Paper Citation Record · LEDGER

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

As of 15 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 11 inbound Pith citation observations for arXiv:2509.09675.

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

pith.paper-citation-record.v1
2509.09675 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:53:02.820543Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:36:23.165454Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:39:57.677897Z

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 7c8d4574-d36e-4a66-9513-d4dc796d50d7 · outbound

This paper cites A Survey of Exploration Methods in Reinforcement Learning.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models A Survey of Exploration Methods in Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.597599Z

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Observation 7086f6d9-3732-48fc-84fc-8e693773f390 · outbound

This paper cites std ´␣ ϕJpwpkq n,h ˇˇ1ďkďK (¯ . Elliptical (“count-based.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models std ´␣ ϕJpwpkq n,h ˇˇ1ďkďK (¯ . Elliptical (“count-based

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.820543Z

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Observation 5aa8ec28-2077-4742-851a-d95c0b788c38 · outbound

This paper cites Qwen3-4B-Base-GRPO.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Qwen3-4B-Base-GRPO

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.813713Z

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Observation e18ab364-7475-43b4-9f0e-aa8b67c2eb79 · outbound

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

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.638102Z

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Observation 6747d781-40bb-453b-a586-a9ba2a630f5f · outbound

This paper cites Breach in the Shield: Unveiling the Vulnerabilities of Large Language Models.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Breach in the Shield: Unveiling the Vulnerabilities of Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.644861Z

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Observation b7a85f9e-f9a8-4575-8593-467edb25e281 · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.672697Z

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Observation edf8c7ba-09fc-4dea-9987-691406fe9548 · outbound

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

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models R-Zero: Self-Evolving Reasoning LLM from Zero Data

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.690293Z

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Observation 82224b45-c9a0-41ba-b27b-66d47ad1791c · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.696442Z

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Observation a6d24b4f-2202-4ed8-8b6c-22d35406aa26 · outbound

This paper cites Continuous control with deep reinforcement learning.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Continuous control with deep reinforcement learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.713848Z

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Observation 90686f68-f452-49b0-a015-954971bc96dc · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.719173Z

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Observation c3fe07f1-8719-4bc0-9cad-4ef046aac618 · outbound

This paper cites Proximal Policy Optimization Algorithms.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Proximal Policy Optimization Algorithms

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.725388Z

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Observation 0d2781dd-027f-4a65-a4c0-b329ef2326e0 · outbound

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

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.730852Z

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Observation 85d9f907-6cf2-486c-9306-6d340ed9c89a · outbound

This paper cites On entropy control in llm-rl algorithms.arXiv preprint arXiv:2509.03493,.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models On entropy control in llm-rl algorithms.arXiv preprint arXiv:2509.03493,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.736661Z

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Observation 5f4aa9d9-897b-4962-9273-2271b2e68222 · outbound

This paper cites Thermometer: Towards Universal Calibration for Large Language Models.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Thermometer: Towards Universal Calibration for Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.742636Z

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Observation 555c6c82-7c77-4038-a2f9-43400cb59813 · outbound

This paper cites Solving math word problems with process- and outcome-based feedback.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Solving math word problems with process- and outcome-based feedback

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.748248Z

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Observation f22aacd1-a65f-4627-9d38-08d962735cec · outbound

This paper cites LiteSearch: Efficacious Tree Search for LLM.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models LiteSearch: Efficacious Tree Search for LLM

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.763705Z

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Observation beefff04-48de-4cf9-b381-03c75111e970 · outbound

This paper cites Towards Self-Improvement of LLMs via MCTS: Leveraging Stepwise Knowledge with Curriculum Preference Learning.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Towards Self-Improvement of LLMs via MCTS: Leveraging Stepwise Knowledge with Curriculum Preference Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.770333Z

Source-reported events for the cited work

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Observation c00dd1be-6d63-497a-aaf2-83d01e19e1ec · outbound

This paper cites Qwen3 Technical Report.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Qwen3 Technical Report

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.782542Z

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Observation 14604fba-6511-4875-b9b9-6e734e0d09cb · outbound

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

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.789262Z

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Observation 785be2f1-8e1f-4b8f-86da-ff8b432162eb · outbound

This paper cites VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.795969Z

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Observation 0f693e32-c32b-4e35-8cc7-c3c537d19128 · outbound

This paper cites One Token to Fool LLM-as-a-Judge.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models One Token to Fool LLM-as-a-Judge

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.802208Z

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Observation 832c0674-f33c-4253-8d81-84f819a15d64 · outbound

This paper cites Learning to Reason via Mixture-of-Thought for Logical Reasoning.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Learning to Reason via Mixture-of-Thought for Logical Reasoning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.808040Z

