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Multiple Choice
An agent executes an identical sequence of states and actions in two different environments, A and B. The agent's policy (π_θ) and a reference policy (π_θ_ref) are also the same in both scenarios. When calculating the approximated policy divergence penalty using the formula Penalty = Σ [log π_θ(a_t|s_t) - log π_θ_ref(a_t|s_t)], the result is identical for both environments. What is the fundamental reason for this?
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Updated 2025-09-26
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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Analysis in Bloom's Taxonomy
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