Pinned
You don’t need frontier lab resources for frontier lab automated LLM evaluation. To prove this, we’re open-sourcing j1-nano and j1-micro: two absurdly tiny (600M & 1.7B parameters) but mighty reward models competitive with orders-of-magnitude larger peers. j1-nano and j1-micro- First came pre-training scaling; then came inference-time scaling. Now comes judge-time scaling. Despite progress in AI through scaled inference-time compute, AI remains unreliable in open-ended, non-verifiable domains. The key limitation is not generation—it is evaluation.
- born to do research forced to build b2b saas
- i've been entirely consumed these past few weeks by the LLM-as-a-judge research agenda. there's lots of great work, but there's also lots of noise, confusion, and redundancy in the literature. i’ve started curating the highest-quality reads here:
- honored to be amongst such amazing peers and community🫶We are excited to announce the startups accepted into the fourth batch of AI Grant! See the thread below as well as aigrant.com to learn more
- super excited to share what we've been cooking up at @haizelabs🕊️🕊️ we are now in the era of grossly excessive AI hype and demoware. but it is high time to recalibrate and revisit the difficult, unsexy, underlying problem that everybody is avoiding -- the AI reliability andToday is a bad, bad day to be a language model. Today, we announce the Haize Labs manifesto. @haizelabs haizes (automatically red-teams) AI systems to preemptively discover and eliminate any failure mode We showcase below one particular application of haizing: jailbreaking the
- come join @qw3rtman @willccbb and myself for the inaugural communion of the NYC AI Reading Group! > where: @haizelabs hq > when: sunday 4/27 @ 11 am > what: inference-time scaling for generalist reward modeling from @deepseek_ai > who: awesome people like yourself :^)
- nyc ai 🚀🚀🚀 scintillating discussion on this fine sunday morning. much more to come. @qw3rtman @willccbb @haizelabs
- session #2 of the NYC AI Reading Group w/ @qw3rtman @willccbb is in order! > where: @haizelabs hq > when: thursday 5/29 @ 7:30 pm > what: sft memorizes, rl generalizes: a comparative study of foundation model post-training > who: awesome people like yourself :^) > also:
- we're looking for outlier talent to join the @haizelabs research team if you're interested in: - robustness of real-world AI - active learning - ultra-efficient model tuning - synthetic data generation - reward modeling - weak supervision dm us or apply below!
- We are thrilled to welcome Professor He He @hhexiy as an advisor to the Haize Labs team! Professor He leads a group at NYU focused on evaluation, scalable oversight, human–AI collaboration, and reasoning.
