This article details a method for training large language models (LLMs) for code generation using a secure, local WebAssembly-based code interpreter and reinforcement learning with Group Relative Policy Optimization (GRPO). It covers the setup, training process, evaluation, and potential next steps.
The article explores the DeepSeek-R1 models, focusing on how reinforcement learning (RL) is used to develop advanced reasoning capabilities in AI. It discusses the DeepSeek-R1-Zero model, which learns reasoning without supervised fine-tuning, and the DeepSeek-R1 model, which combines RL with a small amount of supervised data for improved performance. The article highlights the use of distillation to transfer reasoning patterns to smaller models and addresses challenges and future directions in RL for AI.
