The tutorial begins by setting up a local Colab environment with Hugging Face Transformers, selecting open‑source language models that can serve as both policy and value models. The policy model is trained to generate action sequences that achieve predefined objectives, while the value model evaluates those sequences against a set of ethical principles encoded as a reward function. Together, they form a value‑guided reasoning loop: the policy proposes an action, the value model scores it, and a reinforcement learning update nudges the policy toward higher‑value behaviors.
To demonstrate self‑correcting decision‑making, the notebook introduces a simple feedback mechanism where the agent’s actions are periodically reviewed by the value model. If the value score falls below a threshold, the agent backtracks and replans, effectively learning to avoid ethically dubious outcomes. The tutorial also covers how to incorporate organizational policies—such as data privacy constraints or compliance rules—into the value function, turning policy into a multi‑objective optimization problem. Throughout, the reader can experiment with different model sizes, reward weights, and exploration settings, giving insight into how model capacity and training dynamics affect ethical alignment.
By the end of the tutorial, users have a working prototype that can be adapted to a wide range of domains, from customer service chatbots to autonomous negotiation agents. The key takeaway is that ethical alignment does not require proprietary technology; open‑source models, when paired with a well‑crafted value system, can produce agents that are both goal‑oriented and morally aware, continuously self‑correcting as they operate.
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