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Paper Citation Record · LEDGER

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics

As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2608.01522.

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

pith.paper-citation-record.v1
2608.01522 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:08:27.089569Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

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

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

  • verified exact9
  • verified fuzzy1
  • unresolved31
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

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Outbound references

Observation 5d36def0-842b-44e7-89fa-021b73d2c339 · outbound

This paper cites an unresolved cited work.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Unresolved cited work

Reference 1

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T00:08:22.205235Z digest=sha256:7899f348cf2faea3393d2ce248d2d2b89f81b23de0ef22e8ce422127d33f7fc6

Observation afe4e132-1f59-4f87-9e9f-f59c16195e15 · outbound

This paper cites an unresolved cited work.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Unresolved cited work

Reference 2

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source=arxiv_source observed=2026-08-06T00:08:22.301258Z digest=sha256:6a329f06415fea7e2c48ee08ff4defc2532241280f5e698de2881835481fa953

Observation 1e7ee572-2531-4076-b210-b57198a62915 · outbound

This paper cites Y.; Kim, H.; Nam, J.; and Kwak, D.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Y.; Kim, H.; Nam, J.; and Kwak, D

Reference 3

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source=arxiv_source observed=2026-08-06T00:08:22.378877Z digest=sha256:1e2fdbeb566d3aa9ba2f6ffe4dd806934f66b22abbb483b6408a88d775473587

Observation 98747308-700e-46db-9866-9d4b7a981365 · outbound

This paper cites Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model

Reference 4

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source=arxiv_source observed=2026-08-06T00:08:22.453766Z digest=sha256:7566b52e062aef479e33831588fc5a054874293473ff5006d04e540692274aca

Observation c41ccfc0-7b53-4ac3-a3fe-e2c36127ebb5 · outbound

This paper cites C.-Y.; Peng, B.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics C.-Y.; Peng, B

Reference 5

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source=arxiv_source observed=2026-08-06T00:08:22.565249Z digest=sha256:a1c867c77f9bd3a2ae3f3eda75cc1a85d0f1d88776c344a1273e313c406acaa7

Observation dc4eca74-4853-4e5e-8f89-a58db3cf9f7f · outbound

This paper cites Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems

Reference 6

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local_arxiv, observed 2026-08-06T00:08:29.917633Z

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source=arxiv_source observed=2026-08-06T00:08:22.647848Z digest=sha256:971e7332f887b7bfd4dc5ba26e916310e5f0f4bde6c84256ac8a67a36768757a

Observation 1fa5ee7b-aefd-43c2-89c4-52b3cfbf1c16 · outbound

This paper cites an unresolved cited work.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Unresolved cited work

Reference 7

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source=arxiv_source observed=2026-08-06T00:08:22.747287Z digest=sha256:4ac6b762376d1ad0f71f925cb215d88010e193527b417509a048759fae6a79cd

Observation 160a72b3-4192-4973-ae14-c95c8f7e1222 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Training Verifiers to Solve Math Word Problems

Reference 8

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source=arxiv_source observed=2026-08-06T00:08:22.802880Z digest=sha256:955421bc5b7743bba29d923c3cd3213211afc808c3f86965f962311dcec883de

Observation 55f961c5-37da-4973-99c5-03b2d44e0b40 · outbound

This paper cites The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

Reference 9

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source=arxiv_source observed=2026-08-06T00:08:22.965289Z digest=sha256:9d0e38c738f84f647265a979c809cb5ee0cd6b0ec676d839ff6160705172023b

Observation 1502b380-af18-4e3d-9000-b7db58bad258 · outbound

This paper cites Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models

Reference 10

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source=arxiv_source observed=2026-08-06T00:08:23.124533Z digest=sha256:b97708e3cc374c70213ce88c954e269ec494eb9746deb3a4c46a09397dd34441

