Paper Citation Record · LEDGER

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model

As of 6 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2606.22317.

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

pith.paper-citation-record.v1
2606.22317 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T11:10:56.755558Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:08:22.453766Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T00:08:30.124848Z

Reference resolution

49 of 49 outbound references displayed

verified exact20
verified fuzzy0
unresolved27
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e539e23a-43b7-4966-b543-bbe156f47c81 · outbound

This paper cites Rl for reasoning by adaptively revealing rationales.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Rl for reasoning by adaptively revealing rationales

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

Source-reported events for the cited work

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Observation 787e5272-d8f9-4bc4-b96c-1ffedd6855f8 · outbound

This paper cites Online difficulty filtering for reasoning oriented reinforcement learning.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Online difficulty filtering for reasoning oriented reinforcement learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

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Observation 4a135eb6-9869-4771-b9e7-e43eefdb1b66 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Evaluating Large Language Models Trained on Code

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.281464Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 53c92407-fa65-4bcd-a92d-6d8c29db6b0e · outbound

This paper cites Unveiling the key factors for distilling chain-of- thought reasoning.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Unveiling the key factors for distilling chain-of- thought reasoning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

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Observation b8e2a08b-f532-431c-846a-ddc673baa1ad · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Training Verifiers to Solve Math Word Problems

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.312906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 64a05973-4fc6-49f6-af85-097bb1ea1560 · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.306067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3151905a-5e48-41e1-b11a-8801af193a51 · outbound

This paper cites Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.Nature, 645(8081):633–638, 2025.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.Nature, 645(8081):633–638, 2025

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

Source-reported events for the cited work

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Observation ad351469-98a8-4e59-8bd9-405ba2ef7126 · outbound

This paper cites Omni-math: A universal olympiad level mathematic benchmark for large language models.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Omni-math: A universal olympiad level mathematic benchmark for large language models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

Source-reported events for the cited work

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Observation f8d10b29-0fe4-4ea3-b166-67a56fbff509 · outbound

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

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.300162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation efe3cd24-14c7-4002-9329-7febe15112df · outbound

This paper cites Rewarding the unlikely: Lifting grpo beyond distribution sharpening.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Rewarding the unlikely: Lifting grpo beyond distribution sharpening

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

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Observation d75c0abc-3b57-43a6-87f4-ba96f7b181e6 · outbound

This paper cites Measuring mathematical problem solving with the math dataset.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Measuring mathematical problem solving with the math dataset

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

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Observation fe5532e9-d0c2-4fd2-ae39-4ada51a27e06 · outbound

This paper cites Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

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Observation fb9e5769-20cf-4bb0-9e43-29e2eb5fca8d · outbound

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

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model R-Zero: Self-Evolving Reasoning LLM from Zero Data

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.309663Z

Source-reported events for the cited work

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Observation d3229f20-a436-4298-9568-fa00c3fededc · outbound

This paper cites Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:39:42.303465Z

Source-reported events for the cited work

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Observation 323258c1-76b1-450c-9c1d-2d951894027c · outbound

This paper cites Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:39:42.251664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 891db1dc-2849-41a6-beab-792e3241c235 · outbound

This paper cites Unlocking the power of function vectors for characterizing and mitigating catastrophic forgetting in continual instruction tuning.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Unlocking the power of function vectors for characterizing and mitigating catastrophic forgetting in continual instruction tuning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

Source-reported events for the cited work

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Observation 17048d55-71be-4e19-acdb-9a311510ee34 · outbound

This paper cites Tacler: Tailored curriculum reinforcement learning for efficient reasoning.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Tacler: Tailored curriculum reinforcement learning for efficient reasoning

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:39:42.282278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 17451c98-d139-4776-a6b5-bc6c2dd4e791 · outbound

This paper cites Language models can easily learn to reason from demonstrations.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Language models can easily learn to reason from demonstrations

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

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Observation decc794b-e1f0-437d-9233-7f06515cb487 · outbound

This paper cites Prorl: Prolonged reinforcement learning expands reasoning boundaries in large language models.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Prorl: Prolonged reinforcement learning expands reasoning boundaries in large language models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

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Observation f8ad80ca-2fa3-494f-8906-9fb1e7df4371 · outbound

This paper cites Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

Source-reported events for the cited work

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Observation 7482cacf-f7f2-4d09-a49e-852c9a44988d · outbound

This paper cites An empirical study of catastrophic forgetting in large language models during continual fine-tuning.IEEE Transactions on Audio, Speech and Language Processing, 33:3776–3786, 2025.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model An empirical study of catastrophic forgetting in large language models during continual fine-tuning.IEEE Transactions on Audio, Speech and Language Processing, 33:3776–3786, 2025

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

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Observation edaefd69-d03e-481a-8969-5ab596b96f69 · outbound

This paper cites s1: Simple test-time scaling.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model s1: Simple test-time scaling

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.302931Z

Source-reported events for the cited work

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Observation 723e8753-b778-4129-922a-d40edde2e06e · outbound

This paper cites Openai o1 system card, 2024.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Openai o1 system card, 2024

Reference 23

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Source-reported events for the cited work

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Observation 016d0c7a-9751-40a5-8080-0e77cc613d27 · outbound

This paper cites Curriculum reinforcement learning from easy to hard tasks improves llm reasoning.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Curriculum reinforcement learning from easy to hard tasks improves llm reasoning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

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Observation 8362972f-329f-4e83-bec4-80781be32119 · outbound

This paper cites Seed1.5-thinking: Advancing superb reasoning models with reinforce- ment learning.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Seed1.5-thinking: Advancing superb reasoning models with reinforce- ment learning

