Researchers say they trained a foundation model from scratch for about $1,500

Sapient researchers say their HRM-Text architecture trained a 1B-parameter foundation model from scratch using far less data and compute than standard Transformer-based LLMs. The model trains on instruction-response pairs rather than broad internet-scale next-token prediction, and reportedly performs competitively with larger open models on industry benchmarks.

If validated, this suggests foundation model training could become practical for smaller enterprises instead of remaining limited to hyperscalers and top labs. The approach reflects a broader shift from brute-force scaling toward more efficient architectures and task-focused training pipelines.