title: ProgressGym: Alignment with a Millennium of Moral Progress
summary: 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.
paper_authors._id: 66824a26ab37b944284dda8b
paper_authors.name: Tianyi Qiu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-01T07:46:27.168Z
paper_authors._id: 653260d5303c8ba75ae698e1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/653260d5303c8ba75ae698e1/AH35uVCNuyzSZVlOFRcw2.png
paper_authors.fullname: Tianyi Qiu
paper_authors.type: user
paper_authors.user: TianyiQ
paper_authors._id: 66824a26ab37b944284dda8c
paper_authors.name: Yang Zhang
paper_authors._id: 66824a26ab37b944284dda8d
paper_authors.name: Xuchuan Huang
paper_authors._id: 66824a26ab37b944284dda8e
paper_authors.name: Jasmine Xinze Li
paper_authors._id: 66824a26ab37b944284dda8f
paper_authors.name: Jiaming Ji
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T09:01:54.778Z
paper_authors._id: 6429b13816d4d8293c97a949
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6429b13816d4d8293c97a949/LNuQ1gvW2NMmWyIuEpzCA.jpeg
paper_authors.fullname: JiJiaming
paper_authors.type: user
paper_authors.user: jijiaming
paper_authors._id: 66824a26ab37b944284dda90
paper_authors.name: Yaodong Yang
paper_title: ProgressGym: Alignment with a Millennium of Moral Progress
paper_summary: 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.
paper_ai_keywords: progress alignment moral progress human moral progress alignment methods moral blindspots ProgressGym historical text real-world moral decisions tracking evolving values preemptively anticipating moral progress regulating feedback loop human and AI value shifts lifelong algorithms extrapolative algorithms open leaderboard