Small Reasoning Models
Small Reasoning Models
Overview
Small reasoning models aim to deliver strong multi-step reasoning under constrained compute/latency.
A common pattern is training on high-quality and synthetic reasoning traces, then deploying small models in latency-bound environments.
Example: Phi-4-mini-flash-reasoning
Microsoft describes Phi-4-mini-flash-reasoning as an open-weight model optimized for math reasoning with large context, trained with synthetic data and distillation.
On-device angle
Research like MobileLLM focuses on sub-billion parameter models optimized for on-device use cases, reducing cloud cost and latency.
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