Paper Citation Record · LEDGER

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

As of 5 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 44 inbound Pith citation observations for arXiv:2508.10751.

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

pith.paper-citation-record.v1
2508.10751 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T20:21:08.269218Z

measured 105 of 105 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 44 of 44 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:03:28.124820Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T21:00:08.457909Z

Reference resolution

61 of 61 outbound references displayed

verified exact0
verified fuzzy12
unresolved47
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 20fe78a0-c3b7-4dba-9e54-00d8c30a84d5 · outbound

This paper cites L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:04.023052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4f65368-aa33-417e-a8cc-39db8d830f48 · outbound

This paper cites Aime2024, 2024.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Aime2024, 2024

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:21:09.639070Z

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 311c3a56-09ae-41db-bcc8-c81911226bee · outbound

This paper cites Aime2025, 2025.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Aime2025, 2025

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:21:09.624512Z

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 62f72c5a-6235-4654-bc26-fe632c7dce51 · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models A Survey of Exploration Methods in Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:04.419668Z

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Observation e7c1322f-379a-4b7f-a5f3-bdd4942d70a7 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:04.577447Z

Source-reported events for the cited work

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Observation 3343a24b-c34c-4c73-b8dd-5dcf52e709bf · outbound

This paper cites an unresolved cited work.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-05T20:21:09.609052Z

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 fd4291e8-8156-4eed-a22b-59ee16b57e10 · outbound

This paper cites Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:04.915607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42cc8885-3790-443d-b3e1-dab8a68371a9 · outbound

This paper cites Improving large language models via fine-grained reinforcement learning with minimum editing constraint.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Improving large language models via fine-grained reinforcement learning with minimum editing constraint

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:21:09.593888Z

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 9a097a0b-09ff-4ebf-8733-eafab4a5893d · outbound

This paper cites An Empirical Study on Eliciting and Improving R1-like Reasoning Models.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models An Empirical Study on Eliciting and Improving R1-like Reasoning Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:05.350521Z

Source-reported events for the cited work

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Observation 99805c56-6e20-4578-aacb-2b4bb2e1fcf4 · outbound

This paper cites Reasoning with Exploration: An Entropy Perspective.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Reasoning with Exploration: An Entropy Perspective

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:05.510301Z

Source-reported events for the cited work

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Observation b7a6e9fb-9cdc-4e8f-81d3-5e95ae19aa19 · outbound

This paper cites Thinker: Learning to think fast and slow.CoRR, abs/2505.21097, 2025.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Thinker: Learning to think fast and slow.CoRR, abs/2505.21097, 2025

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:05.724526Z

Source-reported events for the cited work

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Observation 1139fa17-a670-4e6a-8954-5c8efaa7a883 · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:05.883163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d842488-652c-4c9c-8f1b-b8d7477819f7 · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:06.013467Z

Source-reported events for the cited work

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Observation 6c23da36-7ce8-4391-813e-3aa311c32bc2 · outbound

This paper cites The Llama 3 Herd of Models.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models The Llama 3 Herd of Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:06.145355Z

Source-reported events for the cited work

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Observation c0c2e054-71b3-4084-bd1a-8c9069851b83 · outbound

This paper cites Stochastic first- and zeroth-order methods for nonconvex stochastic program- ming.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Stochastic first- and zeroth-order methods for nonconvex stochastic program- ming

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:21:09.579100Z

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 7084e6e2-2365-41dd-a044-67fafd312f2b · outbound

This paper cites Bootstrap resampling methods: something for nothing?The Annals of thoracic surgery, 77(4):1142–1144, 2004.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Bootstrap resampling methods: something for nothing?The Annals of thoracic surgery, 77(4):1142–1144, 2004

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:21:09.563898Z

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 baf71de0-f6fb-4fa1-b7ac-c21f72141e0a · outbound

This paper cites Seed1.5-VL Technical Report.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Seed1.5-VL Technical Report

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:06.466198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 628887d4-4e15-42f6-bfb4-b9b8d72704ec · outbound

This paper cites Skywork Open Reasoner 1 Technical Report.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Skywork Open Reasoner 1 Technical Report

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:06.606460Z

Source-reported events for the cited work

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Observation 245b89ef-4157-44b5-b6b4-b08d30903107 · outbound

This paper cites GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:06.704021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac01cefd-0396-434f-892a-51412916c5c1 · outbound