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Observation 398b7170-af66-43e5-b8c6-f39c12d228aa · outbound

This paper cites Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Reference 1997

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.654971Z

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Observation b36915be-0427-4923-9bf1-971eaa09ee47 · outbound

This paper cites Self-Rewarding Vision-Language Model via Reasoning Decomposition.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Self-Rewarding Vision-Language Model via Reasoning Decomposition

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.707977Z

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Observation d9bacd8f-e76d-40d2-82f6-9ca30d510465 · outbound

This paper cites Navigate the unknown: Enhancing llm reasoning with intrinsic motivation guided exploration.arXiv preprint arXiv:2505.17621,.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Navigate the unknown: Enhancing llm reasoning with intrinsic motivation guided exploration.arXiv preprint arXiv:2505.17621,

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.666926Z

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Observation 1c617351-2487-4a29-9d57-4246570be9c3 · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.702231Z

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Observation 12a00344-c489-4e71-83da-94b86ecbb9da · outbound

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

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Measuring Mathematical Problem Solving With the MATH Dataset

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.684631Z

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Observation 5d9c5d8f-b005-415a-a257-49ea048cacfc · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Training Verifiers to Solve Math Word Problems

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.630026Z

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Observation 2fe00bae-31bc-4acd-938e-f62307b1658f · outbound

This paper cites Supervising the search process produces reliable and generalizable information-seeking agents.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Supervising the search process produces reliable and generalizable information-seeking agents

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.776951Z

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Observation f0ebcc62-be89-4ba5-b101-e6d25f04a06e · outbound

This paper cites Online Preference Alignment for Language Models via Count-based Exploration.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Online Preference Alignment for Language Models via Count-based Exploration

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.606163Z

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Observation 3cdec345-3fa4-4bd6-ba62-80192386feb1 · outbound

This paper cites Calibrating Large Language Models Using Their Generations Only.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Calibrating Large Language Models Using Their Generations Only

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.754686Z

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Observation c8f1606b-f184-41d0-b092-a942c03e5d23 · outbound

This paper cites Deep Think with Confidence.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Deep Think with Confidence

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.660976Z

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Observation 7fce78d2-28a7-4bd5-b754-c5491bdec338 · outbound

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

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.678938Z

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Observation 5a58071c-5895-4ae0-ab48-1c5826335408 · outbound

This paper cites Exploration by Random Network Distillation.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Exploration by Random Network Distillation

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.613187Z

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Pith citing papers

Observation 09f464ea-0ad7-4475-8c3d-19cd3b1ec900 · inbound

StatEval: A Comprehensive Benchmark for Large Language Models in Statistics cites this paper.

StatEval: A Comprehensive Benchmark for Large Language Models in Statistics CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T10:36:23.165454Z

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Observation 93ca5813-c903-41f5-8c9b-4dd664148796 · inbound

Calibration-Aware Policy Optimization for Reasoning LLMs cites this paper.

Calibration-Aware Policy Optimization for Reasoning LLMs CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:06:01.061297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c50ad9dd-2bad-4881-a5e8-43369204a2e7 · inbound

Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data cites this paper.

Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:36:01.999728Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 45a82209-4970-43e8-9141-2cf073ebb7ec · inbound

Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning cites this paper.

Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:11:28.843337Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 368f6c5e-2403-4604-9882-89a8c77393e9 · inbound

Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning cites this paper.

Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:49:14.899678Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 236cf7f9-3abe-43d0-8427-5a59be816a68 · inbound

Reinforcing Multimodal Reasoning Against Visual Degradation cites this paper.

Reinforcing Multimodal Reasoning Against Visual Degradation CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:06:25.142345Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9d32ec0f-a3ba-43e5-bbd1-5de9823bc9c3 · inbound

DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification cites this paper.

DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:01:24.281809Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3039b200-07d3-4b2c-a9be-e5fcab8a40e4 · inbound

Epistemic Uncertainty for Test-Time Discovery cites this paper.

Epistemic Uncertainty for Test-Time Discovery CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:57:06.101531Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1358ee36-5a8f-4a5b-a9b9-66951295ece0 · inbound

SALT: When More Rollouts Don't Help in Group-Based Policy Optimization and How to Make Them Matter cites this paper.

SALT: When More Rollouts Don't Help in Group-Based Policy Optimization and How to Make Them Matter CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:46:56.173296Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 92d15d31-edab-49ac-955e-6afb9bba302f · inbound

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning cites this paper.

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-07-04T07:59:40.656115Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 59aabf2b-ef39-4e89-967b-6d107da89b62 · inbound

ExTra: Exploratory Trajectory Optimization for Language Model Reinforcement Learning cites this paper.

ExTra: Exploratory Trajectory Optimization for Language Model Reinforcement Learning CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:39:57.679442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.