Paper Citation Record · LEDGER

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

As of 7 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 36 inbound Pith citation observations for arXiv:2506.06122.

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

pith.paper-citation-record.v1
2506.06122 v1

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:02:33.099054Z

measured 102 of 102 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 36 of 36 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:16:05.122488Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:29:45.796847Z

Reference resolution

66 of 66 outbound references displayed

verified exact2
verified fuzzy19
unresolved44
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ab363d8d-7b2f-4975-9a8b-c0d38603374e · outbound

This paper cites https://openai.com/index/introducing-o3-and-o4-mini/, 2024.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library https://openai.com/index/introducing-o3-and-o4-mini/, 2024

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.446681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 512580ab-0b0b-49a5-af3b-2db8b3a8261e · outbound

This paper cites URL https://huggingface.co/datasets/Maxwell-Jia/AIME_2024.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library URL https://huggingface.co/datasets/Maxwell-Jia/AIME_2024

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.429316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1096a416-8100-4e99-963a-1e24ec5b7fe3 · outbound

This paper cites https://qwenlm.github.io/blog/qwq-32b/, 2025.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library https://qwenlm.github.io/blog/qwq-32b/, 2025

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.414382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 63217e89-5d4d-4da0-80ae-13cef1e56627 · outbound

This paper cites LMRL gym: Benchmarks for multi-turn reinforcement learning with language models, 2025.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library LMRL gym: Benchmarks for multi-turn reinforcement learning with language models, 2025

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.400303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eb9c277d-b119-4769-8a15-803007d4add3 · outbound

This paper cites Nemotron-crossthink: Scaling self-learning beyond math reasoning.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Nemotron-crossthink: Scaling self-learning beyond math reasoning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.824360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c4200e3-014b-46c1-bade-93478d5c1400 · outbound

This paper cites Deep Reinforcement Learning from Policy-Dependent Human Feedback.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Deep Reinforcement Learning from Policy-Dependent Human Feedback

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.828557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b5dd761-4421-4663-b2f4-bef60f31666f · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.834001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cca0b717-263d-43b3-a1a4-5fc609de5d36 · outbound

This paper cites Gonzalez, and Ion Stoica.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Gonzalez, and Ion Stoica

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.838170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0035524-e9bd-45e4-8d53-2f1331ef07ed · outbound

This paper cites Training Deep Nets with Sublinear Memory Cost.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Training Deep Nets with Sublinear Memory Cost

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.842355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 793eeeff-3b8a-4ae4-83cb-b2c1da129e97 · outbound

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

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.846754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ff9f6ff-9cde-4b24-8e38-d7d5895e15dd · outbound

This paper cites Multi-Programming Language Sandbox for LLMs.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Multi-Programming Language Sandbox for LLMs

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.853271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96b3d1f0-3812-48c6-a12e-2b744886c69f · outbound

This paper cites Group-in-Group Policy Optimization for LLM Agent Training.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Group-in-Group Policy Optimization for LLM Agent Training

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.857820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2d3095f-0f99-4008-ad1c-ad9dbd9f0756 · outbound

This paper cites Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.862408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28b6c286-7e98-473b-8f10-a6e470496c92 · outbound

This paper cites NeMo: a toolkit for Conversational AI and Large Language Models , 2025.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library NeMo: a toolkit for Conversational AI and Large Language Models , 2025

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.376089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d3e8f4f8-3665-4816-a5f4-b793aeb2c65c · outbound

This paper cites DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.870873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 683195ac-343c-4526-8a60-3fb774b83e18 · outbound

This paper cites KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.875169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be8d13dc-eb44-4a95-90e6-5cf38a3a11eb · outbound

This paper cites OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.879473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8651b302-d6ce-4d89-bb60-861847e44f6a · outbound

This paper cites Gpipe: Efficient training of giant neural networks using pipeline parallelism.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Gpipe: Efficient training of giant neural networks using pipeline parallelism

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.884352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 056139c0-5239-4443-b06f-420c0720588a · outbound

This paper cites Reinforcement Learning Via Practice and Critique Advice.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Reinforcement Learning Via Practice and Critique Advice

