DedeProGames's picture

Open to Work

DedeProGames PRO

DedeProGames

·

AI & ML interests

Thinking and Agentic Finetuning

Recent Activity

reacted to anakin87's post with 🔥 about 12 hours ago

📣 I just published a free course on Reinforcement Learning Environments for Language Models! 📌 COURSE: https://github.com/anakin87/llm-rl-environments-lil-course Over the past year, we've seen a shift in LLM Post-Training. Previously, Supervised Fine-Tuning was the most important part: making models imitate curated Question-Answer pairs. Now we also have Reinforcement Learning with Verifiable Rewards. With techniques like GRPO, models can learn through trial and error in dynamic environments. They can climb to new heights without relying on expensively prepared data. But what actually are these environments in practice❓ And how do you build them effectively❓ Fascinated by these concepts, I spent time exploring this space through experiments, post-training Small Language Models. I've packaged everything I learned into this short course. What you'll learn 🔹 Agents, Environments, and LLMs: how to map Reinforcement Learning concepts to the LLM domain 🔹 How to use Verifiers (open-source library by Prime Intellect) to build RL environments as software artifacts 🔹 Common patterns: How to build single-turn, multi-turn, and tool-use environments 🔹 Hands-on: turn a small language model (LFM2-2.6B by LiquidAI) into a Tic Tac Toe master 🔸 Build the game Environment 🔸 Use it to generate synthetic data for SFT warm-up 🔸 Group-based Reinforcement Learning If you're interested in building "little worlds" where LLMs can learn, this course is for you. --- 🤗🕹️ 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

reacted to anakin87's post with ❤️ about 12 hours ago

📣 I just published a free course on Reinforcement Learning Environments for Language Models! 📌 COURSE: https://github.com/anakin87/llm-rl-environments-lil-course Over the past year, we've seen a shift in LLM Post-Training. Previously, Supervised Fine-Tuning was the most important part: making models imitate curated Question-Answer pairs. Now we also have Reinforcement Learning with Verifiable Rewards. With techniques like GRPO, models can learn through trial and error in dynamic environments. They can climb to new heights without relying on expensively prepared data. But what actually are these environments in practice❓ And how do you build them effectively❓ Fascinated by these concepts, I spent time exploring this space through experiments, post-training Small Language Models. I've packaged everything I learned into this short course. What you'll learn 🔹 Agents, Environments, and LLMs: how to map Reinforcement Learning concepts to the LLM domain 🔹 How to use Verifiers (open-source library by Prime Intellect) to build RL environments as software artifacts 🔹 Common patterns: How to build single-turn, multi-turn, and tool-use environments 🔹 Hands-on: turn a small language model (LFM2-2.6B by LiquidAI) into a Tic Tac Toe master 🔸 Build the game Environment 🔸 Use it to generate synthetic data for SFT warm-up 🔸 Group-based Reinforcement Learning If you're interested in building "little worlds" where LLMs can learn, this course is for you. --- 🤗🕹️ 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

liked a Space about 23 hours ago

sam-motamed/VOID

View all activity

Organizations

DedeProGames 's Spaces 1

GRM2

Chat with GRM2, a powerfull reasoning 3b params model