BLEUBERI: BLEU is a surprisingly effective reward for instruction
  following
Authors: name: Yapei Chang, name: Yekyung Kim, name: Michael Krumdick, name: Amir Zadeh, name: Chuan Li, name: Chris Tanner, name: Mohit Iyyer

Reward models are central to aligning LLMs with human preferences, but they
are costly to train, requiring large-scale human-labeled preference data and
powerful pretrained LLM backbones. Meanwhile, the increasing availability of
high-quality synthetic instruction-following datasets raises the question: can
simpler, reference-based metrics serve as viable alternatives to reward models
during RL-based alignment? In this paper, we show first that BLEU, a basic
string-matching metric, surprisingly matches strong reward models in agreement
with human preferences on general instruction-following datasets. Based on this
insight, we develop BLEUBERI, a method that first identifies challenging
instructions and then applies Group Relative Policy Optimization (GRPO) using
BLEU directly as the reward function. We demonstrate that BLEUBERI-trained
models are competitive with models trained via reward model-guided RL across
four challenging instruction-following benchmarks and three different base
language models. A human evaluation further supports that the quality of
BLEUBERI model outputs is on par with those from reward model-aligned models.
Moreover, BLEUBERI models generate outputs that are more factually grounded
than competing methods. Overall, we show that given access to high-quality
reference outputs (easily obtained via existing instruction-following datasets
or synthetic data generation), string matching-based metrics are cheap yet
effective proxies for reward models during alignment. We release our code and
data at https://github.com/lilakk/BLEUBERI.