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reacted to anakin87's post with ❤️ about 20 hours ago
🌀 Let LLMs wander - Engineering RL Environments Reinforcement Learning Environments are little worlds where models can act, get rewards, and learn. I've been exploring how to design them, figuring out what works and what doesn't. If you want to learn how to build them, I recorded a practical intro video. You'll also see how to turn Liquid AI LFM2-2.6B into a Tic-tac-toe master 🙂 🎥 Engineering RL Environments video: https://www.youtube.com/watch?v=71V3fTaUp2Q --- 🌱 LLM RL Environments Lil Course: https://github.com/anakin87/llm-rl-environments-lil-course 🤗🕹️ Play against the trained model: https://huggingface.co/spaces/anakin87/LFM2-2.6B-mr-tictactoe 📚 HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
upvoted a paper 1 day ago
Kronos: A Foundation Model for the Language of Financial Markets
reacted to Juanxi's post with 🔥 1 day ago
📢 Awesome Multimodal Modeling We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligence—from foundational fusion to native omni-models. 🔹 Taxonomy & Evolution: Traditional Multimodal Learning – Foundational work on representation, fusion, and alignment. Multimodal LLMs (MLLMs) – Architectures connecting vision encoders to LLMs for understanding. Unified Multimodal Models (UMMs) – Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms. Native Multimodal Models (NMMs) – Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws. 💡 Key Distinction: UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training. 🔗 Explore & Contribute: https://github.com/OpenEnvision/Awesome-Multimodal-Modeling
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