Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning | Paper Citation Record

Observation b4892cd6-bcd0-41c9-b7bf-b89c2a0f977c · outbound

This paper cites Chi and Quoc V.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Chi and Quoc V

Reference 1

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning NeurIPS , year =

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Unresolved cited work

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This paper cites A Survey of Large Language Models , journal =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning A Survey of Large Language Models , journal =

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Unresolved cited work

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This paper cites Brown and Benjamin Chess and Rewon Child and Scott Gray and Alec Radford and Jeffrey Wu and Dario Amodei , title =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Brown and Benjamin Chess and Rewon Child and Scott Gray and Alec Radford and Jeffrey Wu and Dario Amodei , title =

Reference 6

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Unresolved cited work

Reference 7

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 8

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 9

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 10

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 11

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 12

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This paper cites Group Sequence Policy Optimization , journal =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Group Sequence Policy Optimization , journal =

Reference 13

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This paper cites Your Efficient RL Framework Secretly Brings You Off-Policy RL Training , url =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Your Efficient RL Framework Secretly Brings You Off-Policy RL Training , url =

Reference 14

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This paper cites The Fourteenth International Conference on Learning Representations , year=.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning The Fourteenth International Conference on Learning Representations , year=

Reference 15

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 16

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning 2025 , howpublished =

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning EuroSys , pages =

Reference 20

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 21

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 22

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 23

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This paper cites Qwen3.5: Accelerating Productivity with Native Multimodal Agents , url =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Qwen3.5: Accelerating Productivity with Native Multimodal Agents , url =

Reference 24

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 26

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This paper cites DeepSeek-V2:.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning DeepSeek-V2:

Reference 27

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 28

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 29

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 30

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 31

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 32

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Unresolved cited work

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This paper cites Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model , booktitle =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model , booktitle =

Reference 34

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 35

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

Reference 36

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This paper cites CoRR , volume =.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Unresolved cited work

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Unresolved cited work

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Unresolved cited work

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Gonzalez and Clark W

Reference 41

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning CoRR , volume =

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning NeurIPS , year =

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Unresolved cited work

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This paper cites AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 46

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Incentivizing dual process thinking for efficient large language model reasoning

Reference 47

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 48

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This paper cites macereason-math: A dataset of high-quality multilingual math problems ready for RLVR.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning macereason-math: A dataset of high-quality multilingual math problems ready for RLVR

Reference 49

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning

Reference 50

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning GLM-5: from Vibe Coding to Agentic Engineering

Reference 51

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Unresolved cited work

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

Reference 53

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Open-reasoner-zero: An open source approach to scaling up reinforcement learning on the base model

Reference 54

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning RLVR Datasets and Where to Find Them: Tracing Data Lineage for Better Training Data

Reference 55

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Megascale: Scaling large language model training to more than 10, 000 gpus

Reference 56

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Scaling Laws for Neural Language Models

Reference 57

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Kimi K2.5: Visual Agentic Intelligence

Reference 58

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Large language models are zero-shot reasoners

Reference 59

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Let's verify step by step

Reference 60

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Ring Attention with Blockwise Transformers for Near-Infinite Context

Reference 61

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Observation 99c812be-c174-4e18-a448-0094ca043f57 · outbound

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 62

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Observation 34bf8699-b6e2-4200-a761-ca1bd7a85ae0 · outbound

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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Understanding R1-Zero-Like Training: A Critical Perspective

Reference 63

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Observation eff9b1bb-79c6-428b-86bb-ba04d01d1efd · outbound

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

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:38.290188Z

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Observation 55a42e38-a151-44a7-988d-59c5d60052d8 · outbound

This paper cites Efficient large-scale language model training on GPU clusters using megatron-lm.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Efficient large-scale language model training on GPU clusters using megatron-lm

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:38.296337Z

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Observation 36ba925c-029b-4b53-afd1-c6e80ab1e360 · outbound

This paper cites Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:38.357406Z

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Observation 1de690bf-9584-40f1-b6af-ef0d9ac41291 · outbound

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

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Zero: memory optimizations toward training trillion parameter models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:38.431799Z

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Observation 68c23ab6-9060-489a-8090-23168e798d19 · outbound

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

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:38.475345Z

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Observation 8dbbfaac-f73b-4a31-bb03-50db4791cac6 · outbound

This paper cites Hybridflow: A flexible and efficient RLHF framework.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Hybridflow: A flexible and efficient RLHF framework

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:38.557172Z

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Observation fdf48ca9-d639-46db-80e3-2411d7e9e3f4 · outbound

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

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:38.634701Z

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Observation 75731417-20c7-44a9-bffc-eff3f824f2e9 · outbound

This paper cites Rethinking sample polarity in reinforcement learning with verifiable rewards.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Rethinking sample polarity in reinforcement learning with verifiable rewards

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:38.731951Z

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Observation 4bfb6b8a-90fd-4867-bd40-d2ab0c7b5bc3 · outbound

This paper cites Every step evolves: Scaling reinforcement learning for trillion-scale thinking model.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Every step evolves: Scaling reinforcement learning for trillion-scale thinking model

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:38.854751Z

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Observation c8a2c0c9-c587-426c-b133-05e0f1fc484c · outbound

This paper cites A survey on parallel reasoning.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning A survey on parallel reasoning

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:38.923125Z

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Observation 6c184937-d24d-4ebb-9e1f-65a96be8db18 · outbound

This paper cites Chi, Quoc V.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Chi, Quoc V

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:38.978930Z

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Observation a11c1bc5-f966-4004-b141-1c6c584e3ac5 · outbound

This paper cites Your efficient rl framework secretly brings you off-policy rl training, August 2025.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Your efficient rl framework secretly brings you off-policy rl training, August 2025

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:39.049337Z

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Observation 794d7ead-9bca-434d-8e6b-8bd20fcb2b5c · outbound

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

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:39.109905Z

Source-reported events for the cited work

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Observation 21794d45-9556-4282-914d-9c4b48097b07 · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:39.169135Z

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Observation 47e697d8-4a65-4332-acb8-c38c7128699f · outbound

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

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:39.232262Z

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Observation 8026aa05-9dc7-4e34-ba2c-cb04d2eab06f · outbound

This paper cites SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:39.345872Z

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Observation ae15005e-3986-412a-9fb9-afa01d71d31b · outbound

This paper cites A survey of large language models.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning A survey of large language models

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:39.399475Z

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Observation effb4bfc-0281-44a4-b21c-4446baed5c88 · outbound

This paper cites Group Sequence Policy Optimization.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Group Sequence Policy Optimization

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:39.461508Z

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Observation a49cfe8a-2894-4485-ab0b-ea0df3835026 · outbound

This paper cites Parallel-R1: Towards Parallel Thinking via Reinforcement Learning.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning Parallel-R1: Towards Parallel Thinking via Reinforcement Learning

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:39.525266Z

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Observation d05f42b4-ad2a-45ad-ac66-76730c05f020 · outbound

This paper cites slime: An llm post-training framework for rl scaling.

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning slime: An llm post-training framework for rl scaling

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-02T06:37:39.581071Z

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