Paper Citation Record · LEDGER

The Art of Scaling Reinforcement Learning Compute for LLMs

As of 4 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 44 inbound Pith citation observations for arXiv:2510.13786.

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

pith.paper-citation-record.v1
2510.13786 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z

measured 80 of 80 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 44 of 44 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:25:49.318391Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T12:15:01.137692Z

Reference resolution

36 of 36 outbound references displayed

verified exact12
verified fuzzy6
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch13

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ae038977-eb78-49da-8e44-bfeb7f50ad5d · outbound

This paper cites an unresolved cited work.

The Art of Scaling Reinforcement Learning Compute for LLMs Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-05-16T16:29:14.085252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 28668106-2e17-437f-9bed-27f75d82d55f · outbound

This paper cites Cwm: An open-weights llm for research on code generation with world models.

The Art of Scaling Reinforcement Learning Compute for LLMs Cwm: An open-weights llm for research on code generation with world models

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:13.976324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3134f12a-286c-4f99-add0-6531030943b1 · outbound

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

The Art of Scaling Reinforcement Learning Compute for LLMs The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-16T16:29:13.979906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bd699a5d-3ef4-4bbd-9808-05ad9436106a · outbound

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

The Art of Scaling Reinforcement Learning Compute for LLMs GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T16:29:13.983934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 99ddcc83-aa44-45f4-8bdc-3c5d3c0a65e1 · outbound

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

The Art of Scaling Reinforcement Learning Compute for LLMs Measuring Mathematical Problem Solving With the MATH Dataset

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T16:29:13.972253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4f6f3687-96ac-4848-bbd8-7f190b9b76ab · outbound

This paper cites Training Compute-Optimal Large Language Models.

The Art of Scaling Reinforcement Learning Compute for LLMs Training Compute-Optimal Large Language Models

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-16T16:29:13.987105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6eaf8934-e196-45dd-91e2-c8af25ef7b38 · outbound

This paper cites REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization.

The Art of Scaling Reinforcement Learning Compute for LLMs REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-16T16:29:13.990510Z

Source-reported events for the cited work

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Observation a999b556-f4d4-42ed-b4f6-329a44108c6c · outbound

This paper cites Scaling Laws for Neural Language Models.

The Art of Scaling Reinforcement Learning Compute for LLMs Scaling Laws for Neural Language Models

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-16T16:29:13.993355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8bf06b28-cf5e-4ad1-b1c1-798304e1d709 · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

The Art of Scaling Reinforcement Learning Compute for LLMs Kimi K2: Open Agentic Intelligence

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-16T16:29:13.996692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 150fcd04-9d9e-427d-9dba-c88f8f72ac38 · outbound

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

The Art of Scaling Reinforcement Learning Compute for LLMs ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:52:33.970587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 38ae454e-4f7b-403d-a66c-cc2cee262063 · outbound

This paper cites Quantifying Variance in Evaluation Benchmarks.

The Art of Scaling Reinforcement Learning Compute for LLMs Quantifying Variance in Evaluation Benchmarks

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.003804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 16e52cfc-216f-4d74-b354-52098de5ed16 · outbound

This paper cites MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention.

The Art of Scaling Reinforcement Learning Compute for LLMs MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T16:29:14.006669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7b26de1a-636b-4273-b24e-264f667f04ff · outbound

This paper cites Scaling Data-Constrained Language Models.

The Art of Scaling Reinforcement Learning Compute for LLMs Scaling Data-Constrained Language Models

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T01:35:21.865142Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dce19e5b-ab95-4c40-b085-3e52dadf25c3 · outbound

This paper cites OpenAI o1 System Card.

The Art of Scaling Reinforcement Learning Compute for LLMs OpenAI o1 System Card

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-16T16:29:14.013407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 75ebfafe-8d71-4a7a-90c1-c041c162b77d · outbound

This paper cites How predictable is language model benchmark performance?.

The Art of Scaling Reinforcement Learning Compute for LLMs How predictable is language model benchmark performance?

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:29:14.017077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 33c67b5a-9d66-4a3c-abb8-35214a292016 · outbound

This paper cites Resolving Discrepancies in Compute-Optimal Scaling of Language Models.

The Art of Scaling Reinforcement Learning Compute for LLMs Resolving Discrepancies in Compute-Optimal Scaling of Language Models

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.020476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b9720cbe-1dcf-4070-8683-8df9cd1a62cd · outbound

This paper cites Observational Scaling Laws and the Predictability of Language Model Performance.

The Art of Scaling Reinforcement Learning Compute for LLMs Observational Scaling Laws and the Predictability of Language Model Performance

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.024807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1591812d-8566-4314-bc4e-a05bd89db3d4 · outbound

This paper cites Proximal Policy Optimization Algorithms.

