Paper Citation Record · LEDGER

Reinforcement Learning for Reasoning in Large Language Models with One Training Example

As of 5 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 61 inbound Pith citation observations for arXiv:2504.20571.

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

pith.paper-citation-record.v1
2504.20571 v3

Coverage vector

measured 69 of 69 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-15T19:51:04.779597Z

measured 130 of 130 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 61 of 61 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:55:28.597027Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

69 of 69 outbound references displayed

verified exact40
verified fuzzy22
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch6

External citation measurements

7
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 8463121b-b1d5-43f8-bcd5-c0ca222f5325 · outbound

This paper cites Learning to reason with llms.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Learning to reason with llms

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.177303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 843fc5fc-219d-41b6-8d90-e2ff72eedd0a · outbound

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

Reinforcement Learning for Reasoning in Large Language Models with One Training Example DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:04.844671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5411608b-f66e-48e2-bd8e-c4f42fd216c6 · outbound

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

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:04.852051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3d72fe2e-f683-4894-93c7-68d3f028cfd3 · outbound

This paper cites On Designing Effective RL Reward at Training Time for LLM Reasoning.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example On Designing Effective RL Reward at Training Time for LLM Reasoning

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:51:04.859644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 87b81421-b53a-40ba-91d2-106b8c5f8f5c · outbound

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

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T19:51:04.866291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fc6ddccd-e9fc-452d-b0ec-e119f35f12a8 · outbound

This paper cites Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-17T11:40:33.380427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 36c6a299-11ef-4e56-89d3-bb545b01c390 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Proximal Policy Optimization Algorithms

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:04.879886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b51d1bfe-0d26-4b9a-b229-dc4efe278036 · outbound

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

Reinforcement Learning for Reasoning in Large Language Models with One Training Example DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:04.884980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5c437fa1-e43b-4fe2-9ac4-1f9243aeb264 · outbound

This paper cites VinePPO: Refining Credit Assignment in RL Training of LLMs.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example VinePPO: Refining Credit Assignment in RL Training of LLMs

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:51:04.893040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fac2e47b-4999-44bc-8193-6a25d9126a7b · outbound

This paper cites What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:04.900537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ce80f86d-eeb6-4241-9a09-21e0f4e91a77 · outbound

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

Reinforcement Learning for Reasoning in Large Language Models with One Training Example DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:04.906397Z

Source-reported events for the cited work

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Observation cee44b9b-7798-4947-ac7b-86376f501c6d · outbound

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

Reinforcement Learning for Reasoning in Large Language Models with One Training Example VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:04.931329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation aabc6d2c-69c3-44b2-9775-d671920d3e6b · outbound

This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Understanding R1-Zero-Like Training: A Critical Perspective

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:04.937895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b0bc476b-3f85-4b2a-a01e-ed8bf304e5c0 · outbound

This paper cites Deepcoder: A fully open-source 14b coder at o3-mini level.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Deepcoder: A fully open-source 14b coder at o3-mini level

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.140747Z

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Observation 4fe3e517-7543-4635-a352-ec1c2a920bfa · outbound

This paper cites REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.086065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2979b2dd-a0bb-412b-8df2-a5ccdccbded3 · outbound

This paper cites Srpo: A cross-domain implementation of large-scale reinforcement learning on llm.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Srpo: A cross-domain implementation of large-scale reinforcement learning on llm

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.150567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 13576480-c537-4a47-a2e0-917124a0ea70 · outbound

This paper cites Numinamath.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Numinamath

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.154941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ea1dd23a-3b73-4412-8edb-4727ac7f3c05 · outbound

This paper cites Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.159497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1e0c9d2a-a9ab-408f-9fea-7357485475cd · outbound

This paper cites Limr: Less is more for rl scaling.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Limr: Less is more for rl scaling

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.164285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 23003ecf-b9d1-4578-aa82-057a56a42395 · outbound

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

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.091802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6e0d4383-d98e-4329-bae7-b28ca559b054 · outbound

This paper cites Rethinking Reflection in Pre-Training.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Rethinking Reflection in Pre-Training

