Paper Citation Record · LEDGER

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning

As of 18 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 8 inbound Pith citation observations for arXiv:2505.14625.

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

pith.paper-citation-record.v1
2505.14625 v2

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:36:54.555335Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:43:00.923422Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:16:26.183764Z

Reference resolution

48 of 48 outbound references displayed

verified exact0
verified fuzzy13
unresolved35
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 56aaa975-7b28-424b-8d22-68df808f6885 · outbound

This paper cites Big-math: A large-scale, high-quality math dataset for reinforcement learning in language models, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Big-math: A large-scale, high-quality math dataset for reinforcement learning in language models, 2025

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:49.050333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa829132-5b02-4f00-813a-89c78d605902 · outbound

This paper cites Online difficulty filtering for reasoning oriented reinforcement learning.arXiv preprint arXiv:2504.03380, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Online difficulty filtering for reasoning oriented reinforcement learning.arXiv preprint arXiv:2504.03380, 2025

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:49.101516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7eb3a90b-efdb-4823-a7f5-f85a8f59ef02 · outbound

This paper cites Robotxr1: Enabling embodied robotic intelligence on large language models through closed-loop reinforcement learning, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Robotxr1: Enabling embodied robotic intelligence on large language models through closed-loop reinforcement learning, 2025

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:59.677826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 93031f66-d74d-4699-b9ff-c2290a75f510 · outbound

This paper cites xverify: Efficient answer verifier for reasoning model evaluations, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning xverify: Efficient answer verifier for reasoning model evaluations, 2025

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:59.384896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d5a7cf2f-c2a0-414e-b39f-ef90567747a0 · outbound

This paper cites Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:49.317584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a93a0685-831e-4c90-9a01-795280ab935d · outbound

This paper cites Opencompass: A universal evaluation platform for foundation models.https://github.com/open-compass/opencompass, 2023.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Opencompass: A universal evaluation platform for foundation models.https://github.com/open-compass/opencompass, 2023

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:49.383928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9bafb76-c51d-45d4-a77c-563882fa8f9b · outbound

This paper cites Process Reinforcement through Implicit Rewards.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Process Reinforcement through Implicit Rewards

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:49.435775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 668db233-568d-4c5e-b3dc-0008c8606ebf · outbound

This paper cites an unresolved cited work.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:36:59.080504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b9eaf3c8-7c5f-47db-8983-e4f81e3053ce · outbound

This paper cites Raft: Reward ranked finetuning for generative foundation model alignment, 2023.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Raft: Reward ranked finetuning for generative foundation model alignment, 2023

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:49.637551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 106e2374-3c76-46a7-a2d8-63ff106d590a · outbound

This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:49.712042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e04a842c-6fb0-46ca-8e4e-5fb1c67cd60f · outbound

This paper cites The language model evaluation harness, 07 2024.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning The language model evaluation harness, 07 2024

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:49.802501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc96dede-980f-400e-a3bb-297c8eed8dbe · outbound

This paper cites A survey on llm-as-a-judge, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning A survey on llm-as-a-judge, 2025

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:58.719702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c20689a6-cd8b-46be-93a5-e00c5012ee25 · outbound

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

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:49.951924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6eebf7f-88ce-4155-a7f8-24bba27364bf · outbound

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

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:50.028195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de83242f-d66c-4d96-b460-91f78ea5548c · outbound

This paper cites Ultraeval: A lightweight platform for flexible and comprehensive evaluation for llms, 2024.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Ultraeval: A lightweight platform for flexible and comprehensive evaluation for llms, 2024

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:58.400339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 78210343-cbd8-436f-822d-24ac91b47ace · outbound

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

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Measuring Mathematical Problem Solving With the MATH Dataset

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:50.175071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b835f02-d85b-4a76-97db-17fb968707ef · outbound

This paper cites Putting rl back in rlhf.https://huggingface.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Putting rl back in rlhf.https://huggingface