The Art of Scaling Reinforcement Learning Compute for LLMs Proximal Policy Optimization Algorithms

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T16:29:14.028011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9356063b-adf3-4de8-9c53-d56af148e932 · outbound

This paper cites Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning.

The Art of Scaling Reinforcement Learning Compute for LLMs Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T01:42:19.263401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a3e4bc57-bbd7-42e9-a1b9-7b1ca3204f87 · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

The Art of Scaling Reinforcement Learning Compute for LLMs Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-16T16:29:14.034600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0f043711-7720-401a-a758-a72b39b21b18 · outbound

This paper cites Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning.

The Art of Scaling Reinforcement Learning Compute for LLMs Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.038199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9590be79-55de-442f-a855-e92239a3bc6d · outbound

This paper cites Qwen3 Technical Report.

The Art of Scaling Reinforcement Learning Compute for LLMs Qwen3 Technical Report

Reference 22

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T16:29:14.042708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2498844c-269a-4133-ac73-4c622c4676f8 · outbound

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

The Art of Scaling Reinforcement Learning Compute for LLMs DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 23

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T16:29:14.046510Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ac9027f1-458d-4505-a4d9-19450ba8c1d2 · outbound

This paper cites What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret.

The Art of Scaling Reinforcement Learning Compute for LLMs What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:29:14.050525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2216c8df-3317-471e-98f8-c4ba2496518a · outbound

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

The Art of Scaling Reinforcement Learning Compute for LLMs VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-16T16:29:14.058755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b6ce8d30-d08c-425b-bdea-ea3187e8cd7e · outbound

This paper cites Generative Verifiers: Reward Modeling as Next-Token Prediction.

The Art of Scaling Reinforcement Learning Compute for LLMs Generative Verifiers: Reward Modeling as Next-Token Prediction

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:29:14.055173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0eb73f6f-96c1-401d-bf78-f5b1608761cf · outbound

This paper cites an unresolved cited work.

The Art of Scaling Reinforcement Learning Compute for LLMs Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-05-16T16:29:14.063973Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0cb31d43-77b5-44dc-aea0-286e1738bf4d · outbound

This paper cites (2017) for LLM fine-tuning with verifiable rewards.

The Art of Scaling Reinforcement Learning Compute for LLMs (2017) for LLM fine-tuning with verifiable rewards

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T16:29:14.066378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f435c24c-0f8c-484b-a314-662c4c3ba130 · outbound

This paper cites an unresolved cited work.

The Art of Scaling Reinforcement Learning Compute for LLMs Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-05-16T16:29:14.068606Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b75671b6-7cc2-4069-87b3-ae90f9f15f8c · outbound

This paper cites The lowerϵ is to avoid gradient clipping (epsilon underflow) (Wortsman et al., 2023).

The Art of Scaling Reinforcement Learning Compute for LLMs The lowerϵ is to avoid gradient clipping (epsilon underflow) (Wortsman et al., 2023)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T16:29:14.071043Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e3afc2a3-101b-4fad-ace2-16d0932efb15 · outbound

This paper cites We use a custom code execution environment for coding problems involving unit tests and desired outputs.

The Art of Scaling Reinforcement Learning Compute for LLMs We use a custom code execution environment for coding problems involving unit tests and desired outputs

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T16:29:14.073989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9677fa14-0eda-4d7d-88f6-1027e68e1c17 · outbound

This paper cites We consider two approaches: (a)interruptions, used in works like GLM-4.1V (GLM-V Team et al., 2025), and Qwen3 (Yang et al.

The Art of Scaling Reinforcement Learning Compute for LLMs We consider two approaches: (a)interruptions, used in works like GLM-4.1V (GLM-V Team et al., 2025), and Qwen3 (Yang et al

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T16:29:14.076619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c173bf54-65e5-4a42-909e-fce8989db88f · outbound

This paper cites Okay, time is up. Let me stop thinking and formulate a final answer</think>.

The Art of Scaling Reinforcement Learning Compute for LLMs Okay, time is up. Let me stop thinking and formulate a final answer</think>

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T16:29:14.078959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 96997020-d3c7-48e3-ba2e-29fe91c5f2a9 · outbound

This paper cites an unresolved cited work.

The Art of Scaling Reinforcement Learning Compute for LLMs Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-05-16T16:29:14.081042Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1632bf95-b2b1-4e9d-adbb-c9724059a112 · outbound

This paper cites an unresolved cited work.

The Art of Scaling Reinforcement Learning Compute for LLMs Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-05-16T16:29:14.083158Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bbf1ffff-e25f-4d55-acec-73cf9da2baac · outbound

This paper cites Specifically DAPO drops 0-variance prompts and samples more prompts until the batch is full.

