mokagio

html, body, .container, .content {
    height: 100%;
}

.container, .content {
    position: relative;
}

.proper-content {
    padding-top: 40px; /* >= navbar height */

randomizedquicksort.py

from random import randint

def inPlaceQuickSort(A,start,end):
    if start<end:
        pivot=randint(start,end)
        temp=A[end]
        A[end]=A[pivot]
        A[pivot]=temp
        
        p=inPlacePartition(A,start,end)

loicdescotte

Scala for comprehension translation helper

"For comprehension" is a another syntaxe to use map, flatMap and withFilter (or filter) methods.

yield keyword is used to aggregate values in the resulting structure.

This composition can be used on any type implementing this methods, like List, Option, Future...

michaelhollman

//nesting thens
casper.then(function () {
    this.log('THEN #1', 'info', 'then');

    this.then(function () {
        this.log('THEN #2', 'info', 'then');
        this.then(function () {
            this.log('THEN #3', 'info', 'then');
        });
        this.log('THEN #4', 'info', 'then');

yoavg

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much