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:58.082900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9218d86e-64e4-4a9a-bb18-ab94f77c0901 · outbound

This paper cites Math-Verify: A robust mathematical expression evaluation system.https: //github.com/huggingface/Math-Verify, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Math-Verify: A robust mathematical expression evaluation system.https: //github.com/huggingface/Math-Verify, 2025

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:57.736623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a5eab1cc-2a3e-4122-98b2-6576af50da68 · outbound

This paper cites OpenAI o1 System Card.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning OpenAI o1 System Card

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:50.434833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c30b3d3b-ea42-4ad3-85a2-3bff7c4554b5 · outbound

This paper cites Med-r1: Reinforcement learning for generalizable medical reasoning in vision-language models, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Med-r1: Reinforcement learning for generalizable medical reasoning in vision-language models, 2025

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:50.521407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d7f196a-3c6e-4d18-8406-31549108e161 · outbound

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

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:50.636962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e549583-4066-48f7-ae5a-391be89490b8 · outbound

This paper cites From generation to judgment: Opportunities and challenges of llm-as-a-judge, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning From generation to judgment: Opportunities and challenges of llm-as-a-judge, 2025

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:57.431870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 13bb5dbb-8698-4c18-b1f0-1141c77c40d6 · outbound

This paper cites From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:50.844232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4aad93c-cd18-434c-a46d-a22d024f05c5 · outbound

This paper cites Hashimoto.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Hashimoto

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:57.166159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 415006a0-c6c1-4439-971e-a4c733be84f6 · outbound

This paper cites Wildbench: Benchmarking llms with challenging tasks from real users in the wild, 2024.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Wildbench: Benchmarking llms with challenging tasks from real users in the wild, 2024

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:51.077787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42cc20b7-78f1-4cc5-8994-39500db0b961 · outbound

This paper cites Deepscaler: Surpassing o1-preview with a 1.5 b model by scaling rl.Notion Blog, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Deepscaler: Surpassing o1-preview with a 1.5 b model by scaling rl.Notion Blog, 2025

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:51.220864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a18d1b64-2272-46c8-b00f-40b5de843138 · outbound

This paper cites General- reasoner: Advancing llm reasoning across all domains, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning General- reasoner: Advancing llm reasoning across all domains, 2025

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:56.875421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7f77a618-2304-42ac-a8eb-dff87f7c1c3d · outbound

This paper cites Reinforcement learning with verifiable rewards: Grpo’s effective loss, dy- namics, and success amplification, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Reinforcement learning with verifiable rewards: Grpo’s effective loss, dy- namics, and success amplification, 2025

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:56.563907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation caee832f-c2f3-47a8-800d-a9ac97acbc47 · outbound

This paper cites s1: Simple test-time scaling, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning s1: Simple test-time scaling, 2025

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:51.687694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6203986-d348-4610-8853-308b98f5ad0d · outbound

This paper cites OpenAI Evals: A framework for evaluating llms.https://github.com/openai/ evals, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning OpenAI Evals: A framework for evaluating llms.https://github.com/openai/ evals, 2025

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:56.331189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a4639574-c2f7-41c9-b344-6d1c7e8c1cec · outbound

This paper cites Manning, and Chelsea Finn.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Manning, and Chelsea Finn

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:51.982792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3774910b-089f-4701-b8b2-f7d78db42b96 · outbound

This paper cites Proximal policy optimization algorithms, 2017.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Proximal policy optimization algorithms, 2017

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:52.128786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d82b86b-711b-4e51-8e57-d8c78ff1ee60 · outbound

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

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:52.262883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e882606-ace1-4a33-81e1-dc723e8a65e6 · outbound

This paper cites Hybridflow: A flexible and efficient rlhf framework.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Hybridflow: A flexible and efficient rlhf framework

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:52.386742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acda1c18-7e8d-4349-b20a-cbfe59c44214 · outbound

This paper cites SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:52.514084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a579662-71a9-4b86-abed-e584b3ee26b8 · outbound

