Paper Citation Record · LEDGER

Outcome-based Exploration for LLM Reasoning

As of 18 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 25 inbound Pith citation observations for arXiv:2509.06941.

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

pith.paper-citation-record.v1
2509.06941 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:59:14.608524Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:15:16.022969Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy3
unresolved35
parse uncertain0
malformed identifier0
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External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 772784dd-2c91-4acc-97f1-975b46d09b0d · outbound

This paper cites 2:fort= 1,2,.

Outcome-based Exploration for LLM Reasoning 2:fort= 1,2,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:59:15.222855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 288c2f09-c7eb-49ea-adb0-38ff0434e55b · outbound

This paper cites Dataset Reset Policy Optimization for RLHF.

Outcome-based Exploration for LLM Reasoning Dataset Reset Policy Optimization for RLHF

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.467898Z

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Observation d36f566e-f01d-46dc-9179-90ccd70433a4 · outbound

This paper cites Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models.

Outcome-based Exploration for LLM Reasoning Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.473018Z

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Observation 34ba54e2-b4ab-4ee0-846f-2aa771c9ce62 · outbound

This paper cites Reasoning with Exploration: An Entropy Perspective.

Outcome-based Exploration for LLM Reasoning Reasoning with Exploration: An Entropy Perspective

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.477484Z

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Observation 3ce41058-e781-4281-a027-09c512726caa · outbound

This paper cites Weight ensembling improves reasoning in language models.arXiv preprint arXiv:2504.10478,.

Outcome-based Exploration for LLM Reasoning Weight ensembling improves reasoning in language models.arXiv preprint arXiv:2504.10478,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.481689Z

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Observation 4e0d72bb-28eb-42d3-b440-06304a84dbfb · outbound

This paper cites The Statistical Complexity of Interactive Decision Making.

Outcome-based Exploration for LLM Reasoning The Statistical Complexity of Interactive Decision Making

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.485864Z

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Observation cc6109ad-08bf-4aef-a875-997ebe7deb32 · outbound

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

Outcome-based Exploration for LLM Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.494351Z

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Observation a506f41a-267b-49c8-a223-5e200306f839 · outbound

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

Outcome-based Exploration for LLM Reasoning Measuring Mathematical Problem Solving With the MATH Dataset

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.498713Z

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Observation 359e9c1c-7d9d-440f-96fe-bbdbc4be1d7a · outbound

This paper cites OpenAI o1 System Card.

Outcome-based Exploration for LLM Reasoning OpenAI o1 System Card

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.503377Z

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Observation 94662f91-9583-418f-89b4-06d067697620 · outbound

This paper cites Understanding the Effects of RLHF on LLM Generalisation and Diversity.

Outcome-based Exploration for LLM Reasoning Understanding the Effects of RLHF on LLM Generalisation and Diversity

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.507748Z

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Observation 2c1a1a68-f682-4f72-ab3e-c7d1d3067f65 · outbound

This paper cites Jointly Reinforcing Diversity and Quality in Language Model Generations.

Outcome-based Exploration for LLM Reasoning Jointly Reinforcing Diversity and Quality in Language Model Generations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.516795Z

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Observation 77a22718-0a52-4f80-9d4b-b5cd772f1e00 · outbound

This paper cites Approximating kl divergence, 2020.URL http://joschu.

Outcome-based Exploration for LLM Reasoning Approximating kl divergence, 2020.URL http://joschu

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:59:15.239298Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 498f03d7-ccc7-4751-8b18-066d62061239 · outbound

This paper cites Can large reasoning models self-train?arXiv preprint arXiv:2505.21444,.

Outcome-based Exploration for LLM Reasoning Can large reasoning models self-train?arXiv preprint arXiv:2505.21444,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.538465Z

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Observation 26c7fea0-23d2-4572-910d-e602d65a79a6 · outbound

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

Outcome-based Exploration for LLM Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.543111Z

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Observation 2daa3bfb-6428-44ca-a5e3-92d2dcbcebee · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

Outcome-based Exploration for LLM Reasoning HybridFlow: A Flexible and Efficient RLHF Framework

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.547422Z

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Observation ae28b6aa-0078-4384-a72b-596394671fad · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Outcome-based Exploration for LLM Reasoning Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.551823Z

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Observation 73d87d62-3600-436e-8f4c-b412c9fef2d6 · outbound

This paper cites Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models.

Outcome-based Exploration for LLM Reasoning Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.556101Z

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Observation fb93e9e2-8fda-416f-af68-8a2d11cd188d · outbound

This paper cites On a few pitfalls in KL divergence gradient estimation for RL.