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.097590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fcf169b3-66db-437c-be42-8238dcde9af3 · outbound

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

Reinforcement Learning for Reasoning in Large Language Models with One Training Example HybridFlow: A Flexible and Efficient RLHF Framework

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.102918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 84cc12e7-1966-4fc6-98ae-125f2872f1da · outbound

This paper cites What makes a reward model a good teacher? an optimization perspective.arXiv preprint arXiv:2503.15477.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example What makes a reward model a good teacher? an optimization perspective.arXiv preprint arXiv:2503.15477

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.108901Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1abeb772-11dd-479a-a13d-9435f7103148 · outbound

This paper cites Qwen2.5 Technical Report.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Qwen2.5 Technical Report

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.114377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4ddd9e76-925a-49f3-aa88-d8bee5e54ec8 · outbound

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

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.120340Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5d24e96d-549a-4e6e-85c2-fc981f7cfb4f · outbound

This paper cites The Llama 3 Herd of Models.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example The Llama 3 Herd of Models

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.125944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c3de5f4a-ed65-41d2-a7d9-77010e4b3036 · outbound

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

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Measuring Mathematical Problem Solving With the MATH Dataset

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.131015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b67c5265-ec96-4f5c-aeb9-ffb0ac0c2141 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Gonzalez, Hao Zhang, and Ion Stoica

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.203363Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 13bec755-0736-4380-83aa-244082484b84 · outbound

This paper cites Let's Verify Step by Step.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Let's Verify Step by Step

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.135648Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3e9ef792-e0c8-4d1c-b4a1-086e03aa070c · outbound

This paper cites Aime problems and solutions.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Aime problems and solutions

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.211331Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a805ea32-f4e7-46af-aa54-bf931c2ec4fe · outbound

This paper cites Amc problems and solutions.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Amc problems and solutions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.215330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4f18ef18-be0f-473d-80ca-cb437e8b5a9e · outbound

This paper cites Solving quantitative reasoning problems with language models.Advances in Neural Information Processing Systems, 35:3843–3857.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Solving quantitative reasoning problems with language models.Advances in Neural Information Processing Systems, 35:3843–3857

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.220054Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation efde09b4-e18c-4b22-986a-b3394130ce5e · outbound

This paper cites OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:04.944360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 11cfa2c8-a7ed-474b-9e2d-375d91a32853 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:04.952016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1b1f6322-5128-4e25-a161-11cfdef840cd · outbound

This paper cites EvalTree: Profiling Language Model Weaknesses via Hierarchical Capability Trees.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example EvalTree: Profiling Language Model Weaknesses via Hierarchical Capability Trees

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:04.958598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8d9ccd95-80b0-4530-a4a4-716df1d57e35 · outbound

This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:04.965010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 56670751-48f9-4bc0-90c8-2dd89773d875 · outbound

This paper cites Deep Grokking: Would Deep Neural Networks Generalize Better?.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Deep Grokking: Would Deep Neural Networks Generalize Better?

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:04.972881Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6275d4fe-b964-45e3-97f4-58ec2de7eee8 · outbound

This paper cites Progress measures for grokking via mechanistic interpretability.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Progress measures for grokking via mechanistic interpretability

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:04.977899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 704c7c1b-a68f-4074-9204-915195302826 · outbound

This paper cites Towards understanding grokking: An effective theory of representation learning.Advances in Neural Information Processing Systems, 35:34651–34663.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Towards understanding grokking: An effective theory of representation learning.Advances in Neural Information Processing Systems, 35:34651–34663

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.173391Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4d49d725-10f8-4629-9d37-103edb2dd138 · outbound

This paper cites The Complexity Dynamics of Grokking.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example The Complexity Dynamics of Grokking

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:04.983727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6bad6672-554e-47bb-ae4a-f37db1727bef · outbound

This paper cites Grokking at the Edge of Numerical Stability.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Grokking at the Edge of Numerical Stability

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:51:04.990155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3a7af56d-e27e-457f-9d2e-58d10736a594 · outbound

