During the reinforcement learning fine-tuning of a language model, a specific action a_t (generating a token) is taken in state s_t. This action has a very high probability under the current policy, π_θ(a_t|s_t). However, the advantage function A(s_t, a_t) for this action is a large negative value. Considering just this single term in the policy gradient objective function, describe the resulting effect on the model's parameters during the update step and explain the reasoning behind this effect.