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Sapient Intelligence has released HRM-Text, a 1B parameter text generation model, trained on only 0.04 trillion tokens (costing approximately $1000), surpassing much larger models trained on 100-1000 times more data on multiple reasoning benchmarks, marking the beginning of a new paradigm for AI training.
HRM-text is a 1B-parameter hierarchical reasoning language model proposed by Sapient Intelligence. It thinks efficiently through internal latent space, achieving performance surpassing most models of the same size with extremely low training cost.
Sapient Intelligence released HRM-Text-1B, a 1-billion-parameter language model with a novel dual-timescale recurrent architecture (Hierarchical Reasoning Model) that provides unbounded compute depth at bounded parameter count. The pre-alignment checkpoint is available on Hugging Face.
The paper proposes ProcedureVQA, a benchmark for visual procedural question answering, and Chain-of-Procedure (CoP), a hierarchical reasoning framework that retrieves relevant instructions using visual cues and refines steps through semantic decomposition, achieving up to 13% improvement over baselines.