This paper cites T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:06.806080Z

Source-reported events for the cited work

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Observation 2e500108-796a-4d7f-aa74-1553e62ba1c5 · outbound

This paper cites Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:06.909124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ee29c13-b5f9-431d-a2c7-d3841c64ad5f · outbound

This paper cites Test-Time Learning for Large Language Models.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Test-Time Learning for Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:07.016294Z

Source-reported events for the cited work

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Observation 3c53f383-d82f-45cc-81e8-90294d83ced7 · outbound

This paper cites OpenAI o1 System Card.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models OpenAI o1 System Card

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:07.122698Z

Source-reported events for the cited work

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Observation b84b9ea5-a56b-42ca-a579-831457015b47 · outbound

This paper cites Mistral 7B.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Mistral 7B

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:07.228054Z

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Observation 280a78ad-13ba-4915-9c95-3bed5a25b21d · outbound

This paper cites PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:07.329389Z

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Observation 51ff1928-5d33-47cd-8da2-fce57a9ac1d9 · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:07.431738Z

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Observation 7e909de1-669d-4dfc-90ad-4dee4fa37e3b · outbound

This paper cites PP-PG: combining parameter perturbation with policy gradient methods for effective and efficient explorations in deep reinforcement learning.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models PP-PG: combining parameter perturbation with policy gradient methods for effective and efficient explorations in deep reinforcement learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:21:09.548074Z

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 de686465-cf53-4b9e-89c3-2a3fdf6af3de · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:07.634233Z

Source-reported events for the cited work

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Observation 31165b7e-a287-40f2-af57-a8ecee7d2dbb · outbound

This paper cites Trust, But Verify: A Self-Verification Approach to Reinforcement Learning with Verifiable Rewards.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Trust, But Verify: A Self-Verification Approach to Reinforcement Learning with Verifiable Rewards

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:07.730797Z

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Observation d0e5f048-ce60-4b9d-8933-0fe3d1f6116c · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Understanding R1-Zero-Like Training: A Critical Perspective

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:07.843467Z

Source-reported events for the cited work

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Observation 135cdd04-f9ce-4df2-a4c2-2a1b862eefb8 · outbound

This paper cites Inference-time scaling for generalist reward modeling.CoRR, abs/2504.02495, 2025.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Inference-time scaling for generalist reward modeling.CoRR, abs/2504.02495, 2025

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:07.944339Z

Source-reported events for the cited work

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Observation 63245719-83c1-421f-965f-4e7e4baeb7c0 · outbound

This paper cites Learning from Peers in Reasoning Models.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Learning from Peers in Reasoning Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.060154Z

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Observation 9eaa3cc7-49dd-4760-9e89-0b0c97512861 · outbound

This paper cites Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.144794Z

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Observation 21f9c201-1872-4147-bbf2-d17c4396691d · outbound

This paper cites Nesterov and Vladimir G.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Nesterov and Vladimir G

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:21:09.532062Z

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 4c3ddb77-da42-4ce6-a580-5cc87dd8b440 · outbound

This paper cites Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers

Reference 35

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T20:21:08.865359Z

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

Observation 66f0cbd1-9156-4f7c-be0c-512a26e701f1 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Proximal Policy Optimization Algorithms

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.159316Z

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Observation 2dc96f34-873c-4b75-ae35-482082edbde9 · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.163389Z

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Observation 06d7b561-f235-48b0-80e6-e4599b64f9c2 · outbound

This paper cites Spurious Rewards: Rethinking Training Signals in RLVR.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Spurious Rewards: Rethinking Training Signals in RLVR

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.167577Z

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Observation 0d6b4713-7f46-4386-95c7-6c6a7dfa789f · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.171925Z

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Observation 02db5137-7dcd-4074-b5f8-5b25a7ef2719 · outbound

This paper cites Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:21:09.514447Z

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 871c4546-f675-4c08-b313-9a27eaa3a600 · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.180531Z

Source-reported events for the cited work

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Observation 8e997e85-8055-440b-a0ec-1515799f464c · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Optimizing Language Models for Inference Time Objectives using Reinforcement Learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.184900Z

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Observation 628a0a5e-3563-4f67-926e-7662a64adb4c · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.189939Z

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Observation 0d71cc55-8d76-47da-a7e8-3bd0ded1ff70 · outbound

