Paper Citation Record · LEDGER

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward

As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 7 inbound Pith citation observations for arXiv:2509.01321.

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

pith.paper-citation-record.v1
2509.01321 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:45:41.982954Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T14:49:39.188220Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:09:29.035824Z

Reference resolution

21 of 21 outbound references displayed

verified exact0
verified fuzzy7
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 88bfa3fa-7790-4cb2-a1f6-177f484a5ec9 · outbound

This paper cites This objective aims to select a subset Y that is both diverse (as captured by det(SY)) and influential (as promoted by the product of weights ∏i∈Y wi).

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward This objective aims to select a subset Y that is both diverse (as captured by det(SY)) and influential (as promoted by the product of weights ∏i∈Y wi)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:45:42.198696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5a34c283-42b9-4ecd-8940-9542f172ac93 · outbound

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

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.919862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c075a77f-a5b2-4ff2-bfd0-c722d676654b · outbound

This paper cites Determinantal point processes for machine learning.

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward Determinantal point processes for machine learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.926767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9397d96-dbf1-4a87-929b-45ee2b2021b6 · outbound

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

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.937712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bd7a51a-db29-4631-b72a-09e361584711 · outbound

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

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.944746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1159417-894d-428f-a0b1-3afcd9f06bff · outbound

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

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.948669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9dcc236-a80b-4850-bcbb-1a067acc5307 · outbound

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

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.952290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43746aa5-ec9c-4df8-8e5c-4f0de72a7011 · outbound

This paper cites HARP: A challenging human-annotated math reasoning benchmark.

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward HARP: A challenging human-annotated math reasoning benchmark

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.956023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcf8172e-f661-4089-b7e5-131ca1103dfb · outbound

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

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.959563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e0a894f-8988-445e-8468-2d97eeda04ba · outbound

This paper cites A Survey of Large Language Models.

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward A Survey of Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.962651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48f3f476-4dc4-4040-9d57-7bfa4cf05fbe · outbound

This paper cites We train DeepSeek-R1-Distill-Qwen-7B and DeepSeek-R1-Distill-Llama-8B on 64×H200 GPUs, and Qwen2.5-Math-7B on 32×H200 GPUs.

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward We train DeepSeek-R1-Distill-Qwen-7B and DeepSeek-R1-Distill-Llama-8B on 64×H200 GPUs, and Qwen2.5-Math-7B on 32×H200 GPUs

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:45:42.188191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6e6c5ecd-519d-4fa9-952d-eb28c627c9b0 · outbound

This paper cites We follow Zheng et al.

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward We follow Zheng et al

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:45:42.158494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c3b2bdac-a2c9-44be-987f-c4d4608ca16d · outbound

This paper cites • Random: Randomly samples data from the training set.

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward • Random: Randomly samples data from the training set

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:45:42.148614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ceba9f09-7548-451a-a022-897a36409df8 · outbound

This paper cites Similar to Yue et al.

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward Similar to Yue et al

Reference 256

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:45:42.178162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c3b6a25e-7df7-48b1-9668-7093fca649c4 · outbound

This paper cites Reasoning with Exploration: An Entropy Perspective.

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward Reasoning with Exploration: An Entropy Perspective

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.916058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05558ae2-4940-4aa7-8ec2-355ffabdf59b · outbound

This paper cites User: \n [question] \n Please reason step by step, and put your final answer within \boxed{}. \n \n Assistant:.

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward User: \n [question] \n Please reason step by step, and put your final answer within \boxed{}. \n \n Assistant:

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:45:42.168425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5bda1edf-bfb7-4388-b30d-6e23b180f3e0 · outbound

This paper cites OpenAI o1 System Card.

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward OpenAI o1 System Card

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.923179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2940da6b-fcee-47ff-9612-fdcb03b71617 · outbound

This paper cites LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment.

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.930273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6deb6c8d-754b-4b7d-ab59-6d44bd4c19f8 · outbound

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

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.941078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b113e1de-a27c-4a7e-9a9b-8bb9bd634cc6 · outbound

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

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward Understanding R1-Zero-Like Training: A Critical Perspective

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-05T12:45:41.933948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa6945b6-7936-49d9-b632-7454cdcb75f3 · outbound

This paper cites Zachary Ankner, Cody Blakeney, Kartik Sreenivasan, Max Marion, Matthew L.

Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward Zachary Ankner, Cody Blakeney, Kartik Sreenivasan, Max Marion, Matthew L

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:45:42.208226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation c7f55504-31e6-49ef-94f0-d257b98496ea · inbound

Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning cites this paper.

Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T21:53:35.117871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0017b414-69ab-4557-ba87-51182b5626e4 · inbound

DARE: Difficulty-Adaptive Reinforcement Learning with Co-Evolved Difficulty Estimation cites this paper.

DARE: Difficulty-Adaptive Reinforcement Learning with Co-Evolved Difficulty Estimation Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:19.312129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3a17c0a3-cb51-4d50-becd-eea6adcefb54 · inbound

IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage cites this paper.

IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T14:53:31.190838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 30d0e72f-8d3b-44f4-9bf3-76c60791f499 · inbound

RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning cites this paper.

RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward

Reference 50

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T21:16:13.366003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0d6c5df4-001e-4297-abea-637ad4718b16 · inbound

Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots cites this paper.

Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:26:46.260794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 642a7c29-2225-48ce-a3f3-600d24e1f83a · inbound

Reasoning Arena: Trace Tournaments When Verifiable Rewards Fall Short cites this paper.

Reasoning Arena: Trace Tournaments When Verifiable Rewards Fall Short Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:17:29.544665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c639ed77-de36-4ef0-ae45-59cbff164918 · inbound

Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models cites this paper.

Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:09:29.037593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.