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:39:42.305959Z

Source-reported events for the cited work

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Observation b8c90504-fcc3-44f6-8b25-198bfd340ed9 · outbound

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

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.296432Z

Source-reported events for the cited work

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Observation 7ef419e3-2fbe-40db-8337-6893563729c0 · outbound

This paper cites Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:39:42.245665Z

Source-reported events for the cited work

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Observation ab311a9c-566d-43ae-b144-be5d30a34c1d · outbound

This paper cites Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.235813Z

Source-reported events for the cited work

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Observation a0133171-3f07-48a0-9401-bcdd1fa39746 · outbound

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

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.240336Z

Source-reported events for the cited work

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Observation 8c149a40-a299-411c-8ba8-5a3bf370ad9c · outbound

This paper cites Continual gradient low-rank projec- tion fine-tuning for llms.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Continual gradient low-rank projec- tion fine-tuning for llms

Reference 30

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Observation 06b8fae7-b780-4c1f-8dbe-e5be94315c3f · outbound

This paper cites Inscl: A data-efficient continual learning paradigm for fine-tuning large language models with instructions.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Inscl: A data-efficient continual learning paradigm for fine-tuning large language models with instructions

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

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Observation d7643db9-d66f-4523-901c-3a4510bdcdbb · outbound

This paper cites Reasoning scaffolding: Distilling the flow of thought from llms.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Reasoning scaffolding: Distilling the flow of thought from llms

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

Source-reported events for the cited work

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Observation f5286da5-28a9-4042-aef8-60ae367d41e6 · outbound

This paper cites Reinforcement learning with verifiable rewards im- plicitly incentivizes correct reasoning in base llms.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Reinforcement learning with verifiable rewards im- plicitly incentivizes correct reasoning in base llms

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

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Observation b972c2da-bbc1-4150-bcd6-8612b0d312db · outbound

This paper cites Enhancing long-chain reasoning distillation through error- aware self-reflection.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Enhancing long-chain reasoning distillation through error- aware self-reflection

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:39:42.294055Z

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Observation c5d14caa-9f01-4289-b406-a09d72b93085 · outbound

This paper cites Training large language models for reasoning through reverse curriculum reinforcement learning.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Training large language models for reasoning through reverse curriculum reinforcement learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

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Observation 839f41f2-2611-43a2-8774-b6641b528f98 · outbound

This paper cites Learning to Reason under Off-Policy Guidance.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Learning to Reason under Off-Policy Guidance

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.270996Z

Source-reported events for the cited work

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Observation fbefa8f7-7a04-451b-93e7-c40665df5795 · outbound

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

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.246400Z

Source-reported events for the cited work

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Observation 718c7f29-425f-4845-8d62-542048da84d5 · outbound

This paper cites Qwen3 Technical Report.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Qwen3 Technical Report

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.291866Z

Source-reported events for the cited work

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Observation 95a29bb8-2dc5-4a77-bfbf-602c4f97dede · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:42.287324Z

Source-reported events for the cited work

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Observation a70a5b3d-ba88-4aea-8d5f-8f72a9783c5d · outbound

This paper cites Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model? InThe Thirty-Ninth Annual Conference on Neural Information Processing Systems, 2025.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model? InThe Thirty-Ninth Annual Conference on Neural Information Processing Systems, 2025

Reference 40

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Observation f7a37b8b-b608-4975-b3e9-7ecac6a0816d · outbound

This paper cites Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild

Reference 41

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Observation c27be518-5a20-411d-b4b7-0546b5c51438 · outbound

This paper cites Cures: From gradient analysis to efficient curriculum learning for reasoning llms.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Cures: From gradient analysis to efficient curriculum learning for reasoning llms

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

Source-reported events for the cited work

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Observation 18cc8457-84ee-4f9a-b06a-a2255195ebb4 · outbound

This paper cites On the interplay of pre-training, mid-training, and rl on reasoning language models, 2025.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model On the interplay of pre-training, mid-training, and rl on reasoning language models, 2025

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

Source-reported events for the cited work

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Observation 36f0bdf7-7d43-43d0-b75f-8fb4811e8bfd · outbound

This paper cites Kakade, Cengiz Pehlevan, Samy Jelassi, and Eran Malach.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Kakade, Cengiz Pehlevan, Samy Jelassi, and Eran Malach

Reference 44

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

Source-reported events for the cited work

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Observation 98c9bfd3-6cf0-48e0-8cb6-b7c8f7025cd5 · outbound

This paper cites Automatic curricu- lum expert iteration for reliable llm reasoning.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Automatic curricu- lum expert iteration for reliable llm reasoning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-26T11:10:56.755558Z

Source-reported events for the cited work

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Observation 25c971c0-9475-447f-a550-646d002e9c3e · outbound

This paper cites ProcessBench: Identifying Process Errors in Mathematical Reasoning.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model ProcessBench: Identifying Process Errors in Mathematical Reasoning

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:39:42.309787Z

Source-reported events for the cited work

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Observation 9860daca-8e29-48b4-8ae9-2150d7e84b98 · outbound

This paper cites Group sequence policy optimization, 2025.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Group sequence policy optimization, 2025

Reference 47

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Source-reported events for the cited work

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Observation 9f250876-a5da-45b9-91d1-02e9001bbc6d · outbound

This paper cites Spurious Forgetting in Continual Learning of Language Models.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model Spurious Forgetting in Continual Learning of Language Models

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T08:39:42.278601Z

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Observation c0543458-410c-43b3-813c-6a4ba4d1f2cc · outbound

This paper cites TTRL: Test-Time Reinforcement Learning.

Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model TTRL: Test-Time Reinforcement Learning

Reference 49

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

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

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics cites this paper.

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

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:08:30.212990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.