This paper cites Reft: Reasoning with reinforced fine-tuning.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Reft: Reasoning with reinforced fine-tuning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:21:09.495740Z

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 96da9560-e314-4886-91b2-7cde506d392d · outbound

This paper cites Pass@K Policy Optimization: Solving Harder Reinforcement Learning Problems.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Pass@K Policy Optimization: Solving Harder Reinforcement Learning Problems

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.198160Z

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Observation 46f7412a-5d75-498b-b50c-d766cf13e929 · outbound

This paper cites Math-shepherd: Verify and reinforce llms step-by-step without human annotations.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Math-shepherd: Verify and reinforce llms step-by-step without human annotations

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:21:09.479040Z

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 8c9b5026-2071-446b-9b77-2a7369af0836 · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.206755Z

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Observation 8bbbfd40-7616-42d9-8616-39e8c6566b31 · outbound

This paper cites Williams.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Williams

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:21:09.462263Z

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 3e5fc915-e57f-440b-96a8-326d3280e5a2 · outbound

This paper cites ARM: adaptive reasoning model.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models ARM: adaptive reasoning model

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.215587Z

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Observation 2b52b9ed-e82c-42e1-bdb1-a84c211552fe · outbound

This paper cites Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.221916Z

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Observation e394acda-e293-45be-98a7-f889ebaef45f · outbound

This paper cites Qwen2.5 Technical Report.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Qwen2.5 Technical Report

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.226479Z

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Observation fe7ddb09-536d-440d-9862-378a9c939715 · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.230632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69c396b7-6c36-4336-b590-bd8dffcba871 · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.234662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de3cb2e2-c7c8-4fea-b012-22ad3ae2bae9 · outbound

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

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.239014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d6ef5ab-efdf-4d0c-b7e4-1c4240bdf064 · outbound

This paper cites Boning, and Dina Katabi.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Boning, and Dina Katabi

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.243328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcac152f-aad7-4d74-a694-d4bd78ebca09 · outbound

This paper cites Zeroth-order policy gradient for reinforcement learning from human feedback without reward inference.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Zeroth-order policy gradient for reinforcement learning from human feedback without reward inference

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:21:09.445445Z

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 635baa46-0aa2-4c0c-83e7-d034e1aa98a7 · outbound

This paper cites A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.251549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af82a7c5-9336-4f8c-a046-38fcc0f9ade4 · outbound

This paper cites OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.255765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7303c30-23fe-4d3d-afb2-afc9e91ecb71 · outbound

This paper cites The surprising effectiveness of negative reinforcement in LLM reasoning.CoRR, abs/2506.01347, 2025.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models The surprising effectiveness of negative reinforcement in LLM reasoning.CoRR, abs/2506.01347, 2025

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T20:21:08.260188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daf99f1d-3ef1-47e9-b666-abb418f71612 · outbound

This paper cites an unresolved cited work.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-05T20:21:09.429269Z

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 fac393c8-196a-40fc-aff7-6f40df93604c · outbound

This paper cites TTRL: Test-Time Reinforcement Learning.

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models TTRL: Test-Time Reinforcement Learning

Reference 61

Resolution: malformed identifier; no resolver link, observed 2026-08-05T20:21:08.269218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c40f1373-96d7-47d6-bf88-a85fa7fb7c7d · inbound

From Trial-and-Error to Improvement: A Systematic Analysis of LLM Exploration Mechanisms in RLVR cites this paper.

From Trial-and-Error to Improvement: A Systematic Analysis of LLM Exploration Mechanisms in RLVR Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T22:03:28.124820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 799af097-4862-4a75-989d-849b0b503152 · inbound

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey cites this paper.

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:21:48.591192Z

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 d36f566e-f01d-46dc-9179-90ccd70433a4 · inbound

Outcome-based Exploration for LLM Reasoning cites this paper.

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45b8b181-2879-4fdd-9eca-4e0f414cdbfa · inbound

A Survey of Reinforcement Learning for Large Reasoning Models cites this paper.

A Survey of Reinforcement Learning for Large Reasoning Models Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:02:25.160513Z

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 88eb6109-4516-431f-a947-b6785bebb238 · inbound

Emergent Slow Thinking in LLMs as Inverse Tree Freezing cites this paper.