The Art of Scaling Reinforcement Learning Compute for LLMs Specifically DAPO drops 0-variance prompts and samples more prompts until the batch is full

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T16:29:14.061537Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation 890744d3-a472-471a-80ab-4a0d270be6ee · inbound

Reinforcement Learning from Human Feedback cites this paper.

Reinforcement Learning from Human Feedback The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-22T19:32:01.153805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7132b63c-3286-4b58-8493-a9a9e8218e2f · inbound

RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments cites this paper.

RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 26

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Source-reported events for the cited work

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Observation 332ccb78-ae5a-485b-a8ad-083a7a05303a · inbound

Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning cites this paper.

Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e3886273-f2e8-45b3-9ed8-cf179c0f6d12 · inbound

Toward Training Superintelligent Software Agents through Self-Play SWE-RL cites this paper.

Toward Training Superintelligent Software Agents through Self-Play SWE-RL The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-21T16:10:20.191635Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8f6329e0-b46f-458e-b5d8-3be52158cd30 · inbound

Toward Training Superintelligent Software Agents through Self-Play SWE-RL cites this paper.

Toward Training Superintelligent Software Agents through Self-Play SWE-RL The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 21

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Source-reported events for the cited work

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Observation 60fdfe69-e7a5-42ec-929f-df86bb10a6d7 · inbound

StaleFlow: Staleness-Aware Data Management for Mitigating Data Skewness in Fully Disaggregated RL Post-Training cites this paper.

StaleFlow: Staleness-Aware Data Management for Mitigating Data Skewness in Fully Disaggregated RL Post-Training The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T06:25:49.318391Z

Source-reported events for the cited work

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Observation 5ceacded-530a-40f6-a185-993217b6f506 · inbound

Understanding and Exploiting Weight Update Sparsity for Communication-Efficient Distributed RL cites this paper.

Understanding and Exploiting Weight Update Sparsity for Communication-Efficient Distributed RL The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 29

Resolution: metadata mismatch; local_arxiv, observed 2026-05-21T13:40:12.439577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d3b94f34-1b5e-429e-9d82-6df92ea03926 · inbound

Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design cites this paper.

Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-05-21T13:44:11.524200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 98c45028-e813-4e69-853c-a91652bc950b · inbound

F-GRPO: Don't Let Your Policy Learn the Obvious and Forget the Rare cites this paper.

F-GRPO: Don't Let Your Policy Learn the Obvious and Forget the Rare The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T03:55:00.850928Z

Source-reported events for the cited work

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Observation a20f917b-130e-4f41-a5f5-0e635846e0e1 · inbound

Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning cites this paper.

Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8591c9cf-73fa-4f71-aae3-0acc77639aa9 · inbound

Continued AI Scaling Requires Repeated Efficiency Doublings cites this paper.

Continued AI Scaling Requires Repeated Efficiency Doublings The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:29:14.085942Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 80bf37f4-c32e-4562-b049-4a83d4b7a9be · inbound

Target Policy Optimization cites this paper.

Target Policy Optimization The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4cd2668a-9b7d-46d4-98e4-0089d5428ba9 · inbound

Beyond Distribution Sharpening: The Importance of Task Rewards cites this paper.

Beyond Distribution Sharpening: The Importance of Task Rewards The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c957c6d0-7941-4db5-98ca-246120b096c0 · inbound

Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes cites this paper.

Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d659a415-43c3-4a8b-ab55-4ee4580f6cba · inbound

Scaling Self-Play with Self-Guidance cites this paper.

Scaling Self-Play with Self-Guidance The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2e311385-4a51-4600-840a-99f31adea9d6 · inbound

Cost-Aware Learning cites this paper.

Cost-Aware Learning The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9bc70a71-71d9-4439-9d9f-5b13c6f717c5 · inbound

On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length cites this paper.

On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 90

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5a63f6d2-3f06-4cc1-88fc-82de48b8aa5d · inbound

Balanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO cites this paper.

Balanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 283135aa-3c85-418a-9a72-0b50b15b7fae · inbound

ZAYA1-8B Technical Report cites this paper.

ZAYA1-8B Technical Report The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 158

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation da05758b-252e-4c53-a5a5-1d4486cbbafc · inbound

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key cites this paper.

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 76

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e4ae07ba-b1e9-4406-b13c-ed8be5e58f04 · inbound

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key cites this paper.

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 79

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bd73d658-bfb2-4e24-a3b5-dfeae5a15333 · inbound

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key cites this paper.

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 79

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T22:39:10.411305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8cf8928e-2eaa-4253-bed9-5eb90f8e9d28 · inbound

Weblica: Scalable and Reproducible Training Environments for Visual Web Agents cites this paper.

