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Executable Code Actions Elicit Better LLM Agents

1 Feb 2024 · Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji

Large Language Model (LLM) agents, capable of performing a broad range of actions, such as invoking tools and controlling robots, show great potential in tackling real-world challenges. LLM agents are typically prompted to produce actions by generating #JSON or text in a pre-defined format, which is usually limited by constrained action space (e.g., the scope of pre-defined tools) and restricted flexibility (e.g., inability to compose multiple tools). This work proposes to use executable Python code to consolidate LLM agents' actions into a unified action space (CodeAct). Integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations through multi-turn interactions. Our extensive analysis of 17 LLMs on API-Bank and a newly curated benchmark shows that CodeAct outperforms widely used alternatives (up to 20% higher success rate). The encouraging performance of CodeAct motivates us to build an open-source #LLM agent that interacts with environments by executing interpretable code and collaborates with users using natural language. To this end, we collect an instruction-tuning dataset CodeActInstruct that consists of 7k multi-turn interactions using CodeAct. We show that it can be used with existing data to improve models in agent-oriented tasks without compromising their general capability. CodeActAgent, finetuned from Llama2 and Mistral, is integrated with #Python interpreter and uniquely tailored to perform sophisticated tasks (e.g., model training) using existing libraries and autonomously self-debug.

Paper: https://arxiv.org/pdf/2402.01030v4.pdf

Codes:
https://github.com/epfllm/megatron-llm
https://github.com/xingyaoww/code-act

Datasets: MMLU - GSM8K - HumanEval - MATH

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PiEEG kit - bioscience Lab in home for your Brain and Body

🖥 Github: https://github.com/pieeg-club/PiEEG_Kit

📕 Paper: https://arxiv.org/abs/2503.13482

🌟 Methods: https://paperswithcode.com/task/eeg-1
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Introduction to Graph Neural Networks: A Starting Point for Machine Learning Engineers

📓 Paper


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Forwarded from Papers
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Journal: scientific reports https://www.nature.com/srep/

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Applied Generative AI for Beginners.pdf
7.9 MB
Applied Generative AI for Beginners

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در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو می خواهیم بنویسیم. دوستانی که مایل هستن نفرات ۲ و ٣ این موضوع رو می تونن شرکت کنن.

زمان تقریبی شروع ۲۰ فروردین.

Journal: scientific reports https://www.nature.com/srep/

Price:
2: 400$
3: 300$

توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.

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Graph Theory and Additive Combinatorics
Exploring Structure and Randomness

📚 link


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🔥 Transformers Laid Out

📌 Guide


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Bias-Variance Trade-Off in Statistics at MIT OpenCourseWare

📚 Book



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Greetings.
As part of our research, we want to write a review article in the field of pathology. Friends who are interested in the 2nd and 3rd places on this topic can participate.

Approximate start time: April 10th.

Journal: scientific reports https://www.nature.com/srep/

Price:
2: $400
3: $300

I will help with complete explanations and how to write each section.

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FastCuRL: Curriculum Reinforcement Learning with Progressive Context Extension for Efficient Training R1-like Reasoning Models

🖥 Github: https://github.com/nick7nlp/FastCuRL

📕 Paper: https://arxiv.org/abs/2503.17287v1

🌟 Tasks
: https://paperswithcode.com/task/language-modeling

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Magic of open source is taking over the Video LoRA space

just dropped👇🔥
🍬LTX video community LoRA trainer with I2V support
🍬LTX video Cakify LoRA
🍬LTX video Squish LoRA
(🧨diffusers & comfy workflow)


trainer: https://github.com/Lightricks/LTX-Video-Trainer
LoRA: https://huggingface.co/Lightricks/LTX-Video-Cakeify-LoRA
LoRA2 : https://huggingface.co/Lightricks/LTX-Video-Squish-LoRA
🔥
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Solar_Power_Generation_Forecasting_in_Europe_A_Time_Series_Analysis.pdf
4.7 MB
Solar Power Generation Forecasting in Europe: A Time Series Analysis Python Code

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2025/07/10 05:18:06
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