How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
The main features of agi-templar/stable-alignment are: Agent Environments, Multi-Agent Systems, RLHF Datasets.
Projects with overlapping indexed features include: aiwaves-cn/agents — This is an LLM agent framework and symbolic learning system designed for building self-evolving autonomous agents. It… anthropics/hh-rlhf — Human preference data for "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback". aogara-ds/hoodwinked. bingreeky/maas — 🎉 Updates (2025-05-03) MaAS is accepted as ICML'25 Oral (Top ~1% among 12,107 submissions)! - 🚩 Updates (2025-02-06)… chanwoo-park-official/maporl — (Aug 29, 2025) It is based on the TRL (Transformer Reinforcement Learning) package. We are also planning to use Verl… agiresearch/autoflow — Recent advancements in Large Language Models (LLMs) have shown significant progress in understanding complex natural…
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
Human preference data for "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback"
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,…