Prime Intellect releases modular verifiers v1 for agentic reinforcement learning · Digg

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Prime Intellect releases modular verifiers v1 for agentic reinforcement learning

The rewritten stack splits environments into tasksets, harnesses, and runtimes.

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Prime Intellect releases modular verifiers v1 for agentic reinforcement learning

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Nicholas Hyperion@nickhistgeekTECH

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5 posts, first seen 1h ago

82 likes

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Users are celebrating PrimeIntellect's Verifiers V1 release because separating tasksets from harness and runtime plus 183x trace compression unlocks scalable long-horizon agentic RL training.

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verifiers v1: Decomposing Tasksets and Harnesses for Agentic RL & Evaluations(primeintellect.ai)viaJohannes Hagemann

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Nicholas Hyperion@nickhistgeekTECH

@willccbb @mikasenghaas @xeophon 183x trace compression on 512-turn rollouts is bonkers. Long-horizon agent training at that scale was a non-starter before this.

Jeff Fedor@a_jfedorTECH

@willccbb @mikasenghaas @xeophon Congrats.

Tanishq, Ph.D. at ICML@iScienceLuvrTECH

@willccbb @mikasenghaas @xeophon congrats!

Astrid Wilde 🌞@astridwilde1TECH

@willccbb @mikasenghaas @xeophon huge milestone, well done

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will brown@willccbbTECH

this has been a labor of love for months with @mikasenghaas and @xeophon driving incredible progress, and `v1` is now finally ready for prime time we set out to fully modernize `verifiers` for the agent harness era, and unlocked some insane efficiency gains along the way https://x.com/willccbb/status/2076449433483616346/photo/1 https://twitter.com/PrimeIntellect/status/2076447247693402301