Emergent Slow Thinking in LLMs as Inverse Tree Freezing Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:46:24.288452Z

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 191f6cdb-d00d-4350-822a-e95904af13b6 · 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 Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T11:33:56.981364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce897c27-c9b9-450c-adff-e09bdc6df006 · inbound

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

Beyond the Sampled Token: Preserving Candidate Support in RLVR Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T09:34:06.058912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0672f5f-5a20-4c0a-a0c0-18135e3042d1 · inbound

Towards Generalizable Reasoning: Group Causal Counterfactual Policy Optimization for LLM Reasoning cites this paper.

Towards Generalizable Reasoning: Group Causal Counterfactual Policy Optimization for LLM Reasoning Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:22:31.160267Z

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 365887bf-2e01-45cf-b80d-f1c14d8774aa · inbound

Compress the Easy, Explore the Hard: Difficulty-Aware Entropy Regularization for Efficient LLM Reasoning cites this paper.

Compress the Easy, Explore the Hard: Difficulty-Aware Entropy Regularization for Efficient LLM Reasoning Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T20:44:43.950446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad4def12-7ced-403f-bfb8-dd4bfafe21ec · inbound

From $P(y|x)$ to $P(y)$: Investigating Reinforcement Learning in Pre-train Space cites this paper.

From $P(y|x)$ to $P(y)$: Investigating Reinforcement Learning in Pre-train Space Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:41:03.928079Z

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 493c6899-d3c8-491a-8f31-7eff9bddad66 · inbound

MCPO: Mastery-Consolidated Policy Optimization for Large Reasoning Models cites this paper.

MCPO: Mastery-Consolidated Policy Optimization for Large Reasoning Models Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:06:52.810362Z

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 ffb3f9a7-bae6-4387-a39d-f03a74925aa4 · inbound

SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models cites this paper.

SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:01:49.470700Z

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 6f9a438e-d767-480b-9e90-66e45d68f524 · inbound

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

Uniform-Correct Policy Optimization: Breaking RLVR's Indifference to Diversity Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:26:15.655822Z

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 3a3e0fe9-f636-46dd-a3e8-249b9504625c · inbound

ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning cites this paper.

ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:41:46.295094Z

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 51162d63-f47d-4295-aad7-d13027ce7f2f · inbound

ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning cites this paper.

ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:55:56.520021Z

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 1b4bac14-8d4b-4e1b-99f9-62665a56480d · inbound

On the Implicit Reward Overfitting and the Low-rank Dynamics in RLVR cites this paper.

On the Implicit Reward Overfitting and the Low-rank Dynamics in RLVR Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:06:10.050125Z

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 daa2a35e-d39a-4ae5-b48f-d4d49bffec98 · inbound

PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents cites this paper.

PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:00:56.299422Z

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 b2396714-c593-4147-b06c-da2776362bf8 · inbound

How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors cites this paper.

How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:26:19.271730Z

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 a6579577-05d4-4a44-a098-bf48c50f0bf6 · 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 Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:21:22.408581Z

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 ed419b70-ffc5-4eae-bf39-c060302b0ee9 · inbound

Breaking $\textit{Winner-Takes-All}$: Cooperative Policy Optimization Improves Diverse LLM Reasoning cites this paper.

Breaking $\textit{Winner-Takes-All}$: Cooperative Policy Optimization Improves Diverse LLM Reasoning Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:07:09.183624Z

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 4efd0409-3d61-4fff-90f7-0358f16c0b19 · inbound

Breaking $\textit{Winner-Takes-All}$: Cooperative Policy Optimization Improves Diverse LLM Reasoning cites this paper.

Breaking $\textit{Winner-Takes-All}$: Cooperative Policy Optimization Improves Diverse LLM Reasoning Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:49:10.563663Z

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 9a1a4a03-0132-4c2f-b4ba-3a3c836b1e46 · inbound

SAGE: Shaping Anchors for Guided Exploration in RLVR of LLMs cites this paper.

SAGE: Shaping Anchors for Guided Exploration in RLVR of LLMs Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T20:13:44.104548Z

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 f26dcbd3-d925-49b1-91fb-13e1c3be1936 · inbound

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

Beyond Mode Collapse: Distribution Matching for Diverse Reasoning Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:33:04.068835Z

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 dfe95361-ed25-42cd-8246-efab17eaac41 · inbound

Finite-Time Regret Analysis of Retry-Aware Bandits cites this paper.

