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SauerkrautLM-Doom-MultiVec: How a 1.3M Parameter Model Beats Large Language Models at Playing Doom

A ModernBERT model with only 1.3 million parameters, using hash embedding technology, outperforms large language models in Doom game control tasks, demonstrating the great potential of efficient small models in specific domains.

ModernBERT哈希嵌入Doom游戏小模型高效推理游戏AI参数效率
Published 2026-05-01 05:38Recent activity 2026-05-01 05:46Estimated read 1 min
SauerkrautLM-Doom-MultiVec: How a 1.3M Parameter Model Beats Large Language Models at Playing Doom
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Section 01

导读 / 主楼:SauerkrautLM-Doom-MultiVec: How a 1.3M Parameter Model Beats Large Language Models at Playing Doom

Introduction / Main Floor: SauerkrautLM-Doom-MultiVec: How a 1.3M Parameter Model Beats Large Language Models at Playing Doom

A ModernBERT model with only 1.3 million parameters, using hash embedding technology, outperforms large language models in Doom game control tasks, demonstrating the great potential of efficient small models in specific domains.