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

Reinforcement Learning for Reasoning in Large Language Models with One Training Example SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:04.995171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 89e88595-6b82-4a51-ba4e-fabbdd4e0f50 · outbound

This paper cites Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond

Reference 43

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:51:05.002577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4132bbca-c88a-47bb-8343-c8be44aaf6cc · outbound

This paper cites Fastcurl: Curriculum reinforcement learning with stage-wise context scaling for efficient training r1-like reasoning models.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Fastcurl: Curriculum reinforcement learning with stage-wise context scaling for efficient training r1-like reasoning models

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.008534Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 24c1b142-0484-4d58-9960-dbe23dc2652d · outbound

This paper cites Absolute Zero: Reinforced Self-play Reasoning with Zero Data.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.014418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f872eae1-12ef-47fa-8932-3b10e4dffb34 · outbound

This paper cites Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.021005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f86d6208-7dff-4623-ba6d-f875029cd0b8 · outbound

This paper cites TTRL: Test-Time Reinforcement Learning.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example TTRL: Test-Time Reinforcement Learning

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.027001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7ad7eead-b72b-4edc-ab2d-e2114b9f9814 · outbound

This paper cites Large-Scale Data Selection for Instruction Tuning.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Large-Scale Data Selection for Instruction Tuning

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:51:05.034374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0c577a14-1fa0-4612-9bb3-c3b69a252768 · outbound

This paper cites Alpagasus: Training a better alpaca with fewer data.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Alpagasus: Training a better alpaca with fewer data

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.233005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 23422147-dbc9-45c9-b1c3-d713982f9adb · outbound

This paper cites Smith, Hannaneh Hajishirzi, and Pradeep Dasigi.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Smith, Hannaneh Hajishirzi, and Pradeep Dasigi

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.236850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2b50c2b9-3990-4065-b781-9c8d0fa75229 · outbound

This paper cites LESS: selecting influential data for targeted instruction tuning.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example LESS: selecting influential data for targeted instruction tuning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.145356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 300baaab-6177-4ddf-ace3-da23afa1e48f · outbound

This paper cites Active preference learning for large language models.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Active preference learning for large language models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.168830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b88e2ac5-cdc8-4102-964c-61babca6a388 · outbound

This paper cites Enabling Weak LLMs to Judge Response Reliability via Meta Ranking.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Enabling Weak LLMs to Judge Response Reliability via Meta Ranking

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:04.837297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3c1a0ce5-1a1c-4302-ad9c-3deb7fd487ab · outbound

This paper cites Active Preference Optimization for Sample Efficient RLHF.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Active Preference Optimization for Sample Efficient RLHF

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.040421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2db93978-383a-4694-b1b0-05266e629434 · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in Neural Information Processing Systems.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Training language models to follow instructions with human feedback.Advances in Neural Information Processing Systems

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.186078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 33596df9-c1c4-42b4-95db-a08d683a0a96 · outbound

This paper cites Concise reasoning via reinforcement learning.arXiv preprint arXiv:2504.05185.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Concise reasoning via reinforcement learning.arXiv preprint arXiv:2504.05185

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.047054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6c05cdff-c670-497c-ba7f-d2446bb8316f · outbound

This paper cites Schulman.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Schulman

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.194758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2e341750-2138-46c8-9ec1-da57a5347267 · outbound

This paper cites Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.052431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c9c3e60c-fd4d-446a-b4af-14053b61484e · outbound

This paper cites Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:35:31.553327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b349fb69-6cc8-4a0c-8902-777cef9d4dda · outbound

This paper cites Skywork open reasoner series.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Skywork open reasoner series

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.224160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 80b4ccd6-9441-4721-a1b9-ca8877e95559 · outbound

This paper cites A Survey on LLM-as-a-Judge.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example A Survey on LLM-as-a-Judge

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.063839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b92831f0-a2d8-4795-b403-82ec72eaa5b2 · outbound

This paper cites Qwq-32b: Embracing the power of reinforcement learning, March 2025.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Qwq-32b: Embracing the power of reinforcement learning, March 2025

