title: INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized
    Reinforcement Learning
summary: We introduce INTELLECT-2, the first globally distributed reinforcement
  learning (RL) training run of a 32 billion parameter language model. Unlike
  traditional centralized training efforts, INTELLECT-2 trains a reasoning model
  using fully asynchronous RL across a dynamic, heterogeneous swarm of
  permissionless compute contributors.
    To enable a training run with this unique infrastructure, we built various
  components from scratch: we introduce PRIME-RL, our training framework
  purpose-built for distributed asynchronous reinforcement learning, based on top
  of novel components such as TOPLOC, which verifies rollouts from untrusted
  inference workers, and SHARDCAST, which efficiently broadcasts policy weights
  from training nodes to inference workers.
    Beyond infrastructure components, we propose modifications to the standard
  GRPO training recipe and data filtering techniques that were crucial to achieve
  training stability and ensure that our model successfully learned its training
  objective, thus improving upon QwQ-32B, the state of the art reasoning model in
  the 32B parameter range.
    We open-source INTELLECT-2 along with all of our code and data, hoping to
  encourage and enable more open research in the field of decentralized training.
paper_authors._id: 6822b9035f801d383a4b8742
paper_authors.name: Prime Intellect Team
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paper_authors.name: Jack Min Ong
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paper_authors.fullname: Jack Min Ong
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paper_authors.name: Jannik Straube
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paper_authors.name: Manveer Basra
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paper_authors.name: Aaron Pazdera
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paper_authors.fullname: Aaron Pazdera
paper_authors.type: user
paper_authors.user: apaz
paper_authors._id: 6822b9035f801d383a4b8749
paper_authors.name: Kushal Thaman
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paper_authors.fullname: Kushal Thaman
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paper_authors.name: Matthew Di Ferrante
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paper_authors.name: Felix Gabriel
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paper_authors.name: Kemal Erdem
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paper_authors.name: Michael Keiblinger
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paper_authors.name: Johannes Hagemann
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paper_title: INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized
    Reinforcement Learning
paper_summary: We introduce INTELLECT-2, the first globally distributed reinforcement
  learning (RL) training run of a 32 billion parameter language model. Unlike
  traditional centralized training efforts, INTELLECT-2 trains a reasoning model
  using fully asynchronous RL across a dynamic, heterogeneous swarm of
  permissionless compute contributors.
    To enable a training run with this unique infrastructure, we built various
  components from scratch: we introduce PRIME-RL, our training framework
  purpose-built for distributed asynchronous reinforcement learning, based on top
  of novel components such as TOPLOC, which verifies rollouts from untrusted
  inference workers, and SHARDCAST, which efficiently broadcasts policy weights
  from training nodes to inference workers.
    Beyond infrastructure components, we propose modifications to the standard
  GRPO training recipe and data filtering techniques that were crucial to achieve
  training stability and ensure that our model successfully learned its training
  objective, thus improving upon QwQ-32B, the state of the art reasoning model in
  the 32B parameter range.
    We open-source INTELLECT-2 along with all of our code and data, hoping to
  encourage and enable more open research in the field of decentralized training.
paper_ai_summary: INTELLECT-2, a globally distributed reinforcement learning training of a 32 billion parameter language model, uses fully asynchronous RL across a dynamic, heterogeneous swarm of contributors, improving upon QwQ-32B with custom infrastructure and training modifications.
paper_ai_keywords: reinforcement learning language model distributed training asynchronous reinforcement learning PRIME-RL TOPLOC SHARDCAST GRPO training QwQ-32B decentralized training