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Recent advancements in Large Language Models (LLMs) have shown significant progress in understanding complex natural language. However, LLMs still face challenges in generating and executing programming codes accurately. While some efforts have been made to leverage LLMs for code generation,…
This is an LLM agent framework and symbolic learning system designed for building self-evolving autonomous agents. It functions as a computational graph orchestrator that organizes agent interactions and tool sequences as a trainable graph of nodes. The framework focuses on data-centric agent optimization, allowing agent pipelines and prompts to be upgraded through data-driven training rather than manual engineering. It utilizes a symbolic learning process that applies language-based loss and textual reflections to refine the operational logic and symbolic components of an agent. The system
(Aug 29, 2025) It is based on the TRL (Transformer Reinforcement Learning) package. We are also planning to use Verl to make this easy to use, especially for the inference time scaling, so stay tuned!
🎉 Updates (2025-05-03) MaAS is accepted as ICML'25 Oral (Top ~1% among 12,107 submissions)! - 🚩 Updates (2025-02-06) Initial upload to arXiv (see PDF).
The main features of bingreeky/maas are: Multi-Agent Systems.
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