most citedMemento: Fine-tuning LLM Agents without Fine-tuning LLMs

2 citations · 4 across the 6 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2026

Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search

Zhongwei Yu, Yan Song, Xue Yan +9

Scientific discovery often involves optimising expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and c…

cs.LG2025★ 2 cited

Memento: Fine-tuning LLM Agents without Fine-tuning LLMs

Huichi Zhou, Yihang Chen, Siyuan Guo +8

In this paper, we introduce a novel learning paradigm for Adaptive Large Language Model (LLM) agents that eliminates the need for fine-tuning the underlying LLMs. Existing approach…