Reference 19

Resolution: verified exact; doi, observed 2026-08-07T06:02:33.155236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b5f7a72e-29d9-4520-8d3e-4bb2cf6255b0 · outbound

This paper cites Bradley Knox.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Bradley Knox

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.351460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 90fb3b8c-b6d4-4915-930c-4261edaf2eb8 · outbound

This paper cites Bradley Knox and Peter Stone.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Bradley Knox and Peter Stone

Reference 21

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T06:02:33.696419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ad75e3a8-6c0b-4028-a972-b3872e325934 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Gonzalez, Hao Zhang, and Ion Stoica

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.904314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 615c330c-4bd4-4a25-b56c-5d755219493d · outbound

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

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.908648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f00ffe9-e3b6-4ce6-a072-b46b04f10ab9 · outbound

This paper cites \ PUZZLE \ : Efficiently aligning large language models through \ Light-Weight \ context switch.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library \ PUZZLE \ : Efficiently aligning large language models through \ Light-Weight \ context switch

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.324242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a51ce3ff-543c-4194-beae-29d86be9df15 · outbound

This paper cites DISTFLASHATTN: Distributed Memory-efficient Attention for Long-context LLMs Training.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library DISTFLASHATTN: Distributed Memory-efficient Attention for Long-context LLMs Training

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.918029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4397b7e9-5a51-4462-97ae-4f0f935b4ab0 · outbound

This paper cites Sequence Parallelism: Long Sequence Training from System Perspective.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Sequence Parallelism: Long Sequence Training from System Perspective

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.922834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a44c0d5f-64d6-4a0a-9fc7-8da28e0b317c · outbound

This paper cites 2 D - DPO : Scaling direct preference optimization with 2-dimensional supervision.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library 2 D - DPO : Scaling direct preference optimization with 2-dimensional supervision

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.310187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 406a2f89-b656-4de2-99cb-2610d173383f · outbound

This paper cites MMI ference: Accelerating pre-filling for long-context vlms via modality-aware permutation sparse attention.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library MMI ference: Accelerating pre-filling for long-context vlms via modality-aware permutation sparse attention

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.296688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e7b4d3f4-ebb4-4e22-9f10-77a51fd73506 · outbound

This paper cites Remax: A simple, effective, and efficient method for aligning large language models.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Remax: A simple, effective, and efficient method for aligning large language models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.282458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a09bc472-20b0-4139-99ed-bb10412582e2 · outbound

This paper cites Agentbench: Evaluating LLM s as agents.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Agentbench: Evaluating LLM s as agents

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.266836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 64a4cfb5-9d1c-418b-beef-f05dc3f30fd8 · outbound

This paper cites KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.945286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5806a8e1-d95b-4688-b1ac-4adc6101a954 · outbound

This paper cites Giving advice about preferred actions to reinforcement learners via knowledge-based kernel regression.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Giving advice about preferred actions to reinforcement learners via knowledge-based kernel regression

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.253154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fda7b7fa-5ba2-4757-93b0-eeb1cbb426da · outbound

This paper cites ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.953420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation defc11c7-f3aa-457b-90db-c9975613fc73 · outbound

This paper cites Deepspeed autotuning.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Deepspeed autotuning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.236231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 123395c2-3d21-432f-9930-228d98f2d3da · outbound

This paper cites Jordan, and Ion Stoica.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Jordan, and Ion Stoica

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.220809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3a5065d3-99e7-4fd3-891e-2949c7603e78 · outbound

This paper cites Codeforces Dataset , 2025.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Codeforces Dataset , 2025

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.206003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a1697028-1a66-4132-9bb1-b72fb554c0e7 · outbound

This paper cites Training language models to follow instructions with human feedback.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Training language models to follow instructions with human feedback

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.975022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27d00742-6fb2-48e7-9c2b-b7117f3999f9 · outbound

This paper cites Training Software Engineering Agents and Verifiers with SWE-Gym.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Training Software Engineering Agents and Verifiers with SWE-Gym