Outcome-based Exploration for LLM Reasoning On a few pitfalls in KL divergence gradient estimation for RL

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.560552Z

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Observation 0d78bad3-7a1d-4923-8247-095ae001384d · outbound

This paper cites Optimizing Language Models for Inference Time Objectives using Reinforcement Learning.

Outcome-based Exploration for LLM Reasoning Optimizing Language Models for Inference Time Objectives using Reinforcement Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.565055Z

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Observation ab716baf-2fff-4450-87df-83fb63bd4183 · outbound

This paper cites The invisible leash: Why rlvr may not escape its origin.arXiv preprint arXiv:2507.14843,.

Outcome-based Exploration for LLM Reasoning The invisible leash: Why rlvr may not escape its origin.arXiv preprint arXiv:2507.14843,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.569218Z

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Observation ee64cf97-aa98-409a-94f1-1a11ad286fef · outbound

This paper cites Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF.

Outcome-based Exploration for LLM Reasoning Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.573190Z

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Observation ca620d9e-6f09-4fc7-bfbe-5a69903cbd8f · outbound

This paper cites Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint.

Outcome-based Exploration for LLM Reasoning Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.577593Z

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Observation 635fd568-f897-4a7b-80ec-6f02b0858d77 · outbound

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

Outcome-based Exploration for LLM Reasoning DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.585858Z

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Observation ac98f467-6180-45da-b120-1910ac200a53 · outbound

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

Outcome-based Exploration for LLM Reasoning Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.590415Z

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Observation 02725c34-f758-487b-923c-88331c46836e · outbound

This paper cites The Price of Format: Diversity Collapse in LLMs.

Outcome-based Exploration for LLM Reasoning The Price of Format: Diversity Collapse in LLMs

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.595531Z

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Observation 07e30d42-0401-4a3e-98d7-9f776f3d8e3e · outbound

This paper cites Self-Exploring Language Models: Active Preference Elicitation for Online Alignment.

Outcome-based Exploration for LLM Reasoning Self-Exploring Language Models: Active Preference Elicitation for Online Alignment

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.599880Z

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Observation 14b2af2a-9784-420d-848f-0382d14a4838 · outbound

This paper cites First Return, Entropy-Eliciting Explore.

Outcome-based Exploration for LLM Reasoning First Return, Entropy-Eliciting Explore

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.604039Z

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Observation 923ed1ad-05bf-484b-b01e-fd396b1f5ffd · outbound

This paper cites Optimistically optimistic exploration for provably efficient infinite- horizon reinforcement and imitation learning.arXiv preprint arXiv:2502.13900,.

Outcome-based Exploration for LLM Reasoning Optimistically optimistic exploration for provably efficient infinite- horizon reinforcement and imitation learning.arXiv preprint arXiv:2502.13900,

Reference 1996

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.521058Z

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Observation d6b198d2-3e16-4493-851b-f3f8ea1ea061 · outbound

This paper cites Exploration by Random Network Distillation.

Outcome-based Exploration for LLM Reasoning Exploration by Random Network Distillation

Reference 2002

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.458521Z

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Observation 8590e1fa-3675-4041-bd44-43f2f9dd1d6e · outbound

This paper cites Diverse Preference Optimization.

Outcome-based Exploration for LLM Reasoning Diverse Preference Optimization

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.512402Z

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Observation e2c308a8-ab1d-4242-83f7-f1ad2fa216db · outbound

This paper cites Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF.

Outcome-based Exploration for LLM Reasoning Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.463200Z

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Observation 5cc9057c-9b41-48ca-8c6c-47c9a99027ba · outbound

This paper cites Qwen2.5 Technical Report.

Outcome-based Exploration for LLM Reasoning Qwen2.5 Technical Report

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.581505Z

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Observation 62e79bd9-5de4-4b91-b579-44a002b56daf · outbound

This paper cites e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs.

Outcome-based Exploration for LLM Reasoning e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.534182Z

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Observation 4927a9db-db97-43b7-bc2c-0629bb70414b · outbound

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

Outcome-based Exploration for LLM Reasoning Navigate the unknown: Enhancing llm reasoning with intrinsic motivation guided exploration.arXiv preprint arXiv:2505.17621,

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.490216Z

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Observation d04a1e4f-1a34-405c-bc14-f9f4ca59e70d · outbound

This paper cites Attributing mode collapse in the fine-tuning of large language models.

