title: Absolute Zero: Reinforced Self-play Reasoning with Zero Data
summary: Reinforcement learning with verifiable rewards (RLVR) has shown promise in
  enhancing the reasoning capabilities of large language models by learning
  directly from outcome-based rewards. Recent RLVR works that operate under the
  zero setting avoid supervision in labeling the reasoning process, but still
  depend on manually curated collections of questions and answers for training.
  The scarcity of high-quality, human-produced examples raises concerns about the
  long-term scalability of relying on human supervision, a challenge already
  evident in the domain of language model pretraining. Furthermore, in a
  hypothetical future where AI surpasses human intelligence, tasks provided by
  humans may offer limited learning potential for a superintelligent system. To
  address these concerns, we propose a new RLVR paradigm called Absolute Zero, in
  which a single model learns to propose tasks that maximize its own learning
  progress and improves reasoning by solving them, without relying on any
  external data. Under this paradigm, we introduce the Absolute Zero Reasoner
  (AZR), a system that self-evolves its training curriculum and reasoning ability
  by using a code executor to both validate proposed code reasoning tasks and
  verify answers, serving as an unified source of verifiable reward to guide
  open-ended yet grounded learning. Despite being trained entirely without
  external data, AZR achieves overall SOTA performance on coding and mathematical
  reasoning tasks, outperforming existing zero-setting models that rely on tens
  of thousands of in-domain human-curated examples. Furthermore, we demonstrate
  that AZR can be effectively applied across different model scales and is
  compatible with various model classes.
paper_authors._id: 681ab9b8f43603c60cab87a3
paper_authors.name: Andrew Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-14T07:36:10.447Z
paper_authors._id: 630482fbce6b12280b18971d
paper_authors.avatarUrl: /avatars/b07f31fd970d736bdf574d56da7a5634.svg
paper_authors.fullname: Andrew Zhao
paper_authors.type: user
paper_authors.user: andrewzh
paper_authors._id: 681ab9b8f43603c60cab87a4
paper_authors.name: Yiran Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-07T08:09:25.939Z
paper_authors._id: 647eb24118274bce0308b2b8
paper_authors.avatarUrl: /avatars/463e49a89b61164ccfad85ced10658b2.svg
paper_authors.fullname: Yiran Wu
paper_authors.type: user
paper_authors.user: kevinwyr
paper_authors._id: 681ab9b8f43603c60cab87a5
paper_authors.name: Yang Yue
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-07T08:09:23.436Z
paper_authors._id: 649d475111592b1a765ac1a3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/649d475111592b1a765ac1a3/rjORJjErJq-mthghan08U.jpeg
paper_authors.fullname: Yang Yue
paper_authors.type: user
paper_authors.user: Yang130
paper_authors._id: 681ab9b8f43603c60cab87a6
paper_authors.name: Tong Wu
paper_authors._id: 681ab9b8f43603c60cab87a7
paper_authors.name: Quentin Xu
paper_authors._id: 681ab9b8f43603c60cab87a8
paper_authors.name: Yang Yue
paper_authors._id: 681ab9b8f43603c60cab87a9
paper_authors.name: Matthieu Lin
paper_authors._id: 681ab9b8f43603c60cab87aa
paper_authors.name: Shenzhi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-07T08:09:28.623Z
paper_authors._id: 6486dde1f74857df3f1a5828
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6486dde1f74857df3f1a5828/FgE80CpalBO5qqArdfwxA.jpeg
paper_authors.fullname: Shenzhi Wang
paper_authors.type: user
paper_authors.user: shenzhi-wang
paper_authors._id: 681ab9b8f43603c60cab87ab
paper_authors.name: Qingyun Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-16T07:12:53.632Z
paper_authors._id: 647fa485d0cd8be13e662973
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/647fa485d0cd8be13e662973/_QLgqLWhWir8_bkDCdTx6.png
paper_authors.fullname: Qingyun Wu
paper_authors.type: user
paper_authors.user: qingyun-wu
paper_authors._id: 681ab9b8f43603c60cab87ac
paper_authors.name: Zilong Zheng
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-07T01:39:06.052Z
paper_authors._id: 63a95a6a7930fa8c7dd63d4e
paper_authors.avatarUrl: /avatars/d9d0420f7ddfe2f3a7e029fb05f1c89f.svg
paper_authors.fullname: Zilong Zheng
paper_authors.type: user
paper_authors.user: zlzheng
paper_authors._id: 681ab9b8f43603c60cab87ad
paper_authors.name: Gao Huang
paper_title: Absolute Zero: Reinforced Self-play Reasoning with Zero Data
paper_summary: Reinforcement learning with verifiable rewards (RLVR) has shown promise in
  enhancing the reasoning capabilities of large language models by learning
  directly from outcome-based rewards. Recent RLVR works that operate under the
  zero setting avoid supervision in labeling the reasoning process, but still
  depend on manually curated collections of questions and answers for training.
  The scarcity of high-quality, human-produced examples raises concerns about the
  long-term scalability of relying on human supervision, a challenge already
  evident in the domain of language model pretraining. Furthermore, in a
  hypothetical future where AI surpasses human intelligence, tasks provided by
  humans may offer limited learning potential for a superintelligent system. To
  address these concerns, we propose a new RLVR paradigm called Absolute Zero, in
  which a single model learns to propose tasks that maximize its own learning
  progress and improves reasoning by solving them, without relying on any
  external data. Under this paradigm, we introduce the Absolute Zero Reasoner
  (AZR), a system that self-evolves its training curriculum and reasoning ability
  by using a code executor to both validate proposed code reasoning tasks and
  verify answers, serving as an unified source of verifiable reward to guide
  open-ended yet grounded learning. Despite being trained entirely without
  external data, AZR achieves overall SOTA performance on coding and mathematical
  reasoning tasks, outperforming existing zero-setting models that rely on tens
  of thousands of in-domain human-curated examples. Furthermore, we demonstrate
  that AZR can be effectively applied across different model scales and is
  compatible with various model classes.
paper_ai_summary: Absolute Zero Reasoner (AZR) achieves state-of-the-art performance on coding and mathematical reasoning tasks through self-generated, verified learning without external data.
paper_ai_keywords: reinforcement learning with verifiable rewards zero setting self-evolving training curriculum code executor verifiable reward open-ended learning grounded learning