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.979143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 633b5127-42d9-4779-a1e5-7cdf14c0ff53 · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.983611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d18e43d9-aaca-4ef0-bb16-eeeeb14d299a · outbound

This paper cites Zero: Memory optimizations toward training trillion parameter models.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Zero: Memory optimizations toward training trillion parameter models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:32.987821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46f32bd2-0b30-4ab9-a40c-980c200795e6 · outbound

This paper cites Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.140846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 927c602d-c083-4269-ab55-c88aa426d8c0 · outbound

This paper cites \ Zero-offload \ : Democratizing \ billion-scale \ model training.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library \ Zero-offload \ : Democratizing \ billion-scale \ model training

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.107266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c66bf214-1d17-4cad-882e-9f3369c3361c · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.001110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da40d64a-8569-4411-9254-4304e9b63ba6 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Proximal Policy Optimization Algorithms

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.005872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b291a80f-b68d-48ab-a4db-a12b72032a51 · outbound

This paper cites Seed-thinking-v1.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Seed-thinking-v1

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.010006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e957aad-b38a-4f15-8153-d1dce9d3d95e · outbound

This paper cites Sglang: Fast serving framework for large language models.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Sglang: Fast serving framework for large language models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:34.081743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 370f16b0-649d-43df-b2dd-35ec2da6d49e · outbound

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

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.017868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b23fa2d8-71d9-4169-8408-6b79ee715869 · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library HybridFlow: A Flexible and Efficient RLHF Framework

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.021832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8bcd306-525c-4606-b0d6-0eb8a9030166 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.026092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 735ce3c0-d2bb-4b5f-be3c-410c65cf066e · outbound

This paper cites LLM-as-a-Judge & Reward Model: What They Can and Cannot Do.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library LLM-as-a-Judge & Reward Model: What They Can and Cannot Do

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.030635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f20c883-72af-4be5-b670-ffd717f77947 · outbound

This paper cites Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.035346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccacab87-c5a4-4bef-b8c4-7defe3dc7e0e · outbound

This paper cites RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.039639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de5044b5-9486-4bb7-84fc-3278acc94c7d · outbound

This paper cites Deep TAMER : Interactive Agent Shaping in High-Dimensional State Spaces.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Deep TAMER : Interactive Agent Shaping in High-Dimensional State Spaces

Reference 53

Resolution: verified exact; doi, observed 2026-08-07T06:02:33.140311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 93fe47af-8199-4c12-9e38-51a65f955481 · outbound

This paper cites KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.047913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2aa1046d-820a-47b7-93e7-aa2b8b71956e · outbound

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

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.052451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2efadbfd-edfb-4181-b9f7-132155bc0036 · outbound

This paper cites Star: Bootstrapping reasoning with reasoning.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Star: Bootstrapping reasoning with reasoning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.056426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68bec4cf-22ee-441b-8a54-330e0d72bbc7 · outbound

This paper cites OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.060236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dc93f18-6194-4cba-bd60-729ece3974ec · outbound

This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.064390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdaff5c4-cb20-4e3e-bf71-6da010ba40a2 · outbound

This paper cites Optimizing RLHF Training for Large Language Models with Stage Fusion.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Optimizing RLHF Training for Large Language Models with Stage Fusion

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.069126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f11a428-3c5d-49a9-bcf4-d8d0d4410b22 · outbound

This paper cites StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.073265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ac5e8ab-15e7-49d2-96fe-10d5b3a07f41 · outbound

This paper cites Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.077517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c54788d8-f16d-4631-8d1d-3d7393188dbe · outbound

This paper cites Ar CH er: Training language model agents via hierarchical multi-turn RL.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Ar CH er: Training language model agents via hierarchical multi-turn RL

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:33.985545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation feaa7981-0591-492c-af26-a8a78b1dd2c2 · outbound

This paper cites write newline.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library write newline

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.085783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5292bed-1d2d-4471-b832-a9d43a5904c0 · outbound

This paper cites @esa (Ref.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library @esa (Ref

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.090557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78be3607-5a0c-4183-975a-66aa7cb91b35 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Unresolved cited work

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.094738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0892558c-3855-4e65-96a6-48ba4c59a46c · outbound

This paper cites an unresolved cited work.

