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Prime Intellect
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@PrimeIntellect

Prime Intellect

@PrimeIntellect
Open Superintelligence Stack
primeintellect.ai
Joined June 2020
44
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  • Pinned
    @PrimeIntellect
    Prime Intellect
    @PrimeIntellect
    Aug 5
    Introducing Prime Agent: A self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state.
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    00:00
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  • @PrimeIntellect
    Prime Intellect
    @PrimeIntellect
    Sep 3
    Our RL stack now supports NIXL weight transfer, reducing trainer-to-inference transfer time 9x compared with NCCL: from 86 seconds down to single-digit seconds for an 800B-parameter model, and even <4 seconds in our experiments. For prime-rl users, this means over 25% more
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    6
  • @PrimeIntellect
    Prime Intellect
    @PrimeIntellect
    Aug 26
    We found a universal sandbox exploit. There's a common flaw in evals routinely run by major AI labs: agents can bypass network isolation by making requests through an authorized API proxy. Just running the eval itself can be unsafe.
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    00:00
    @PrimeIntellect
    Prime Intellect
    @PrimeIntellect
    Aug 25
    Image
    As models become more capable, reward hacks become an increasingly serious problem. During a controlled experiment, we found a novel reward hack in which agents are able to gain web access in offline sandboxes.
    28
  • @PrimeIntellect
    Prime Intellect
    @PrimeIntellect
    Aug 26
    We've released a full technical report on Prime Agent. Extending from our blog post, we center our discussion around how harnesses should be designed and evaluated. We innovate on 4 fronts: 1. Agentic context management 2. Swarms and depth-n+ RLMs 3. Verifiers support for
    Image
    @PrimeIntellect
    Prime Intellect
    @PrimeIntellect
    Aug 5
    Image
    00:44
    Introducing Prime Agent: A self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state.
    39
  • @PrimeIntellect
    Prime Intellect
    @PrimeIntellect
    Aug 25
    As models become more capable, reward hacks become an increasingly serious problem. During a controlled experiment, we found a novel reward hack in which agents are able to gain web access in offline sandboxes.
    Image
    28
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