Outcome-based Exploration for LLM Reasoning Attributing mode collapse in the fine-tuning of large language models

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:59:15.254676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 964e69e0-4b1d-4850-ae05-6aa530a6b73f · outbound

This paper cites Asymmetric rein- force for off-policy reinforcement learning: Balancing positive and negative rewards.arXiv preprint arXiv:2506.20520,.

Outcome-based Exploration for LLM Reasoning Asymmetric rein- force for off-policy reinforcement learning: Balancing positive and negative rewards.arXiv preprint arXiv:2506.20520,

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.443923Z

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Observation a61d8773-626d-46fe-b3bf-f7fa28628346 · outbound

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

Outcome-based Exploration for LLM Reasoning Online Preference Alignment for Language Models via Count-based Exploration

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.448812Z

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Observation b385f625-764e-405a-b34c-728dee68553d · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Outcome-based Exploration for LLM Reasoning Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-04T22:59:14.454002Z

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Pith citing papers

Observation aba17bf7-aa41-4982-be33-c74970665f6d · inbound

Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision cites this paper.

Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision Outcome-based Exploration for LLM Reasoning

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:46:34.147476Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 93fc4f7e-c621-4a05-9be5-d73b16ef952b · inbound

Polychromic Objectives for Reinforcement Learning cites this paper.

Polychromic Objectives for Reinforcement Learning Outcome-based Exploration for LLM Reasoning

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-18T11:56:20.005263Z

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Observation d7bb58a8-660f-45d5-a3b7-88336b8b09c5 · inbound

Selective Expert Guidance for Effective and Diverse Exploration in Reinforcement Learning of LLMs cites this paper.

Selective Expert Guidance for Effective and Diverse Exploration in Reinforcement Learning of LLMs Outcome-based Exploration for LLM Reasoning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T11:33:55.597960Z

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Observation 47bb9eb1-86ab-484e-aecf-a73e478b05a3 · inbound

On the optimization dynamics of RLVR: Gradient gap and step size thresholds cites this paper.

On the optimization dynamics of RLVR: Gradient gap and step size thresholds Outcome-based Exploration for LLM Reasoning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-18T08:36:07.367130Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f7150d8a-8e2d-4f20-95f4-cc2b6b036feb · inbound

Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective cites this paper.

Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective Outcome-based Exploration for LLM Reasoning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-18T07:41:03.322829Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8365833d-3a81-4807-8ea1-032cec879060 · inbound

Beyond the Sampled Token: Preserving Candidate Support in RLVR cites this paper.

Beyond the Sampled Token: Preserving Candidate Support in RLVR Outcome-based Exploration for LLM Reasoning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T09:34:09.191451Z

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Observation 7f61e66d-647e-4ac4-9bb8-3c2b623fc48a · inbound

The Past Is Not Past: Memory-Enhanced Dynamic Reward Shaping cites this paper.

The Past Is Not Past: Memory-Enhanced Dynamic Reward Shaping Outcome-based Exploration for LLM Reasoning

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:16:05.109966Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ec353b52-5182-4790-b97c-e9927c819038 · inbound

The Past Is Not Past: Memory-Enhanced Dynamic Reward Shaping cites this paper.

The Past Is Not Past: Memory-Enhanced Dynamic Reward Shaping Outcome-based Exploration for LLM Reasoning

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T15:35:32.810462Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bc8eb1c4-2005-4bcb-8c0a-d00995cf9619 · inbound

Uniform-Correct Policy Optimization: Breaking RLVR's Indifference to Diversity cites this paper.

Uniform-Correct Policy Optimization: Breaking RLVR's Indifference to Diversity Outcome-based Exploration for LLM Reasoning

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:26:15.307690Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3a0da2d4-b844-4ad7-966f-e3e171b50b9c · inbound

Rebellious Student: Reversing Teacher Signals for Reasoning Exploration with Self-Distilled RLVR cites this paper.

Rebellious Student: Reversing Teacher Signals for Reasoning Exploration with Self-Distilled RLVR Outcome-based Exploration for LLM Reasoning

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:21:22.471904Z

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Observation 9d1c9fdd-fa13-48a5-8ad1-5b2657b1e058 · inbound

Automated Reformulation of Robust Optimization via Memory-Augmented Large Language Models cites this paper.

Automated Reformulation of Robust Optimization via Memory-Augmented Large Language Models Outcome-based Exploration for LLM Reasoning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:02:22.006754Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 15b83eb1-f291-473d-bba4-8dbfa2f28860 · inbound

Beyond Mode Collapse: Distribution Matching for Diverse Reasoning cites this paper.