This paper cites DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:52.625163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 988bea9c-818f-4c1a-9c72-eddf547bbe1f · outbound

This paper cites A minimalist approach to llm reasoning: from rejection sampling to reinforce, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning A minimalist approach to llm reasoning: from rejection sampling to reinforce, 2025

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:52.715159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8f4181c-2265-483b-8555-2bfc748af162 · outbound

This paper cites Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:52.916366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bb1a502-39cc-45e2-8769-3041624abbf4 · outbound

This paper cites Qwen2.5-math technical report: Toward mathematical expert model via self-improvement, 2024.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Qwen2.5-math technical report: Toward mathematical expert model via self-improvement, 2024

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:53.097735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31866f05-745b-428c-85b5-d350a798a339 · outbound

This paper cites Limo: Less is more for reasoning, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Limo: Less is more for reasoning, 2025

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:53.220799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c960e078-d3cb-4cf8-a455-d4dc2fb840e4 · outbound

This paper cites Dapo: An open-source llm reinforcement learning system at scale, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Dapo: An open-source llm reinforcement learning system at scale, 2025

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:53.453264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f78eea0f-e438-4e9a-bad1-a1df752bc050 · outbound

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

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:53.603007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70534c50-af02-4c81-ae12-951af34c4ea8 · outbound

This paper cites Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild, 2025.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild, 2025

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:53.787633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65631411-b382-47d5-9dff-0ec989593bd8 · outbound

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

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:53.974048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d0567c8-dec6-4884-aa07-53b613d8ee30 · outbound

This paper cites LLM as a judge.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning LLM as a judge

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:56.054865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9bed4c2a-33a8-443c-b0df-f3edfdac91f6 · outbound

This paper cites an unresolved cited work.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:36:55.783306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a1be238-ee3e-4a3c-a577-be0e0032c2c9 · outbound

This paper cites an unresolved cited work.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:36:55.515728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1413de55-c728-4406-9901-d4215721e8a8 · outbound

This paper cites reasoning.

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning reasoning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:55.233979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 3e48e768-8302-413e-96bd-f89841ee7373 · inbound

Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers cites this paper.

Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:40:28.721581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 42f34f68-8adb-4ba9-baf8-69569e51f279 · inbound

Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR cites this paper.

Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:45:53.117674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c04c54cf-8883-403e-b34a-b417301c2bce · inbound

High-Dimensional Statistics: Reflections on Progress and Open Problems cites this paper.

High-Dimensional Statistics: Reflections on Progress and Open Problems TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning

Reference 105

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:36:06.803837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 84e5ac04-f9fb-47ee-a91c-3ba5aa9b5650 · inbound

High-Dimensional Statistics: Reflections on Progress and Open Problems cites this paper.

High-Dimensional Statistics: Reflections on Progress and Open Problems TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning

Reference 105

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:15:46.717605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ae651858-8e70-472e-9370-c824e5cd6fd5 · inbound

Trust Region On-Policy Distillation cites this paper.

Trust Region On-Policy Distillation TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning

Reference 187

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T20:46:14.318017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 607992f7-4e8e-4307-abe4-9675df8e705b · inbound

Trading Human Curation for Synthetic Augmentation in RLVR cites this paper.

Trading Human Curation for Synthetic Augmentation in RLVR TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:16:26.185430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4b0a04ab-7d48-4726-979c-99ad29cd69c5 · inbound

Trading Human Curation for Synthetic Augmentation in RLVR cites this paper.

Trading Human Curation for Synthetic Augmentation in RLVR TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-12T15:15:09.675778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcb0a385-ed47-474e-88e8-9680e753f793 · inbound

Judging Is Not Enumerating: Silent Omissions in LLM-Authored Acceptable Sets cites this paper.

Judging Is Not Enumerating: Silent Omissions in LLM-Authored Acceptable Sets TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T00:43:00.923422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.