We examine min-p sampling (ICLR 2025 oral) & find significant problems in all 4 lines of evidence: human eval, NLP evals, LLM-as-judge evals, community adoption claims
Best of N was shown to exhibit power (polynomial) law scaling (left), but maths suggest one should expect exponential scaling (center). We show how to resolve this "paradox", then use our insights to design methods for predicting inference-scaling capabilities that can be more sample efficient!
Thanks for the advice and links! Do you know of a Render tutorial that involves getting Flask and ReactJS services to communicate with one another? Your 2nd and 3rd links demonstrate each independently. I don't know whether the same challenge will pop up in NextJS
Why make people search instead of quoting the relevant section?
"The human fasting mimicking diet (FMD) program is a plant-based diet program designed to attain fasting-like effects while providing micronutrient nourishment (vitamins, minerals, etc.) and minimize the burden of fasting. It comprises proprietary vegetable-based soups, energy bars, energy drinks, chip snacks, chamomile flower tea, and a vegetable supplement formula tablet (Table S4). The human FMD diet consists of a 5 day regimen: day 1 of the diet supplies ∼1,090 kcal (10% protein, 56% fat, 34% carbohydrate), days 2–5 are identical in formulation and provide 725 kcal (9% protein, 44% fat, 47% carbohydrate)."
"Subjects in the FMD cohort consumed the provided experimental diet consisting of 3 cycles of 5 continuous days of FMD followed by 25 days of normal food intake."
People frequently recommend Strang's teaching as an amazing pedagogical approach for engineers and applied mathematicians, but I find I'm frustrated every time I read his books or listen to his lectures. They don't work well for me and I've found much better alternatives
I just finished this fantastic class taught by Cengiz Pehlevan (https://pehlevan.seas.harvard.edu/), so I thought I might share the lectures and exercises with HN.
This is going to sound cynical, but I recently invested a week in rllib for a project before discovering that much of the under-the-hood implementation was horribly confusing, poorly documented and missing critical functionality (for instance, their IMPALA implementation only works with discrete action spaces). Does this library conceal similar problems?