Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

# train_grpo.py | |
# | |
# See https://github.com/willccbb/verifiers for ongoing developments | |
# | |
import re | |
import torch | |
from datasets import load_dataset, Dataset | |
from transformers import AutoTokenizer, AutoModelForCausalLM | |
from peft import LoraConfig | |
from trl import GRPOConfig, GRPOTrainer |
Yoav Goldberg, April 2023.
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
Welcome! We're glad you're here! This is the guide that we will be going through during the workshop.
IMPORTANT: Please make sure you have installed the software listed in this section before the workshop so that we can hit the ground running when the workshop starts.