Observation 6a4e408d-a9d4-41b7-b629-1601260c0875 · outbound

This paper cites FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI

Reference 11

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source=arxiv_source observed=2026-08-06T00:08:23.220982Z digest=sha256:08de6001fd4ca2595f208b8d0c4ebbd4dd4b329fba9a76d3630348a7e351b63b

Observation 082e6d06-398f-4600-a127-18d04123a176 · outbound

This paper cites LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models

Reference 12

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source=arxiv_source observed=2026-08-06T00:08:23.331668Z digest=sha256:ccff411277061eda0ab3d12bc68b0c7755808561eb0298887bffb191497cbc8b

Observation a3818433-6bd3-4e9d-aff3-867595dfc4a3 · outbound

This paper cites Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction

Reference 13

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T00:08:23.437074Z digest=sha256:dc31394156993de3054d04fc9bba8743be02bdace476919876a5a437f7464677

Observation 7049221d-c652-4120-a28e-84b764880ac5 · outbound

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

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Measuring Mathematical Problem Solving With the MATH Dataset

Reference 14

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source=arxiv_source observed=2026-08-06T00:08:23.557233Z digest=sha256:7ff7afa80aa61a397ae02bd6e0c91a1327eaf4c2ab11acc9f7c86c2852c0cca3

Observation 3a7b2a6b-419b-48d8-aa18-77e82cc17200 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics LoRA: Low-Rank Adaptation of Large Language Models

Reference 15

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source=arxiv_source observed=2026-08-06T00:08:23.659512Z digest=sha256:5d76ac73c05a27317004ea1869603900a52d2649c05302e7cfe356da0299e1a0

Observation 05fe3a9a-a991-4487-a9e3-ad615971040d · outbound

This paper cites R-Zero: Self-Evolving Reasoning LLM from Zero Data.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics R-Zero: Self-Evolving Reasoning LLM from Zero Data

Reference 16

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source=arxiv_source observed=2026-08-06T00:08:23.821560Z digest=sha256:c543597ef2a978fceed2470195900651b7c2c0e4d3608eea11a79d171aa41caa

Observation 1e6e7eef-9e77-48a0-95ed-d94b47422642 · outbound

This paper cites On the Emergence of Implicit Curriculum in RLVR Learning Dynamics.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics On the Emergence of Implicit Curriculum in RLVR Learning Dynamics

Reference 17

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source=arxiv_source observed=2026-08-06T00:08:23.894377Z digest=sha256:cdc95e1f04eb4ba3b13d9cbcce44e635298380f4f951cc34b6a42cc3e2f544ae

Observation f797a26b-738a-413f-8c77-b79072c75b0c · outbound

This paper cites an unresolved cited work.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Unresolved cited work

Reference 18

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source=arxiv_source observed=2026-08-06T00:08:24.092576Z digest=sha256:c42dbb71012734a480a92ff0f02cf2172e591f9b863656660f06c253c7aa167c

Observation a7e01ae2-2f1f-4c8f-8d6c-d0031bf62ec9 · outbound

This paper cites ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities

Reference 19

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source=arxiv_source observed=2026-08-06T00:08:24.264070Z digest=sha256:59cbc2176303276f40afc88d09063433548bccb3c373f9bbd728b0ad0dc41401

Observation 6c4f66b6-5fac-44b2-a9b5-52f9c7f926fc · outbound

This paper cites Executable Functional Abstractions: Inferring Generative Programs for Advanced Math Problems.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Executable Functional Abstractions: Inferring Generative Programs for Advanced Math Problems

Reference 20

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source=arxiv_source observed=2026-08-06T00:08:24.393336Z digest=sha256:20850cf32d0beb568a5ec4acdc1d2a0c4b0ea73c20c69720880e5539a6e665a3

Observation 77a1f805-2869-48e1-83ad-23c326f45a3a · outbound

This paper cites Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients

Reference 21

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T00:08:24.529583Z digest=sha256:4aeeef4fe9b32c81868110c58a920f6a61af54aaa5972753bac19c83a7ef7121

