Sam Acquaviva @Sam_Acqua
now @ cursor, ex-cocosci @ mit, after thought ex-intern @ apple, google x samacquaviva.com Joined February 2014-
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@srush_nlp Thanks Sasha :) excited to work together
Super cool work + also great to see ppl report the gen ppl frontiers :)
Self-conditioning can be distilled into one-step language generation. We show that self-conditioned flow LMs perform fixed-point iteration, and introduce fixed-point flow maps to compress both the iterations and the entire flow. At one step, FMLM★ achieves 112.5 gPPL at
So cool
How well can you describe the feature selectivity of a vision neuron … with words? Interpretability has long borrowed from neuroscience — and maybe it can give back too! 🧵
This is cool but disingenuous framing imo. All of the records were marginal, building on other records, so “out-outperformed all 1,016 other researchers” is a stretch lol I’m curious what this system’s performance would be if it couldn’t build on human records
OpenAI ran a hiring challenge, but the top candidate was one they couldn’t hire: our autonomous research agent, Aiden. In Parameter Golf, Aiden ran for 22 days, and out-outperformed all 1,016 other researchers: 🧵 (1/8)
@YifeiZuoX awesome -- thank you for running this
@YifeiZuoX @Haoxiang__Wang @kellerjordan0 I mean compute-matched and param-matched during training. e.g. both models at .6B and trained for 1e23 FLOPs (for example)
@YifeiZuoX @Haoxiang__Wang @kellerjordan0 Thanks for the response — and great paper! Isn’t parallax better compute-matched at .6b? Or am I missing something? And any reason the param-matched transformer and compute-matched parallax aren’t included for 1.7b?
@whatthelukh Great post! wdyt about CTPO as a lower-variance-but-still-unbiased estimate of IS ratio? I'm curious how it would do on your "Simple Horizon Simulation" at small batch sizes
@redtachyon + w/ discount factor = 1 & 0/1 rewards, reinforce = rejection sampling
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