# Cross-Entropy Games and Frost Training

> Source: <https://arxiv.org/abs/2605.27701>
> Published: 2026-05-28 04:00:00+00:00

# Computer Science > Artificial Intelligence

[Submitted on 26 May 2026]

# Title:Cross-Entropy Games and Frost Training

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Abstract:We present Frost Training, a method for improving Monte Carlo-based policy optimization for a large family of LLM-as-a-judge tasks called Cross-Entropy Games. The key idea is to exploit the gradient of the reward function in embedding space. This signal is used in the Greedy Coordinate Gradient (GCG) jailbreaking technique; we demonstrate for the first time that it can also be used to boost model training. We validate our method using GRPO training for maximum-likelihood infilling. Frost Training improves the model's ability to generate high-scoring outputs, reaching higher maximum scores in a best-of-k setting, and does so at an increased speed.

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