Weblica: Scalable and Reproducible Training Environments for Visual Web Agents The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f4d0cb26-0180-4e15-be8c-a0b6b6dbee06 · inbound

KL for a KL: On-Policy Distillation with Control Variate Baseline cites this paper.

KL for a KL: On-Policy Distillation with Control Variate Baseline The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9e8e7edf-f295-47ff-9b1f-1ba7ae188728 · inbound

Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models cites this paper.

Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cccee1d1-89b2-44d5-b2cd-786401296878 · inbound

TRACE: Distilling Where It Matters via Token-Routed Self On-Policy Alignment cites this paper.

TRACE: Distilling Where It Matters via Token-Routed Self On-Policy Alignment The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2ad4c347-8285-43e9-945c-61ee19bac263 · inbound

Learning to Foresee: Unveiling the Unlocking Efficiency of On-Policy Distillation cites this paper.

Learning to Foresee: Unveiling the Unlocking Efficiency of On-Policy Distillation The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 93

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 48bbc7fc-73a8-4fbd-a577-d5bd6be6699a · inbound

Learning to Foresee: Unveiling the Unlocking Efficiency of On-Policy Distillation cites this paper.

Learning to Foresee: Unveiling the Unlocking Efficiency of On-Policy Distillation The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 93

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 336770af-d9bc-4cc6-9709-f44452ac097b · inbound

Learning, Fast and Slow: Towards LLMs That Adapt Continually cites this paper.

Learning, Fast and Slow: Towards LLMs That Adapt Continually The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8ce70be2-cb62-4b06-ae07-e233b13cdf09 · inbound

Learning, Fast and Slow: Towards LLMs That Adapt Continually cites this paper.

Learning, Fast and Slow: Towards LLMs That Adapt Continually The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.085942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e0d6d125-b30f-4223-8bd9-f922b29aa54d · inbound

Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design cites this paper.

Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 21

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T18:58:53.857181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e7e3fe6c-d9f9-44af-8dfd-0893782dd9f5 · inbound

Faster Synchronous On-Policy RL via Straggler-Aware Group Sizing cites this paper.

Faster Synchronous On-Policy RL via Straggler-Aware Group Sizing The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T22:06:17.099267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d40eddb5-23b1-42d0-9853-690827aeed2a · inbound

RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training cites this paper.

RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 48

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T02:46:28.994858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fbc048f1-6ec6-4991-813c-a93f9d112039 · inbound

Architecture-Aware Reinforcement Learning Makes Sliding-Window Attention Competitive in Math Reasoning cites this paper.

Architecture-Aware Reinforcement Learning Makes Sliding-Window Attention Competitive in Math Reasoning The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 88

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T09:47:59.843785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3b985535-95dd-49a0-a977-75a043a7f9a9 · inbound

Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients cites this paper.

Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:56.161137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 90fe2acd-3c6f-4d6f-a600-5727a08e177a · inbound

Breaking the Solver Bottleneck: Training Task Generators at the Learnable Frontier cites this paper.

Breaking the Solver Bottleneck: Training Task Generators at the Learnable Frontier The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 146

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T08:57:48.095122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1513e3a3-426d-4284-a48d-ea0393642148 · inbound

ZONOS2 Technical Report cites this paper.

ZONOS2 Technical Report The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 183

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T18:40:03.282572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ea59d1be-41d0-4456-b515-19d2e8cd1779 · inbound

ZONOS2 Technical Report cites this paper.

ZONOS2 Technical Report The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 183

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T18:15:58.953101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 16474127-7b58-4d61-a363-5702f9e930e4 · 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 The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-07-03T21:08:57.677335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b886bcb1-417d-4c26-8913-2c0937be3a9f · inbound

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments cites this paper.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af318e1f-883f-4e64-93af-cb0701df1549 · inbound

Mach-Mind-4-Flash Technical Report cites this paper.

Mach-Mind-4-Flash Technical Report The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-13T03:29:34.486347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5290bcba-6c1a-4b26-8172-e73dffe05623 · inbound

Where Should RL Post-Training Compute Go? Model Size, Search, Learning, and Feedback cites this paper.

Where Should RL Post-Training Compute Go? Model Size, Search, Learning, and Feedback The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T05:22:33.420002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b84ad023-1d0f-4acb-8a1b-f1c422cb1437 · inbound

Understanding Reasoning from Pretraining to Post-Training cites this paper.

Understanding Reasoning from Pretraining to Post-Training The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T21:26:32.726200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e72b1c8-2290-4794-a5a2-a98a76ad0bd4 · inbound

ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution cites this paper.

ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 168

Resolution: unresolved; no resolver link, observed 2026-08-01T09:52:05.363334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.