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.181718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5875cb2f-c345-4953-8204-9f78a78aee60 · outbound

This paper cites A Survey on In-context Learning.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example A Survey on In-context Learning

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.068857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e70c6b57-36fa-4b3a-8c0f-fcadd44d65b0 · outbound

This paper cites Deep Learning is Robust to Massive Label Noise.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Deep Learning is Robust to Massive Label Noise

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.074776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fa047314-872e-4088-a1b4-22ba7582fd50 · outbound

This paper cites Deep double descent: Where bigger models and more data hurt.Journal of Statistical Mechanics: Theory and Experiment, 2021(12):124003.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Deep double descent: Where bigger models and more data hurt.Journal of Statistical Mechanics: Theory and Experiment, 2021(12):124003

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.207458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c635af5e-0242-4d74-826a-1916b9bc5f1a · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.080527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4576612c-ff1c-49f0-9e02-b233b0e11fba · outbound

This paper cites Smith, Benoit Dherin, David G.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Smith, Benoit Dherin, David G

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.190750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 783b5164-98ac-4685-bebd-3fd3e8f8b850 · outbound

This paper cites Acemath: Advancing frontier math reasoning with post-training and reward modeling.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Acemath: Advancing frontier math reasoning with post-training and reward modeling

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:51:05.199248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 61f95623-acc5-4a72-a3f6-ce1371155490 · outbound

This paper cites L′ PG-GRPO(·, θ) +βL ′ KL(·, θ, θref) +αL ′ Entropy(·, θ) # ,(3) where β and α are hyper-parameters (in general β >0 , α <0 ), and “·.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example L′ PG-GRPO(·, θ) +βL ′ KL(·, θ, θref) +αL ′ Entropy(·, θ) # ,(3) where β and α are hyper-parameters (in general β >0 , α <0 ), and “·

Reference 69

Resolution: malformed identifier; raw_fallback, observed 2026-05-15T19:51:05.228629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

Observation 6037df06-3d9a-4400-98df-705d2b3222b2 · inbound

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems cites this paper.

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 165

Resolution: verified exact; local_arxiv, observed 2026-05-22T21:42:11.062930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ddb875a3-dde7-4df8-9d81-f33f23f2a9a1 · inbound

The Unreasonable Effectiveness of Entropy Minimization in LLM Reasoning cites this paper.

The Unreasonable Effectiveness of Entropy Minimization in LLM Reasoning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 88

Resolution: verified exact; local_arxiv, observed 2026-05-18T15:58:33.535702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation df1f535c-3613-4506-b444-84dd3fb8bcab · inbound

Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning cites this paper.

Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f0840b4a-20cd-425a-bafe-30c7d1e93f58 · inbound

Mixture-of-Experts Can Surpass Dense LLMs Under Strictly Equal Resource cites this paper.

Mixture-of-Experts Can Surpass Dense LLMs Under Strictly Equal Resource Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-05-22T00:05:47.611002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 90e8a6b3-0f95-4722-878e-f9142e2cb60c · inbound

Hierarchical Reasoning Model cites this paper.

Hierarchical Reasoning Model Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 99

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 97238aa9-57a3-4003-bb15-0ac9bee3ee39 · inbound

Generalizing Verifiable Instruction Following cites this paper.

Generalizing Verifiable Instruction Following Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-19T05:42:05.989110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cc458fcf-f3f6-4432-8fef-b3bd6e6ba1de · inbound

The Serial Scaling Hypothesis cites this paper.

The Serial Scaling Hypothesis Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 123

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:12:02.419405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 25803ded-892f-4eb0-8dfd-08c9acb5dbfa · inbound

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization cites this paper.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.104689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2e3b7d35-cd44-463f-816c-8df179fb8adf · inbound

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned cites this paper.

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T22:55:28.597027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3956e337-5322-46db-8da2-37a0c21ed02f · inbound

DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search cites this paper.

DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:12:35.904803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c47e22c4-2d7e-40b8-8d5e-d98405d5ef39 · inbound

XRPO: Pushing the limits of GRPO with Targeted Exploration and Exploitation cites this paper.

