arXiv Artificial Intelligence

Compute Aligned Training: Optimizing for Test Time Inference

2 hours ago 02.10.2026 07:00 👁 2 views
Compute Aligned Training: Optimizing for Test Time Inference

Quick summary

arXiv:2604.24957v3 Announce Type: replace-cross 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 opera

Key takeaways

  • arXiv:2604.24957v3 Announce Type: replace-cross 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.

Why it matters

The significance is not only the legal text but how it changes product design. Decisions around “Compute Aligned Training: Optimizing for Test Time Inference” may reshape data collection, model training, output accountability and market access.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