  Large language models (LLMs) such as ChatGPT have seen widespread adoption
due to their strong instruction-following abilities. Developing these LLMs
involves a complex yet poorly understood workflow requiring training with human
feedback. Replicating and understanding this instruction-following requires
tackling three major challenges: the high cost of data collection, the lack of
trustworthy evaluation, and the absence of reference method implementations. We
address these challenges with AlpacaFarm, a simulator that enables research and
development for learning from feedback at a low cost. First, we design LLM
prompts to simulate human feedback that are 50x cheaper than crowdworkers and
display high agreement with humans. Second, we propose an automatic evaluation
and validate it against human instructions obtained on real-world interactions.
Third, we contribute reference implementations for several methods (PPO, DPO,
best-of-n, expert iteration, and more) that learn from pairwise feedback.
Finally, as an end-to-end validation of AlpacaFarm, we train and evaluate
eleven models on 10k pairs of real human feedback and show that rankings of
models trained in AlpacaFarm match rankings of models trained on human data. As
a demonstration of the research possible in AlpacaFarm, we find that methods
that use a reward model can substantially improve over supervised fine-tuning
and that our reference PPO implementation leads to a +10% improvement in
win-rate against Davinci003. We release all components of AlpacaFarm at
https://github.com/tatsu-lab/alpaca_farm.
