ProgressGym: Alignment with a Millennium of Moral Progress
Authors: name: Tianyi Qiu, name: Yang Zhang, name: Xuchuan Huang, name: Jasmine Xinze Li, name: Jiaming Ji, name: Yaodong Yang

Frontier AI systems, including large language models (LLMs), hold increasing
influence over the epistemology of human users. Such influence can reinforce
prevailing societal values, potentially contributing to the lock-in of
misguided moral beliefs and, consequently, the perpetuation of problematic
moral practices on a broad scale. We introduce progress alignment as a
technical solution to mitigate this imminent risk. Progress alignment
algorithms learn to emulate the mechanics of human moral progress, thereby
addressing the susceptibility of existing alignment methods to contemporary
moral blindspots. To empower research in progress alignment, we introduce
ProgressGym, an experimental framework allowing the learning of moral progress
mechanics from history, in order to facilitate future progress in real-world
moral decisions. Leveraging 9 centuries of historical text and 18 historical
LLMs, ProgressGym enables codification of real-world progress alignment
challenges into concrete benchmarks. Specifically, we introduce three core
challenges: tracking evolving values (PG-Follow), preemptively anticipating
moral progress (PG-Predict), and regulating the feedback loop between human and
AI value shifts (PG-Coevolve). Alignment methods without a temporal dimension
are inapplicable to these tasks. In response, we present lifelong and
extrapolative algorithms as baseline methods of progress alignment, and build
an open leaderboard soliciting novel algorithms and challenges. The framework
and the leaderboard are available at
https://github.com/PKU-Alignment/ProgressGym and
https://huggingface.co/spaces/PKU-Alignment/ProgressGym-LeaderBoard
respectively.