R-Zero: Self-Evolving Reasoning LLM from Zero Data
Self-evolving Large Language Models (LLMs) offer a scalable path toward
super-intelligence by autonomously generating, refining, and learning from
their own experiences. However, existing methods for training such models still
rely heavily on vast human-curated tasks and labels, typically via fine-tuning
or reinforcement learning, which poses a fundamental bottleneck to advancing AI
systems toward capabilities beyond human intelligence. To overcome this
limitation, we introduce R-Zero, a fully autonomous framework that generates
its own training data from scratch. Starting from a single base LLM, R-Zero
initializes two independent models with distinct roles, a Challenger and a
Solver. These models are optimized separately and co-evolve through
interaction: the Challenger is rewarded for proposing tasks near the edge of
the Solver capability, and the Solver is rewarded for solving increasingly
challenging tasks posed by the Challenger. This process yields a targeted,
self-improving curriculum without any pre-existing tasks and labels.
Empirically, R-Zero substantially improves reasoning capability across
different backbone LLMs, e.g., boosting the Qwen3-4B-Base by +6.49 on
math-reasoning benchmarks and +7.54 on general-domain reasoning benchmarks.
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Chengsong Huang
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2025-08-08T16:19:51.646Z
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Chengsong Huang
user
ChengsongHuang
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Wenhao Yu
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Xiaoyang Wang
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Hongming Zhang
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Zongxia Li
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Ruosen Li
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Jiaxin Huang
68955cff48b0ae5ca2710d32
Haitao Mi
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Dong Yu
R-Zero: Self-Evolving Reasoning LLM from Zero Data
Self-evolving Large Language Models (LLMs) offer a scalable path toward
super-intelligence by autonomously generating, refining, and learning from
their own experiences. However, existing methods for training such models still
rely heavily on vast human-curated tasks and labels, typically via fine-tuning
or reinforcement learning, which poses a fundamental bottleneck to advancing AI
systems toward capabilities beyond human intelligence. To overcome this
limitation, we introduce R-Zero, a fully autonomous framework that generates
its own training data from scratch. Starting from a single base LLM, R-Zero
initializes two independent models with distinct roles, a Challenger and a
Solver. These models are optimized separately and co-evolve through
interaction: the Challenger is rewarded for proposing tasks near the edge of
the Solver capability, and the Solver is rewarded for solving increasingly
challenging tasks posed by the Challenger. This process yields a targeted,
self-improving curriculum without any pre-existing tasks and labels.
Empirically, R-Zero substantially improves reasoning capability across
different backbone LLMs, e.g., boosting the Qwen3-4B-Base by +6.49 on
math-reasoning benchmarks and +7.54 on general-domain reasoning benchmarks.
R-Zero is a self-evolving framework that autonomously generates and learns from its own training data, improving reasoning capabilities in LLMs without human-curated tasks.
Self-evolving Large Language Models LLMs R-Zero Challenger Solver co-evolve math-reasoning benchmarks general-domain reasoning benchmarks