XRPO: Pushing the limits of GRPO with Targeted Exploration and Exploitation Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T11:11:12.717090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8483788b-ad20-4095-8610-8555719921a9 · inbound

Base Models Know How to Reason, Thinking Models Learn When cites this paper.

Base Models Know How to Reason, Thinking Models Learn When Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T11:02:19.585843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ea65617-6196-4d4f-b4a5-6101a2cf85e6 · inbound

Unlocking Exploration in RLVR: Uncertainty-aware Advantage Shaping for Deeper Reasoning cites this paper.

Unlocking Exploration in RLVR: Uncertainty-aware Advantage Shaping for Deeper Reasoning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-18T07:21:04.419242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0d9e54dc-572f-4166-88d6-ec1dd1e6bd86 · inbound

Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting cites this paper.

Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T08:49:34.341309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53825121-318c-45f8-bb7e-7751ca1e669c · inbound

Sharpness-Guided Group Relative Policy Optimization via Probability Shaping cites this paper.

Sharpness-Guided Group Relative Policy Optimization via Probability Shaping Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-18T03:12:22.151814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2e67659e-2962-4cb4-b066-412c13cbcf6b · inbound

Differentiable Evolutionary Reinforcement Learning cites this paper.

Differentiable Evolutionary Reinforcement Learning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 24

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T22:38:37.697896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8e1f2154-235d-4662-a4c5-651862baaaac · inbound

Learning to Discover at Test Time cites this paper.

Learning to Discover at Test Time Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-05-16T05:16:04.253884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0b947e40-a8bb-4b4d-94d6-e2c353ac01c7 · inbound

Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning cites this paper.

Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 19

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T10:27:44.451881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a2ac95ec-0a92-453c-8de6-d1fffd89561d · inbound

Conversation for Non-verifiable Learning: Self-Evolving LLMs through Meta-Evaluation cites this paper.

Conversation for Non-verifiable Learning: Self-Evolving LLMs through Meta-Evaluation Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-16T10:07:43.364006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9e02310b-c160-401d-bfba-c18ca0d98734 · inbound

Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models cites this paper.

Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-21T14:10:12.992968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 70b8840c-b6a1-45be-bf21-4a5bd9a7919e · inbound

Beyond Variance: Prompt-Efficient RLVR via Rare-Event Amplification and Bidirectional Pairing cites this paper.

Beyond Variance: Prompt-Efficient RLVR via Rare-Event Amplification and Bidirectional Pairing Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-16T08:27:36.817013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cb29fe4b-54ac-46ad-98ed-3658784c21f7 · inbound

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

On the Emergence of Implicit Curriculum in RLVR Learning Dynamics Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-02T23:11:56.603670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35e95cbf-e28e-4198-8946-86cd7942f5d0 · inbound

LLM Reasoning with Process Rewards for Outcome-Guided Steps cites this paper.

LLM Reasoning with Process Rewards for Outcome-Guided Steps Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-16T06:47:28.370284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 35d66e6c-ec7c-4bb5-8dac-5e3c9cdfb688 · inbound

OmniJigsaw: Enhancing Omni-Modal Reasoning via Modality-Orchestrated Reordering cites this paper.

OmniJigsaw: Enhancing Omni-Modal Reasoning via Modality-Orchestrated Reordering Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b3a50fc4-141d-4be0-b4d1-641d27cead18 · inbound

EvoRAG: Making Knowledge Graph-based RAG Automatically Evolve through Feedback-driven Backpropagation cites this paper.

EvoRAG: Making Knowledge Graph-based RAG Automatically Evolve through Feedback-driven Backpropagation Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c07c4949-cf9b-460e-8ba6-b2caae029302 · inbound

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

HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 59

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f85758cb-421a-4647-89f9-8b07480f7489 · inbound

Infection-Reasoner: A Compact Vision-Language Model for Wound Infection Classification with Evidence-Grounded Clinical Reasoning cites this paper.

