是之前Language to Rewards for Robotic Skill Synthesis工作的延伸。 (捉个虫:本方法是“免梯度上下文学习”,即 gradient-free in-context learning) 下面是某一轮迭代对奖励函数的修改。可以看到既有系数(magic number)的修改,又有函数形式的修改。 算法很朴实。 可以想到一个简单的改进,是提供一些人类写的奖励函...
Eureka: Human-Level Reward Design via Coding Large Language Models https://arxiv.org/pdf/2310.12931关于大语言模型最大的一个梦想之一,是创造“AI 研究员”,能自己写代码跑实验发 arxiv 那种。本文尝试在强化学习的“核心科技”——奖励函数——设计上,… 周舒畅发表于大模型读大... Nature:大语言模型正在...
Large language models can perform content generation, translation, and analytical reasoning tasks. Find out the top 10 LLMs to use in 2024.
We are seeking a Senior Researcher (Large Language Models) with a proficient coding and research skills to join our dynamic team. Our mission is to revolutionize the use of Large Language Models on edge and limited-resource devices such as laptops and phones. We aim to achieve this by focusin...
Large language models (LLMs), led by GPT and followed by numerous other models, have demonstrated their strong capabilities in many areas, from language processing such as text generation and document summarization, to coding, reasoning, ...
While a lot of recent research focuses on enhancing the textual reasoning capabilities of Large Language Models (LLMs) by optimizing the multi-agent framework or reasoning chains, several benchmark tasks can be solved with 100% success through direct coding, which is mor...
boasts an impressive 540 billion parameters, making it one of the largest and most powerful language models available today. Moreover, with advanced coding skills, it can proficiently generate code in various programming languages like Python, Java, and C++, making it an invaluable asset for ...
A tiny library for coding withlargelanguage models. Check out theMiniChain Zooto get a sense of how it works. Coding Code (math_demo.py): Annotate Python functions that call language models. @prompt(OpenAI(),template_file="math.pmpt.tpl")defmath_prompt(model,question):"Prompt to call GP...
Coding with context is particularly effective for constructs that often appear as part of sequences, but can also lead to the model coding more based on the context rather than the current line. This investigation highlights the potential of GPT-4 Turbo for efficient auto-coding of large ...
NVIDIA 实现机器人灵巧性的重大突破 | Eureka: Human-Level Reward Design via Coding Large Language Models [译] 摘要 大型语言模型(LLM)在序列决策任务的高级语义规划方面表现出色。然而,如何利用它们来学习复杂的低级操作任务,比如灵巧的转笔,仍然是一个悬而未决的问题。我们弥合了这一基本差距,并提出了 EUREKA,...