Observation 59f5f96d-954b-43de-9c55-70257781ffc8 · outbound

This paper cites N.; Guo, Z.; and Chen, W.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics N.; Guo, Z.; and Chen, W

Reference 22

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source=arxiv_source observed=2026-08-06T00:08:24.590955Z digest=sha256:799328b693ac6fdef2e8bcfdb0a5139ecaec5198ab5b59d2b4bf6732b475258e

Observation 320d73ca-b445-4c57-8b2c-a322c9e2aee3 · outbound

This paper cites SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning

Reference 23

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source=arxiv_source observed=2026-08-06T00:08:24.739824Z digest=sha256:65515a1fa6565e51cfa43cab4d6989bfcfb2435cb7a50a2b927c637bfadd21b2

Observation 18e1d785-71a1-4d60-b579-091137d6ce39 · outbound

This paper cites Let's Verify Step by Step.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Let's Verify Step by Step

Reference 24

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source=arxiv_source observed=2026-08-06T00:08:24.826627Z digest=sha256:f6267c02a65d2d93f4bdfed071dc4c54ea14713cd0acad06c44f6b4a28d8aba2

Observation 7145b4ce-6d8a-451c-8d7e-236ebea1f53f · outbound

This paper cites Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training

Reference 25

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source=arxiv_source observed=2026-08-06T00:08:24.971238Z digest=sha256:8e17516019f14fe946e3de30f49abbd788cfd7e923467a5b8eb1387f0a201064

Observation e9d067c6-2a3f-4cfe-8a25-8dd9f3e40c9c · outbound

This paper cites Augmenting Math Word Problems via Iterative Question Composing.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Augmenting Math Word Problems via Iterative Question Composing

Reference 26

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source=arxiv_source observed=2026-08-06T00:08:25.129155Z digest=sha256:eca8e4afa706dcf942d5c990ede5ae7ae0bfacfade6224d6320d97fe2c6196b4

Observation 4dcbe759-1785-4b01-93f2-7865b7afdc9f · outbound

This paper cites ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 27

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source=arxiv_source observed=2026-08-06T00:08:25.365379Z digest=sha256:009e2dbd47421d7e0c9a9e45ea70dd09c07e9bcf9eb6c43c5bc16c372e7393e4

Observation fdb61e68-0c05-42de-8a15-7b65e0b3a022 · outbound

This paper cites Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning

Reference 28

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source=arxiv_source observed=2026-08-06T00:08:25.571429Z digest=sha256:94b99cb6ea8a288aa206f71e06dafbd4ddd46c37111da0e9d3fcac19b992c2f7

Observation 166b9f55-4cde-4a6a-8ee7-af3499ee993d · outbound

This paper cites Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning

Reference 29

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source=arxiv_source observed=2026-08-06T00:08:25.757170Z digest=sha256:fa21edd8a6b2e6a4cf8948ee3ac23287b3f92cf295d9ff94c6e6cf3462e6efb8

Observation bf24a7a1-9fe9-4d55-b960-91b809835627 · outbound

This paper cites Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad

Reference 30

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source=arxiv_source observed=2026-08-06T00:08:25.854976Z digest=sha256:47427c96b79132233a875871cbd3cabec1785a84d4204829c39a0639319c156d

Observation 09c6067f-041f-45f9-a358-ea66f29aade2 · outbound

This paper cites an unresolved cited work.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Unresolved cited work

Reference 31

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source=arxiv_source observed=2026-08-06T00:08:25.958549Z digest=sha256:11170ffc9f3e35c32ddced255ae525ce68b927f3d5bebbfb4154a39bb654d4e0

Observation ec615c0f-6570-4048-a053-5c49094d6aee · outbound

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

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 32

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source=arxiv_source observed=2026-08-06T00:08:26.039213Z digest=sha256:bf2ddc7cc50be2a818efb11bce42a476b3d78e63a426b29e06ce60c08f5840f9