Beyond Mode Collapse: Distribution Matching for Diverse Reasoning Outcome-based Exploration for LLM Reasoning

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:33:04.041635Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6b0ad7f3-33f2-4b9e-a5a8-d3c0de4561b9 · inbound

Where Rollouts Begin: Low-Load, High-Leverage First-Token Diversification for RLVR cites this paper.

Where Rollouts Begin: Low-Load, High-Leverage First-Token Diversification for RLVR Outcome-based Exploration for LLM Reasoning

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-06-29T12:03:24.374582Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5c2f4873-9732-44ca-92cd-a2ca7728e82b · inbound

Exploiting Verification-Generation Gap: Test-Time Reinforcement Learning with Confidence-Conditioned Verification cites this paper.

Exploiting Verification-Generation Gap: Test-Time Reinforcement Learning with Confidence-Conditioned Verification Outcome-based Exploration for LLM Reasoning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:26:27.241826Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cfbde808-e086-48c3-a262-22ea20a23115 · inbound

Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning cites this paper.

Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning Outcome-based Exploration for LLM Reasoning

Reference 53

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:26:26.262089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 04a2bc2c-32e2-40d3-9bb4-51b9022ca6e9 · inbound

On Advantage Estimates for Max@K Policy Gradients cites this paper.

On Advantage Estimates for Max@K Policy Gradients Outcome-based Exploration for LLM Reasoning

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:06:56.413899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a8126bf8-e979-40d1-a9e8-ef3344887b16 · inbound

OrderGrad: Optimizing Beyond the Mean with Order-Statistic Policy Gradient Estimation cites this paper.

OrderGrad: Optimizing Beyond the Mean with Order-Statistic Policy Gradient Estimation Outcome-based Exploration for LLM Reasoning

Reference 90

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:16:56.999981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d9b42875-8786-441d-b3f2-9035010c79b4 · inbound

Sample Where You Struggle: Sharpening Base Model Reasoning via Entropy-Guided Power Sampling cites this paper.

Sample Where You Struggle: Sharpening Base Model Reasoning via Entropy-Guided Power Sampling Outcome-based Exploration for LLM Reasoning

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T22:37:25.535668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 460713b0-7beb-42ad-921e-862a533385bf · inbound

Reasoning or Memorization? Direction-Aware Diversity Exploration in LLM Reinforcement Learning cites this paper.

Reasoning or Memorization? Direction-Aware Diversity Exploration in LLM Reinforcement Learning Outcome-based Exploration for LLM Reasoning

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T04:47:38.177041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 09bd86c5-8aec-4a16-8f1c-288d66fe7ce6 · inbound

When are likely answers right? On Sequence Probability and Correctness in LLMs cites this paper.

When are likely answers right? On Sequence Probability and Correctness in LLMs Outcome-based Exploration for LLM Reasoning

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:09:54.111481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ae9caf7-aae5-4d94-965c-b29c28373d27 · inbound

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning cites this paper.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Outcome-based Exploration for LLM Reasoning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a903df79-6c71-478c-beec-f75b19897c0a · inbound

It Takes 8 Tokens: Weak-to-Strong Off-Policy RL via Auxiliary Branches cites this paper.

It Takes 8 Tokens: Weak-to-Strong Off-Policy RL via Auxiliary Branches Outcome-based Exploration for LLM Reasoning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-02T14:45:24.695248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48f9f5bd-daf5-4f16-aa10-d070be0d09a1 · inbound

Bridging Compute- and Data-Optimal Pretraining cites this paper.

Bridging Compute- and Data-Optimal Pretraining Outcome-based Exploration for LLM Reasoning

Reference 107

Resolution: unresolved; no resolver link, observed 2026-08-01T03:02:07.968437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93ca818a-4402-4f00-a1c5-b184bffdb329 · inbound

Instruction-Conditioned Exploration for Reinforcement Learning with Self-Distillation to an Unconditioned Policy cites this paper.

Instruction-Conditioned Exploration for Reinforcement Learning with Self-Distillation to an Unconditioned Policy Outcome-based Exploration for LLM Reasoning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T15:25:49.290473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0751ac2f-e549-4bfc-917f-80d0aead3b5c · inbound

Instruction-Conditioned Exploration for Reinforcement Learning with Self-Distillation to an Unconditioned Policy cites this paper.

Instruction-Conditioned Exploration for Reinforcement Learning with Self-Distillation to an Unconditioned Policy Outcome-based Exploration for LLM Reasoning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:16.022969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.