HY-MT1.5 Technical Report

Quick Overview

The technical report for HY-MT1.5 demonstrates that the smaller, locally-run model significantly outperforms the larger, cloud-based Gemini 3.0 Pro model in translation tasks involving minority languages like Tibetan and Uger, achieving 99% of the quality at a fraction of the cost and latency, primarily by utilizing a specialized training pipeline focused on context preservation and avoiding common pitfalls like relying solely on simple metrics.

Key Points: The HY-MT1.5 model, despite being small enough to run locally on a phone, significantly outperformed Gemini 3.0 Pro in translation quality for minority languages like Tibetan and Uger. The 1.8B parameter HY-MT1.5 model scored 2.74 on Chinese-English tasks, beating the 72B parameter Gemini 3.0 Pro model's score of 2.55 on the same benchmark. HY-MT1.5 achieves this superior performance by using a five-stage training pipeline that includes specialized instruction tuning and incorporating reverse KL divergence to match the teacher model's probability distribution. The paper explicitly notes that standard AI training relies too heavily on simple metrics, whereas HY-MT1.5 focuses on preserving context, nuance, cultural appropriateness, and avoiding the 'causal blunder' of misinterpreting the reason for an output. The small model's ability to run locally allows for instant, private, and secure translation without high-speed internet, addressing the digital divide. The training pipeline involves stages of pre-training, instruction tuning, reverse KL divergence optimization, and finally, reinforcement learning (RL) based on human feedback (RLHF) using a specialized rubric.

Context: This video discusses the technical report for a new small language model called HY-MT1.5, developed by the Tensen Huanyun team, focusing on its superior performance in translation, especially for languages that are poorly represented in standard training data. The discussion contrasts the efficiency and quality of this small, specialized model against large, commercial models like Google's Gemini 3.0 Pro.