arXiv:2509.22193v2 Announce Type: replace Abstract: Distilling reasoning traces from strong teacher models has become the standard recipe for building capable small language models. Yet reasoning traces are 5-20$\times$ longer than standard instruction fine-tuning (IFT) outputs, meaning every practitioner who chooses reasoning distillation implicitly forgoes training a larger IFT model on the same compute budget. Whether this trade-off is worthwhile remains unaddressed. We study it with a controlled experiment: a single teacher generates paired IFT and reasoning outputs for identical prompts b
This research addresses a critical trade-off in the current paradigm of AI model development, as practitioners grapple with optimizing compute usage for reasoning capabilities.
A strategic reader should care because this impacts the efficiency and resource allocation for training AI models, directly influencing the capabilities of smaller language models and the overall compute footprint of AI.
The understanding of the compute cost-benefit analysis between scaling model size and distilling reasoning traces fundamentally shifts, potentially leading to more efficient model development strategies.
- · AI compute providers
- · Smaller AI development labs
- · AI hardware manufacturers
- · Data scientists focused on model optimization
- · Developers solely focused on massive model scaling without optimization
- · Inefficient AI training methodologies
This research directly informs the choice between different training methodologies for language models, particularly for resource-constrained environments.
It could lead to a proliferation of more capable small language models, reducing the barrier to entry for AI development and deployment.
Increased efficiency in AI training might subtly mitigate the energy and compute demands, influencing the long-term sustainability of AI growth.
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Read at arXiv cs.CL