Paper Citation Record · LEDGER

A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 32 inbound Pith citation observations for arXiv:2504.11343.

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

pith.paper-citation-record.v1
2504.11343 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 32 of 32 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 32 of 32 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:38:53.871009Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T04:05:55.456269Z

Reference resolution

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verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f3cfd3a9-54be-4a41-b964-576ef5d6a952 · inbound

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

Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-22T18:46:57.054014Z

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 55773bad-37ca-41b8-a1eb-2acc1865b435 · inbound

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning cites this paper.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.196041Z

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 bb2d36c3-c7c9-4a0d-9b02-8641cb2cc399 · inbound

Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning cites this paper.

Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:41:50.456853Z

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 0c7116e4-8cdc-42c2-9271-48c094d9c972 · inbound

Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training cites this paper.

Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-21T22:40:43.244574Z

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 3a903c09-9f27-48b5-996a-8758fa183abe · inbound

DiffusionNFT: Online Diffusion Reinforcement with Forward Process cites this paper.

DiffusionNFT: Online Diffusion Reinforcement with Forward Process A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:54:30.986262Z

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 9bb53df9-d8d6-4975-bdc3-ab161ec4c708 · inbound

Simple Policy Gradients for Reasoning with Diffusion Language Models cites this paper.

Simple Policy Gradients for Reasoning with Diffusion Language Models A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:53.871009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d40d08e-cbcd-4cba-a805-8b25870ed109 · inbound

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

Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T08:49:34.587109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2e87cb3-0435-455e-8590-bda005b55f76 · inbound

TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework cites this paper.

TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-03T23:33:50.580515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acc3680d-d292-4990-a7da-29ed62b2cacd · inbound

Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control cites this paper.

Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:51:25.636355Z

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 73b77fc9-59fc-469e-91af-c9034311ff2f · inbound

Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control cites this paper.

Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:46:32.380758Z

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 1755dd5f-1fb3-48ea-8ae5-caa2491a1599 · inbound

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

Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:11:28.859103Z

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 630449dd-b80d-4aa3-a288-bfdc3eeaca58 · inbound

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

Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:49:14.948536Z

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 4e3f625f-f911-47dc-913c-9b7283007d79 · inbound

Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning cites this paper.

Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:16:09.087260Z

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 27f16019-f5ab-482c-a3c3-229879c8f308 · inbound

Beyond Uniform Credit Assignment: Selective Eligibility Traces for RLVR cites this paper.

Beyond Uniform Credit Assignment: Selective Eligibility Traces for RLVR A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:36:10.409003Z

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 8904c37e-bae8-43f4-b1f4-e6b1b9576a75 · inbound

Gradient Extrapolation-Based Policy Optimization cites this paper.

Gradient Extrapolation-Based Policy Optimization A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:55:56.827682Z

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 ac7f4f7a-08a9-4ae7-9c10-ac45239fb51b · inbound

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

Experience Sharing in Mutual Reinforcement Learning for Heterogeneous Language Models A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 99

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:00:55.092691Z

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 cffefd4d-5467-42c3-b666-e5c677216762 · inbound

Selective Off-Policy Reference Tuning with Plan Guidance cites this paper.

Selective Off-Policy Reference Tuning with Plan Guidance A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:47:05.243802Z

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 04ef7fec-55a2-469b-b11e-13f85b769347 · inbound

Selective Off-Policy Reference Tuning with Plan Guidance cites this paper.

Selective Off-Policy Reference Tuning with Plan Guidance A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:22:59.403633Z

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 6d45c1f7-88b7-4b72-9a5e-75ef342eb4fc · inbound

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models cites this paper.

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:07:28.062118Z

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 f3cd6cc3-8198-42f8-b0c1-5d701390201b · inbound

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models cites this paper.

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:19:28.199683Z

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 fd58e815-33b8-4c2b-a799-7d7328ae7e9f · 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 A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:59:41.063061Z

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 0aee1525-3fe3-446e-a08f-0a4820de4161 · inbound

ARCA: Adapter-Residual Credit Assignment When Token Signals Degenerate cites this paper.

ARCA: Adapter-Residual Credit Assignment When Token Signals Degenerate A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:16:00.804526Z

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 c20fbf8a-6285-40a2-b777-36c0e8059a36 · inbound

Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection cites this paper.

Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-07-01T23:26:22.844157Z

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 247f0921-085c-40d3-bb1a-5832dc6274cc · inbound

Rollout-Level Advantage-Prioritized Experience Replay for GRPO cites this paper.

Rollout-Level Advantage-Prioritized Experience Replay for GRPO A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-07-02T06:06:41.052519Z

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 92d6a179-4695-4e99-92a4-c596e43b8601 · inbound

AsyncWebRL: Efficient Multi-Step RL for Visual Web Agents cites this paper.

AsyncWebRL: Efficient Multi-Step RL for Visual Web Agents A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:56:55.521293Z

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 842d94d0-100c-48b9-8371-829985f2993b · inbound

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

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 137

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:38:56.067368Z

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 08ff4005-a93c-4e45-97dc-15cb1ad52b5a · inbound

DRIFT: Refining Instruction Data via On-Policy Data Attribution cites this paper.

DRIFT: Refining Instruction Data via On-Policy Data Attribution A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T19:18:54.722039Z

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 7d607ce7-cda3-4fb0-9d51-f107ba4b1af5 · inbound

What are Key Factors for Updates in RL for LLM Reasoning? cites this paper.

What are Key Factors for Updates in RL for LLM Reasoning? A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:09:43.575477Z

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 6299d2f4-a2e8-4d50-99c9-9b112124ab68 · inbound

RL Post-Training Builds Compositional Reasoning Strategies cites this paper.

RL Post-Training Builds Compositional Reasoning Strategies A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-09T04:05:55.457832Z

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 9d906ae0-a2f9-433e-821b-42cf4472e9ba · inbound

Self-Evolving Agent Harnesses via Gated Semantic Quality-Diversity cites this paper.

Self-Evolving Agent Harnesses via Gated Semantic Quality-Diversity A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T04:32:37.522325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b0b7d76-777d-494d-a718-80aaea0fcccf · inbound

It Takes 8 Tokens: Weak-to-Strong Off-Policy RL via Auxiliary Branches cites this paper.

It Takes 8 Tokens: Weak-to-Strong Off-Policy RL via Auxiliary Branches A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-02T14:45:23.980380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9475cb6-7154-4997-af99-64cca06655f9 · inbound

Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning cites this paper.

Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-01T13:54:23.273919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.