Finite-Time Regret Analysis of Retry-Aware Bandits Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:03:59.249766Z

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 f35f1a59-919b-493e-b88b-69a2bfa9def1 · inbound

Finite-Time Regret Analysis of Retry-Aware Bandits cites this paper.

Finite-Time Regret Analysis of Retry-Aware Bandits Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:34:57.515573Z

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 42e13782-b429-419b-8dc1-0dce22bdd636 · inbound

Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards cites this paper.

Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:59:41.116576Z

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 97047d31-dc3f-44fd-ad7c-ac45de92f9ca · inbound

Residual Skill Optimization for Text-to-SQL Ensembles cites this paper.

Residual Skill Optimization for Text-to-SQL Ensembles Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-22T08:41:17.254306Z

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 f655d136-4f42-443e-b1fa-a80c77dff8a4 · inbound

Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models cites this paper.

Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:03:13.949295Z

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 fc4b7227-bd3d-41aa-811f-d456642b1cf9 · 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 Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:26:27.306210Z

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 19363c93-9a7d-400c-9e42-7115da93686e · inbound

Retry Policy Gradients in Continuous Action Spaces cites this paper.

Retry Policy Gradients in Continuous Action Spaces Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:56:57.024097Z

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 b02f5bbd-79a9-4cf7-8857-b3f6bccb225e · inbound

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

On Advantage Estimates for Max@K Policy Gradients Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:06:56.383237Z

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 742e7457-58dc-4252-90bd-f8328b9c11b5 · 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 Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:16:56.976361Z

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 eeba6203-dd94-4814-ae96-c7756f3bf42c · inbound

SFT Overtraining Predicts Rank Inversion via Entropy Collapse Under RLVR cites this paper.

SFT Overtraining Predicts Rank Inversion via Entropy Collapse Under RLVR Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:38:55.621080Z

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 31da5b42-3f07-437e-aab9-7e9d4d976486 · inbound

REVES: REvision and VErification--Augmented Training for Test-Time Scaling cites this paper.

REVES: REvision and VErification--Augmented Training for Test-Time Scaling Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:19:14.053289Z

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 a43c264f-4835-47fe-934d-7d24b231d5d5 · inbound

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

Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:29:16.717650Z

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 daa031ff-e73d-4e70-88eb-e64ef10d3383 · inbound

On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity cites this paper.

On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T21:00:08.459905Z

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 c3aac177-1822-4da3-9390-aeb872f3e1f0 · inbound

Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RL cites this paper.

Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RL Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:08:57.650057Z

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 65a40f4f-3a54-45ee-94f8-cb0002af75a6 · inbound

DecompRL: Solving Harder Problems by Learning Modular Code Generation cites this paper.

DecompRL: Solving Harder Problems by Learning Modular Code Generation Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:38:39.768877Z

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 735e13ff-2756-48e3-88b5-344b01ad6948 · inbound

Spectral Rewiring for Exploration, Purification, and Model Merging cites this paper.

Spectral Rewiring for Exploration, Purification, and Model Merging Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-12T05:08:55.438431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11a1f182-415f-45d8-a3ec-9b9c8e7a7bd0 · inbound

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning cites this paper.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:36.599379Z

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Observation f3e1b247-a286-4779-8f1e-f44820c54e7c · inbound

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation cites this paper.

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-01T08:41:09.320896Z

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Observation 53d17f62-7644-4900-8a92-e26913548d27 · inbound

TaPR: Test-Aware Policy Refinement for Feedback-Conditioned Code Generation cites this paper.

TaPR: Test-Aware Policy Refinement for Feedback-Conditioned Code Generation Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-05T00:54:52.387697Z

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Observation 1f503e5d-6900-4a9f-9df7-401fa10d48df · inbound

Instruction-Conditioned Exploration with Asymmetric Reinforcement Learning and Self-Distillation cites this paper.

Instruction-Conditioned Exploration with Asymmetric Reinforcement Learning and Self-Distillation Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T15:25:49.330187Z

Source-reported events for the cited work

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Observation 53186b75-65a6-4686-bc35-4da457bbb35e · inbound

Beyond the Mean: Multi-Moment Policy Optimization for LLM Reasoning cites this paper.

Beyond the Mean: Multi-Moment Policy Optimization for LLM Reasoning Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T13:44:40.174330Z

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