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Source channel @githubtrending · Post #15340 · Dec 17

#python#gym#gym_environment#reinforcement_learning#reinforcement_learning_agent#reinforcement_learning_environments#rl_environment#rl_training NeMo Gym helps you build and run reinforcement‑learning training environments for large language models, letting you develop, test, and collect verified rollouts separately from the training loop and integrate with your preferred RL framework and model endpoints (OpenAI, vLLM, etc.). It includes ready resource servers, datasets, and patterns for multi‑step, multi‑turn, and tool‑using scenarios, runs on a typical dev machine (no GPU required), and is early-stage with evolving APIs and docs. Benefit: you can generate high‑quality, verifiable training data faster and plug it into existing training pipelines to improve model behavior. https://github.com/NVIDIA-NeMo/Gym

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AI & Law

@ai_and_law · Post #377 · 08/19/2024, 07:04 AM

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MIT CSAIL Unveils Groundbreaking AI Risk Repository MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has launched its first-ever AI Risk Repository, setting a new standard for understanding and managing AI risks. This comprehensive database, encompassing over 700 identified risks from 43 existing frameworks, is poised to become an essential tool for stakeholders across the AI ecosystem. The AI Risk Repository is divided into three key components: ✅ AI Risk Database - a detailed compilation of risks, complete with references. ✅ Causal Taxonomy of AI Risks - an analytical framework that explains how, when, and why these risks manifest. ✅ Domain Taxonomy of AI Risks - categorizes these risks into seven domains and 23 subdomains. This repository offers an invaluable resource for researchers, developers, policymakers, and regulators, providing a unified reference point for identifying, analyzing, and mitigating AI-related risks. #AIandLaw#AIrisks#MITCSAIL#AIregulation#ResponsibleAI

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