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Large language models (LLMs) such as ChatGPT have seen widespread adoptiondue to their strong instruction-following abilities. Developing these LLMsinvolves a complex yet poorly understood workflow requiring training with humanfeedback. Replicating and understanding this instruction-following requirestackling three major challenges: the high cost of data collection, the lack oftrustworthy evaluation, and the absence of reference method implementations. Weaddress these challenges with AlpacaFarm, a simulator that enables research anddevelopment for learning from feedback at a low cost. First, we design LLMprompts to simulate human feedback that are 50x cheaper than crowdworkers anddisplay high agreement with humans. Second, we propose an automatic evaluationand 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 evaluateeleven models on 10k pairs of real human feedback and show that rankings ofmodels trained in AlpacaFarm match rankings of models trained on human data. Asa demonstration of the research possible in AlpacaFarm, we find that methodsthat use a reward model can substantially improve over supervised fine-tuningand that our reference PPO implementation leads to a +10% improvement inwin-rate against Davinci003. We release all components of AlpacaFarm athttps://github.com/tatsu-lab/alpaca_farm.

AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback