Paper Citation Record · LEDGER

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization

As of 8 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 3 inbound Pith citation observations for arXiv:2507.06573.

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

pith.paper-citation-record.v1
2507.06573 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:07:36.826395Z

measured 16 of 16 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T14:08:40.968105Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T14:13:30.120402Z

Reference resolution

13 of 13 outbound references displayed

verified exact0
verified fuzzy3
unresolved9
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ef897c4f-df5c-408d-a1dd-cdceafedd80a · outbound

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

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:35.795001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 847c084f-a9ab-43c3-aebb-fc2e12b2151b · outbound

This paper cites In The Twelfth In- ternational Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024.

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization In The Twelfth In- ternational Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:07:37.413613Z

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 b2e1df28-db64-4825-b65a-54c5fb249698 · outbound

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

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization Understanding R1-Zero-Like Training: A Critical Perspective

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:36.278237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48c950b9-6541-4e7b-8811-f18eff5a7c80 · outbound

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

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:36.432526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0489ca83-0458-43a3-a87a-bb07e55e015d · outbound

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

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:36.534922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ee80aa1-e4b6-4d90-ba9b-0c678caca20c · outbound

This paper cites SRPO: A Cross-Domain Implementation of Large-Scale Reinforcement Learning on LLM.

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization SRPO: A Cross-Domain Implementation of Large-Scale Reinforcement Learning on LLM

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:36.624737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06880c01-66ea-45a5-bfc6-f2f41724fff8 · outbound

This paper cites an unresolved cited work.

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization Unresolved cited work

Reference 12

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T19:07:37.268626Z

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 35635517-f411-4aa6-8762-66ed6ee0b8c0 · outbound

This paper cites Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V.

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V

Reference 626

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:07:37.555442Z

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 e9f16861-1523-4116-9bcc-19502952b717 · outbound

This paper cites Figure 8 illustrates the cosine similarity between the embeddings of model-generated solutions and oracle expert solutions, comparing training with and without PG-Sampling.

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization Figure 8 illustrates the cosine similarity between the embeddings of model-generated solutions and oracle expert solutions, comparing training with and without PG-Sampling

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:07:37.139709Z

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 fbab5568-6cdb-48f6-9369-0112cd8020ae · outbound

This paper cites Proximal Policy Optimization Algorithms.

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization Proximal Policy Optimization Algorithms

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:36.344889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b894d50c-c028-4d0c-9c0d-fb4703238dab · outbound

This paper cites LIMR: Less is More for RL Scaling.

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization LIMR: Less is More for RL Scaling

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:36.105216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 219b8998-dcc0-4f6c-8368-7323e81837a5 · outbound

This paper cites Process Reinforcement through Implicit Rewards.

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization Process Reinforcement through Implicit Rewards

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:35.753972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f738fbeb-dc33-4e5b-9782-9a22379c6465 · outbound

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

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization Large-Scale Data Selection for Instruction Tuning

Reference 9061

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:35.904174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 23659746-3e51-4ba5-b8ca-8aac15e366d7 · inbound

SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning cites this paper.

SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:28:05.756861Z

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 988c29da-eaf5-464d-b870-5250a0180a72 · inbound

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

Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:15:49.338226Z

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 ba1428bf-2956-452f-bdb3-71112283d2dd · inbound

Single-Rollout Hidden-State Dynamics for Training-Free RLVR Data Selection cites this paper.

Single-Rollout Hidden-State Dynamics for Training-Free RLVR Data Selection From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:13:30.122157Z

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.