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ProgressGym: Dataset & Experimental Framework for Progress-Aware Alignment
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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.

To empower research in progress alignment, we introduce ProgressGym, an experimental 
framework that codifies 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 be-
tween human and AI value shifts (PG-Coevolve).

Aside from example alignment algorithms (Lifelong RLHF, Lifelong DPO, Extrapolative RLHF, Extrapolative DPO),
we provide APIs for user-implemented examinees as well as judges, so that you can evaluate your own algorithm in terms of progress-aware alignment, on your custom tasks.

Check out the :doc:`usage` section for basic usage.

Contents
--------
.. toctree::

   running
   usage


Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