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:33.099054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c1576350-4c9a-4f54-bcae-7fcecb8cd1d3 · inbound

RecGPT Technical Report cites this paper.

RecGPT Technical Report Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T11:16:05.122488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 897f391b-d021-49bd-bf3a-e1519725b756 · inbound

Agent Lightning: Train ANY AI Agents with Reinforcement Learning cites this paper.

Agent Lightning: Train ANY AI Agents with Reinforcement Learning Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T04:17:55.008086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 301f3916-28be-482d-97d3-d72a57f8f99b · inbound

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle cites this paper.

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 179

Resolution: unresolved; no resolver link, observed 2026-08-04T16:07:42.401654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad7c4fc5-127c-4d41-af37-1e097e952e77 · inbound

SHE: Stepwise Hybrid Examination Reinforcement Learning Framework for E-commerce Search Relevance cites this paper.

SHE: Stepwise Hybrid Examination Reinforcement Learning Framework for E-commerce Search Relevance Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:36:11.236206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff131638-77fc-41eb-bd47-63dfda5b131e · inbound

TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance cites this paper.

TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T10:53:09.781696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72950ae2-769d-4d8a-b64a-9ff5eaac8337 · inbound

Learning to Trust: Dynamic Utilization of Retrieval-Augmented Generation for E-commerce Search Relevance cites this paper.

Learning to Trust: Dynamic Utilization of Retrieval-Augmented Generation for E-commerce Search Relevance Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-18T08:11:07.151719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b00c5174-590a-4584-aaa1-0aed4cf90aa2 · inbound

Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization cites this paper.

Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T09:48:09.807998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36e7d6b3-f6d2-4b37-bd48-56e3bb42f980 · inbound

Seer: Online Context Learning for Fast Synchronous LLM Reinforcement Learning cites this paper.

Seer: Online Context Learning for Fast Synchronous LLM Reinforcement Learning Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:40:14.531558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e0190bf3-ac8c-4976-b820-cf6851901b3d · inbound

TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots cites this paper.

TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:30:11.134883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a677679c-d9c2-4e32-9af5-1c0ba159a767 · inbound

EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis cites this paper.

EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:08:04.507306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1acb3228-e099-4eb7-9fee-cabf3f8bd5a7 · 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 Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-04T06:25:53.985066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a890e8d4-eb8d-409d-8cd3-39bcc6009c17 · inbound

FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning cites this paper.

FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-16T11:07:47.084927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 54e0a443-0b89-41c7-81af-a6060d84df1e · inbound

Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models cites this paper.

Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:10:13.042955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1d08131e-bc68-4cac-8b13-e6311204bd50 · inbound

Rubrics to Tokens: Bridging Response-level Rubrics and Token-level Rewards in Instruction Following Tasks cites this paper.

Rubrics to Tokens: Bridging Response-level Rubrics and Token-level Rewards in Instruction Following Tasks Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:28:14.103827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3a2c3f59-b823-4fc0-9825-6df5dccc7e83 · inbound

Relax: An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scale cites this paper.

Relax: An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scale Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:16:03.246392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a2a9e41c-0119-478c-b58c-56dc4825aef2 · inbound

EasyVideoR1: Easier RL for Video Understanding cites this paper.

EasyVideoR1: Easier RL for Video Understanding Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:42:06.689947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 99dfa058-3c03-46c6-8b34-8c311883effb · inbound

Freshness-Aware Prioritized Experience Replay for LLM/VLM Reinforcement Learning cites this paper.

Freshness-Aware Prioritized Experience Replay for LLM/VLM Reinforcement Learning Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:52:14.069720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b1a486e4-e265-4b14-9808-e213a9aecbd7 · inbound

JigsawRL: Assembling RL Pipelines for Efficient LLM Post-Training cites this paper.

JigsawRL: Assembling RL Pipelines for Efficient LLM Post-Training Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:11:11.139120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a8b294fb-cc58-4de5-9569-c9d875a851aa · inbound

ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL cites this paper.

ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:31:16.305049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d2f7b234-e637-4a7a-8c4c-15610da0c84e · inbound

ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL cites this paper.

ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:39:53.289906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 40ed7ce8-f643-4933-b370-36d34c3c9eff · inbound

UserGPT Technical Report cites this paper.

UserGPT Technical Report Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:11:18.683912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7a6902da-cc9d-44b4-9d2e-093d8d1a27e0 · inbound

Verifiable Process Rewards for Agentic Reasoning cites this paper.

Verifiable Process Rewards for Agentic Reasoning Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:46:27.373550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b1f48d73-be66-4a87-b2f0-3d1e3bde7565 · inbound

Verifiable Process Rewards for Agentic Reasoning cites this paper.

Verifiable Process Rewards for Agentic Reasoning Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-06-30T22:45:07.266701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f3660f3f-d005-46a3-a023-10a31f34b7e2 · inbound

Missing Old Logits in Asynchronous Agentic RL: Semantic Mismatch and Repair Methods for Off-Policy Correction cites this paper.

Missing Old Logits in Asynchronous Agentic RL: Semantic Mismatch and Repair Methods for Off-Policy Correction Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:02:23.534275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6518ee4a-f0b2-471c-af4f-1923bffdc160 · inbound

Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding cites this paper.

Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-20T07:03:06.446849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9d6eb88f-6a99-4b30-8264-ddb67750a865 · inbound

Libra: Efficient Resource Management for Agentic RL Post-Training cites this paper.

Libra: Efficient Resource Management for Agentic RL Post-Training Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:16:27.171549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3c641bf8-1063-4d73-a40c-366c69a3b6fc · inbound

SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice cites this paper.

SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-02T05:46:41.068185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9bcb4746-94ca-4dfc-8ebf-c5cdb6f82076 · inbound

Spotlight: Synergizing Seed Exploration and Spot GPUs for DiT RL Post-Training cites this paper.

Spotlight: Synergizing Seed Exploration and Spot GPUs for DiT RL Post-Training Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-04T02:49:24.692270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 262e3738-a5e4-427d-94f9-349e4919805e · inbound

Group-Graph Policy Optimization for Long-Horizon Agentic Reinforcement Learning cites this paper.

Group-Graph Policy Optimization for Long-Horizon Agentic Reinforcement Learning Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:29:45.798796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 40aadc8f-ea47-46b9-b722-25100d56df1f · inbound

The Mirage of Optimizing Training Policies: Monotonic Inference Policies as the Real Objective for LLM Reinforcement Learning cites this paper.

The Mirage of Optimizing Training Policies: Monotonic Inference Policies as the Real Objective for LLM Reinforcement Learning Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:24:20.912894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fe389f63-900c-4705-ab04-2215ee34ba30 · inbound

ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping cites this paper.

ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-07-01T11:45:46.883444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8fde50f1-ed10-4e5f-97cc-876f04a51ffb · inbound

ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping cites this paper.

ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-02T09:19:06.709345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc362501-73a8-4172-b17c-eddfad69ac7d · inbound

Enhancing Large Multimodal Models in Key Information Extraction via Scene-Aware Document Synthesis cites this paper.

Enhancing Large Multimodal Models in Key Information Extraction via Scene-Aware Document Synthesis Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 87

Resolution: unresolved; no resolver link, observed 2026-07-11T16:02:00.920066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f69aa17-7c57-4d73-a9b0-1eade8a3b828 · inbound

Bidirectional Resource Scheduling for Disaggregated and Asynchronous RL Post-Training cites this paper.

Bidirectional Resource Scheduling for Disaggregated and Asynchronous RL Post-Training Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-13T04:42:35.589143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dcd30dd-5f01-4957-979c-a3a7d152910c · inbound

QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization cites this paper.

QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T06:48:17.223313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0af7cf3-3f4c-4b46-97c8-74e1d9486267 · inbound

Zing: Social Mind for LLMs cites this paper.

Zing: Social Mind for LLMs Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-30T14:24:27.787441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.