Observation bbfb2bf7-b513-4dd5-95d9-9632d261dd80 · outbound

This paper cites Efficient Reinforcement Finetuning via Adaptive Curriculum Learning.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Efficient Reinforcement Finetuning via Adaptive Curriculum Learning

Reference 33

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source=arxiv_source observed=2026-08-06T00:08:26.113912Z digest=sha256:0fd604df6bbdc74d1d53bcf7f83be806d3e4ce97eff3bdfbd807325110b35b09

Observation 3b8e3f2b-e8fc-4857-9463-42f308cc2a1e · outbound

This paper cites an unresolved cited work.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Unresolved cited work

Reference 34

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T00:08:26.228746Z digest=sha256:a4edd7408ee995092baa85ec6dda83b24d9c64361fb15f44acda6957b7edd475

Observation a97687d4-99e0-4e60-99e2-6eb33bf6ffa1 · outbound

This paper cites A.; Di Giacomo, D.; Bond \'a r, I.; Engdahl, E.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics A.; Di Giacomo, D.; Bond \'a r, I.; Engdahl, E

Reference 35

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raw_fallback, observed 2026-08-06T00:08:30.549639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T00:08:26.317291Z digest=sha256:18cd5aa8224174308d49be80198e9659d6e74b1520b437118497c4e74d2f208f

Observation 7ce4ed75-9699-482f-b0e8-cd77a78d8fa4 · outbound

This paper cites Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T00:08:26.436283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:08:26.436283Z digest=sha256:b2f91f2915133b297ac56bfe33e9b96b5caebe0ada6b9626b491ba3667a0ef29

Observation c73a7ed3-e9d1-4db3-996e-c6cf06bdff0d · outbound

This paper cites PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T00:08:26.563300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:08:26.563300Z digest=sha256:e91fdb2936ed2ab617df9f8741feef53925c14905d2c9d12d401a068057411d0

Observation 869d2bb8-4850-4f5e-96ae-a51523cbb232 · outbound

This paper cites CT Open: An Open-Access, Uncontaminated, Live Platform for the Open Challenge of Clinical Trial Outcome Prediction.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics CT Open: An Open-Access, Uncontaminated, Live Platform for the Open Challenge of Clinical Trial Outcome Prediction

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T00:08:26.676385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:08:26.676385Z digest=sha256:937d085c4cbf095b728f6b4f384a35e1ea3d360691cbdd02a454375f90faec34

Observation ad48a7e9-6041-45fb-ae39-a29a3bab8f2a · outbound

This paper cites MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T00:08:26.742198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:08:26.742198Z digest=sha256:04b11149025302951ae7af066b82b28521faa4b77fe61e06a62fe2847fd3e7d3

Observation 28c8611c-8363-424b-b700-09269d6beb1b · outbound

This paper cites an unresolved cited work.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Unresolved cited work

Reference 40

Resolution
verified exact
raw_fallback, observed 2026-08-06T00:08:27.704155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T00:08:26.833683Z digest=sha256:12a575a4a7933553b82cee193dea1226561445adcdd143cc256c995c09383e51

Observation d1ab7a5a-a1da-479a-92d6-25e1ca92c2bb · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-06T00:08:26.939816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:08:26.939816Z digest=sha256:a80af2e67805ab61ecab45a110b72ea09a6bea9b4d83555fbafca06d0e05b94f

Observation 4ad2b193-5d0c-4c5f-87ab-d942a94c7a89 · outbound

This paper cites D$^2$Evo: Dual Difficulty-Aware Self-Evolution for Data-Efficient Reinforcement Learning.

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics D$^2$Evo: Dual Difficulty-Aware Self-Evolution for Data-Efficient Reinforcement Learning

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-06T00:08:27.354804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T00:08:27.089569Z digest=sha256:443d2d23dcda4b68d0dc5e7bda13d66fef592210fdf984d5b126a2f46bdf39a0

Pith citing papers

No inbound Pith citation observations are available.