Compute Aligned Training: Optimizing for Test Time Inference
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 on the base policy, we derive new loss functions that maximize performance when said strategies are applied. We instantiate such loss functions for SF...
| Authors | Adam Ousherovitch, Ambuj Tewari |
|---|---|
| Primary category | cs.LG |
| Published | 27 April 2026 |
| Last updated at source | 30 September 2026 |
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