๊ฐ€์ž… ํ›„ ์ดˆ๋Œ€ ๋งํฌ๋ฅผ ๊ณต์œ ํ•˜๋ฉด ๋™์˜์ƒ ์žฌ์ƒ ๋ฐ ์ดˆ๋Œ€ ๋ณด์ƒ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

Yuhang Yao
@yuhang_yao
Senior Research Scientist @ZOOM | PhD @CarnegieMellon | Graph Agent Research
๊ฐ€์ž… December 2021
117 ํŒ”๋กœ์ž‰ ์ค‘    1K ํŒฌ
๐Ÿš€ MERA: Model Evolution and Routing with Skill Adaptation for Agentic Systems at Scale How can we make small models stronger for on-device agent deployment? MERA uses stronger models to guide an iterative loop of RL/GRPO, skill learning, and router optimization. Student failures become verified demonstrations, reusable SkillBook procedures, and LoRA updates, helping the small model take on more work over time. ๐Ÿ”ฅ Results: Qwen2.5-Coder-1.5B: 28.7% โ†’ 49.7% coding pass Qwen3.5-2B on TAU-2: 14/35 โ†’ 18/35 Fine-tuned 2B matches an unadapted 4B model Donโ€™t just route around small models. Evolve them. ๐Ÿ“„ ๐Ÿ’ป
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