arXiv:2604.24957v2 Announce Type: replace Abstract: Scaling test-time compute has emerged as a powerful mechanism for enhancing Large Language Model (LLM) performance. However, standard post-training paradigms, Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), optimize the likelihood of individual samples under a base policy, creating a misalignment with test time procedures that rely on aggregated or filtered outputs. In this work, we propose Compute Aligned Training, which aligns training objectives with test-time strategies. By conceptualizing inference strategies as operators o
The increasing scale of Large Language Models (LLMs) and their test-time computational demands necessitate novel training methodologies to maximize performance efficiency.
Optimizing LLM training objectives to align with test-time inference strategies can lead to significant gains in performance and efficiency for AI applications.
Traditional LLM training paradigms, which previously focused on individual sample likelihood, will evolve to consider aggregated and filtered outputs during inference, potentially leading to more robust and powerful models.
- · AI developers
- · Cloud providers
- · Companies using LLMs for complex tasks
- · Companies with inefficient LLM deployments
More efficient and powerful LLMs will become available for various applications.
The improved performance of LLMs could accelerate the development of more sophisticated AI agents and autonomous systems.
Enhanced LLM capabilities could lead to new forms of human-computer interaction and automation across industries.
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