aiqbench analysis
This research addresses the critical misalignment between standard post-training methods like SFT and RL, which optimize for individual samples, and modern inference strategies that rely on aggregated outputs or test-time compute scaling. By deriving new loss functions that explicitly align training objectives with these dynamic inference operators, the authors demonstrate a method to significantly enhance LLM performance when leveraging increased computation during the testing phase. This work is essential for researchers aiming to maximize the efficiency of large models as they transition toward heavier test-time compute regimes.
Compute Aligned Training: Optimizing for Test Time Inference
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 operators on the base policy, we derive new loss functions that maximize performance when said strategies are applied. We instantiate such loss functions for SFT and RL across common test time strategies. Finally, we provide empirical evidence that this training method substantially improves test time scaling over standard training.
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 on the base policy, we derive new loss functions that maximize performance when said strategies are applied. We instantiate such loss functions for SFT and RL across common test time strategies. Finally, we provide empirical evidence that this training method substantially improves test time scaling over standard training.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2604.24957 [cs.LG]
(or arXiv:2604.24957v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2604.24957 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 215 words
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