From dd61eaae4386cae945c08a99ef922b63e2a31c99 Mon Sep 17 00:00:00 2001 From: Shaokun Zhang Date: Thu, 28 Mar 2024 01:11:20 -0400 Subject: [PATCH] update (#2178) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: AnonymousRepoSub <“shaokunzhang529@outlook.com” > --- website/blog/2023-12-23-AgentOptimizer/index.mdx | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/website/blog/2023-12-23-AgentOptimizer/index.mdx b/website/blog/2023-12-23-AgentOptimizer/index.mdx index 23e348fa3..caafda757 100644 --- a/website/blog/2023-12-23-AgentOptimizer/index.mdx +++ b/website/blog/2023-12-23-AgentOptimizer/index.mdx @@ -12,10 +12,12 @@ tags: [LLM, research] **TL;DR:** Introducing **AgentOptimizer**, a new class for training LLM agents in the era of LLMs as a service. **AgentOptimizer** is able to prompt LLMs to iteratively optimize function/skills of AutoGen agents according to the historical conversation and performance. -Checkout one implementation for **AgentOptimizer** on [MATH](https://github.com/hendrycks/math) dataset -[here](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_agentoptimizer.ipynb). -More information could be found in the [paper](https://arxiv.org/abs/2402.11359). +More information could be found in: + +**Paper**: https://arxiv.org/abs/2402.11359. + +**Notebook**: https://github.com/microsoft/autogen/blob/main/notebook/agentchat_agentoptimizer.ipynb. ## Introduction In the traditional ML pipeline, we train a model by updating its weights according to the loss on the training set, while in the era of LLM agents, how should we train an agent?