Infection-Reasoner: A Compact Vision-Language Model for Wound Infection Classification with Evidence-Grounded Clinical Reasoning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b790f4ce-5587-4045-9221-7d056d9ed2d5 · inbound

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

Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6fdd3abf-f46e-40fa-bd5e-85ade0fe9de3 · inbound

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

Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-20T23:49:14.924649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fadce319-d934-46f0-8fd7-16be1facb75e · inbound

Cost-Aware Learning cites this paper.

Cost-Aware Learning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5bc1f85a-1970-4c0b-81f5-2f27ea2b4094 · inbound

Selector-Guided Autonomous Curriculum for One-Shot Reinforcement Learning from Verifiable Rewards cites this paper.

Selector-Guided Autonomous Curriculum for One-Shot Reinforcement Learning from Verifiable Rewards Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ff2b9387-b00b-4e4b-8b53-46ad76ec21e7 · inbound

Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning cites this paper.

Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 124

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1358d144-2135-42ce-bcdf-bbe2578bd8eb · inbound

Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning cites this paper.

Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3dce258f-0d01-4839-8fc7-223aaee4a1a6 · inbound

Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning cites this paper.

Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7228c3f8-c4ae-447c-b9cb-859a9fdd0000 · inbound

Beyond Negative Rollouts: Positive-Only Policy Optimization with Implicit Negative Gradients cites this paper.

Beyond Negative Rollouts: Positive-Only Policy Optimization with Implicit Negative Gradients Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 58

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f3e41791-5ee7-48b4-83ad-651960f75e7f · inbound

Gradient Extrapolation-Based Policy Optimization cites this paper.

Gradient Extrapolation-Based Policy Optimization Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ecfe04e5-a551-4d23-b476-380bd5e7b7c8 · inbound

Experience Sharing in Mutual Reinforcement Learning for Heterogeneous Language Models cites this paper.

Experience Sharing in Mutual Reinforcement Learning for Heterogeneous Language Models Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 93

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5791f443-db00-45ab-a72f-23b3cb693270 · inbound

HTPO: Towards Exploration-Exploitation Balanced Policy Optimization via Hierarchical Token-level Objective Control cites this paper.

HTPO: Towards Exploration-Exploitation Balanced Policy Optimization via Hierarchical Token-level Objective Control Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 11f12a6c-80c8-4004-8747-c40e4f230b73 · inbound

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems cites this paper.

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 153

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 50a71dc9-cb22-4650-8e27-9eb12bd1e9a5 · inbound

Forge: Quality-Aware Reinforcement Learning for NP-Hard Optimization in LLMs cites this paper.

Forge: Quality-Aware Reinforcement Learning for NP-Hard Optimization in LLMs Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0429e0cc-5908-40f8-bcef-8d0ec3ef85da · inbound

Taming Extreme Tokens: Covariance-Aware GRPO with Gaussian-Kernel Advantage Reweighting cites this paper.

Taming Extreme Tokens: Covariance-Aware GRPO with Gaussian-Kernel Advantage Reweighting Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:51:05.238288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation faf44c2e-2dfa-47c6-b0b4-10407353a272 · inbound

Reasoning Can Be Restored by Correcting a Few Decision Tokens cites this paper.

Reasoning Can Be Restored by Correcting a Few Decision Tokens Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-19T20:57:46.832471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3eb97e40-a127-4330-bf81-4d1738208df2 · inbound

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning cites this paper.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-21T08:14:03.324992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b90a97b5-82a6-4100-81b2-cd3ab7c3b356 · inbound

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning cites this paper.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.284952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5c28d3c4-e146-47c6-a316-0e2d7420c068 · inbound

Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning cites this paper.

Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-22T05:44:38.431639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bdfe8046-8767-4940-bd26-4f8f068069a7 · inbound

Hide to Guide: Learning via Semantic Masking cites this paper.

Hide to Guide: Learning via Semantic Masking Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-06-30T12:34:39.101921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e13180f9-a5d3-4ef0-889f-94b394a5a387 · inbound

Mechanistically Interpreting the Role of Sample Difficulty in RLVR for LLMs cites this paper.

