import numpy as np

def gs1_adv(vec, prompts, rewards, T=0.1):
    X = vec.transform(prompts).toarray().astype(np.float64)
    norms = np.linalg.norm(X, axis=1, keepdims=True)
    Xn = X / np.where(norms == 0, 1.0, norms)
    S = Xn @ Xn.T
    W = np.exp(S / T)
    np.fill_diagonal(W, 0.0)
    r = np.asarray(rewards, dtype=np.float64)
    b = (W @ r) / W.sum(1)
    return r - b, b