INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized
  Reinforcement Learning
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.
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Prime Intellect Team
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Sami Jaghouar
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samsja
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Justus Mattern
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Justus Mattern
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Jack Min Ong
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Jack Min Ong
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Jannik Straube
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Jannik St
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Manveer Basra
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Aaron Pazdera
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Aaron Pazdera
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Kushal Thaman
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Matthew Di Ferrante
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Matthew Di Ferrante
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Felix Gabriel
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Fares Obeid
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Kemal Erdem
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Michael Keiblinger
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Johannes Hagemann
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INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized
  Reinforcement Learning
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.
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.
reinforcement learning language model distributed training asynchronous reinforcement learning PRIME-RL TOPLOC SHARDCAST GRPO training QwQ-32B decentralized training