Summary of Aviary: Training Language Agents on Challenging Scientific Tasks, by Siddharth Narayanan et al.
Aviary: training language agents on challenging scientific tasks
by Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths, Manu Ponnapati, Albert Bou, Jon Laurent, Ori Kabeli, Geemi Wellawatte, Sam Cox, Samuel G. Rodriques, Andrew D. White
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As machine learning educators writing for a technical audience not specialized in the paper’s subfield, we summarize the abstract as follows: The paper introduces Aviary, an extensible gymnasium for language agents that interact with tools via natural language or code. Language agents are promising for automating intellectual tasks in science because they can reason and plan to solve complex real-world tasks. However, their flexibility creates conceptual and practical challenges for software implementations. The authors formalize agents as policies solving language-grounded partially observable Markov decision processes, which they term language decision processes. They implement five environments, including three scientific environments that require multi-step reasoning, such as manipulating DNA constructs, answering research questions, and engineering protein stability. With online training and scaling inference-time compute, the authors show that language agents backed by open-source LLMs can match and exceed both frontier LLM agents and human experts on multiple tasks at a lower inference cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about creating AI assistants that can help scientists with complex tasks. These AI assistants can communicate with tools using natural language or code. The challenge is making sure they work well and are efficient. To test their abilities, the authors created five environments, including three related to biology research, such as DNA manipulation and protein engineering. They found that these AI assistants can perform just as well as human experts on multiple tasks, but at a much lower cost. |
Keywords
» Artificial intelligence » Inference » Machine learning