Mechanistically Interpreting the Role of Sample Difficulty in RLVR for LLMs Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-06-29T12:13:26.565970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 56b0ad6b-cd6a-47cc-877f-7df9ddc22f11 · inbound

On the Generalization Gap in Self-Evolving Language Model Reasoning cites this paper.

On the Generalization Gap in Self-Evolving Language Model Reasoning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 36

Resolution: metadata mismatch; local_arxiv, observed 2026-06-28T17:22:24.111531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d4bb9c7f-1c18-4bd3-8f4c-0b2b03593d09 · inbound

Trust Region On-Policy Distillation cites this paper.

Trust Region On-Policy Distillation Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 66

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T20:56:13.476262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dc009b3b-49f5-433c-b105-9600622c3361 · inbound

TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning cites this paper.

TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 37

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T04:27:36.741801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 37259542-0c6b-45a4-8074-17d174f934df · inbound

Verifiable Environments Are LEGO Bricks: Recursive Composition for Reasoning Generalization cites this paper.

Verifiable Environments Are LEGO Bricks: Recursive Composition for Reasoning Generalization Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T10:27:56.834469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8f495052-3063-4e91-a5b3-fbd4725fbd91 · inbound

Select and Improve: Understanding the Mechanics of Post-Training for Reasoning cites this paper.

Select and Improve: Understanding the Mechanics of Post-Training for Reasoning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:08:21.748795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f1bbcac1-8a10-4861-89e9-2b8793c6b517 · inbound

How Post-Training Shapes Biological Reasoning Models cites this paper.

How Post-Training Shapes Biological Reasoning Models Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-01T07:55:31.008268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ca75886f-c9a8-4ae9-a44c-325a75358f3e · inbound

Continual Self-Improvement with Lightweight Experiential Latent Memories cites this paper.

Continual Self-Improvement with Lightweight Experiential Latent Memories Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-03T19:18:54.805783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 29db8f2f-1ba7-4855-8206-960c5dcf44e1 · inbound

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning cites this paper.

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 43

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T20:38:55.987264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 92c70472-23b5-4c9f-a708-67a086483e8f · inbound

Provable Benefits of RLVR over SFT for Reasoning Models: Learning to Backtrack Efficiently cites this paper.

Provable Benefits of RLVR over SFT for Reasoning Models: Learning to Backtrack Efficiently Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T10:09:44.762957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2dabf5c4-f7db-4e0c-9067-85ec7897adfa · inbound

Prompt engineering using order-of-addition experiments: An application to generating two-level fractional factorial designs cites this paper.

Prompt engineering using order-of-addition experiments: An application to generating two-level fractional factorial designs Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-11T06:09:35.110633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b01ef1b-9cf8-4dbf-9158-8d55ba54e72a · inbound

RLVP: Penalize the Path, Reward the Outcome cites this paper.

RLVP: Penalize the Path, Reward the Outcome Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:16:11.387942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e762360f-4ca1-45cd-88a9-6af9be4ffd7b · inbound

TraversRL: Traversable Pedestrian Pathway Generation With Reinforcement Learning cites this paper.

TraversRL: Traversable Pedestrian Pathway Generation With Reinforcement Learning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T17:54:29.121056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9b913fd-6f47-4257-9f9b-58bbc8cb07c8 · inbound

Early Verdicts, Better Budgets: Sequential Adaptive Rollout Allocation for Compute-Efficient RLVR cites this paper.

Early Verdicts, Better Budgets: Sequential Adaptive Rollout Allocation for Compute-Efficient RLVR Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 1945

Resolution: unresolved; no resolver link, observed 2026-08-01T00:29:00.474113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5beffa7d-7656-4943-b9ae-fb73df30609f · inbound

SVR: Self-Verifying Refinement via Joint Verdict-Confidence Reinforcement Learning for Adaptive Test-Time Compute cites this paper.

SVR: Self-Verifying Refinement via Joint Verdict-Confidence Reinforcement Learning for Adaptive Test-Time Compute Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-31T06:48:41.337255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.