31358

Observation 5ff1a335-898a-4926-b228-1eeacf38255a · outbound

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

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 24

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:54.466759Z digest=sha256:3e23a5c352e856bcd99465e5748dab3e3402ca048b2df6fa16889473ae811046

Observation 7d2fe63a-e74e-4ea6-b315-b09ac42ba3c4 · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 25

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no resolver link, observed 2026-08-07T11:13:54.545213Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:54.545213Z digest=sha256:c6e669c60b5178b241e365dc2ba16c5ee312b923e2dd02352f77fec39bb95f9f

Observation bb53f15c-9bda-41fe-b338-77fba178d538 · outbound

This paper cites Reinforcement Learning for Reasoning in Large Language Models with One Training Example.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:54.643303Z digest=sha256:5bbd3be7dd097d7cacde63d0a27f6c986146eb9aef3163821f010b472dd76063

Observation c47b0fc3-0508-435c-aa00-a904a00345d2 · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 27

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no resolver link, observed 2026-08-07T11:13:54.739185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:54.739185Z digest=sha256:45f6b26992c01ccb7c938d7c94096d9be3f9ea3b31ce919cf661c1c1fa602abb

Observation 3a427ab1-20d6-42e5-875b-457b07433dc2 · outbound

This paper cites Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate

Reference 28

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no resolver link, observed 2026-08-07T11:13:54.819029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:54.819029Z digest=sha256:6a6f0397d48661f945dedd27e0e6f871005d327667ffbdfffd301a22dbf320e3

Observation 8b679c15-fa0b-45ea-9eb4-314ab6cb5bf7 · outbound

This paper cites MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining

Reference 29

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no resolver link, observed 2026-08-07T11:13:54.909053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:54.909053Z digest=sha256:54b7eabb7bbe8fa209cf7a183179cfcbf3561a97bbdd34cd29e1a561c56bce63

Observation 72345245-f1cc-42a6-84b1-174caee69774 · outbound

This paper cites Qwen3 Technical Report.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Qwen3 Technical Report

Reference 30

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no resolver link, observed 2026-08-07T11:13:55.027673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:55.027673Z digest=sha256:6957d98d8d63f10227612286a7b547122400127f7985744beafc09413a49b162

Observation 0a341be0-3ea0-4c17-9ce4-a97b22bc3daa · outbound

This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 31

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no resolver link, observed 2026-08-07T11:13:55.110716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:55.110716Z digest=sha256:2d6d38f5f0e299f1302feaff4014b8e0c8ae31ea5716ee81d52ecfcee1da7981

Observation 7a5f9fc7-85ed-475f-8b00-cb6c7d8884c3 · outbound

This paper cites LIMO: Less is More for Reasoning.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem LIMO: Less is More for Reasoning

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:55.202416Z digest=sha256:4c05c69abfee1529c78f95a5d13a684329db3a88d0416b3e724ca8db9c030a9c

Observation bfc1964e-dd6b-4193-892f-3d6fbb2eff31 · outbound

This paper cites an unresolved cited work.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Unresolved cited work

Reference 33

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raw_fallback, observed 2026-08-07T11:13:55.861725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:13:55.271965Z digest=sha256:6aef5a94410ef9df3a20e6c10209f2b72a893e53d06736b20db2141ce5c63f57

Observation 475ea730-30f7-4685-a1f9-f66e4a87bee5 · outbound

This paper cites SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild.

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:13:55.402176Z digest=sha256:bdaf1e7d3b7abdbf3140891ab1c5782e1aa0416815317e74ebab671fb8aed036

Pith citing papers

Observation 444f8f0e-d11a-4ce5-a8bc-e6279d55bb8a · inbound

HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment cites this paper.

HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem

Reference 35

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verified exact
arxiv_id, observed 2026-05-10T09:23:37.523565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-10T05:23:08.478393Z digest=sha256:db7d5c00f7c42e9b8d9e363ac39860037814c335b074c9b9f8e3ed40375fec75