This is a GitHub repository for a Reinforcement Learning Tic Tac Toe project. It contains a single Python file, TicTacToeRL.py. The repository has 0 stars and 0 forks as of the current data.
DeepMind researchers propose a new 'streams' approach to AI development, focusing on experiential learning and autonomous interaction with the world, moving beyond the limitations of current large language models and potentially surpassing human intelligence.
Details the development and release of DeepCoder-14B-Preview, a 14B parameter code reasoning model achieving performance comparable to o3-mini through reinforcement learning, along with the dataset, code, and system optimizations used in its creation.
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.
ByteDance Research has released DAPO (Dynamic Sampling Policy Optimization), an open-source reinforcement learning system for LLMs, aiming to improve reasoning abilities and address reproducibility issues. DAPO includes innovations like Clip-Higher, Dynamic Sampling, Token-level Policy Gradient Loss, and Overlong Reward Shaping, achieving a score of 50 on the AIME 2024 benchmark with the Qwen2.5-32B model.
This article explores the application of reinforcement learning (RL) to Partial Differential Equations (PDEs), highlighting the complexity and challenges involved in controlling systems described by PDEs compared to Ordinary Differential Equations (ODEs). It discusses various approaches, including genetic programming and neural network-based methods, and presents experimental results on controlling PDE systems like the diffusion equation and Kuramoto–Sivashinsky equation. The author emphasizes the potential of machine learning to improve understanding and control of PDE systems, which have wide-ranging applications in fields like fluid dynamics, thermodynamics, and engineering.
Scaling Reinforcement Learning (RL) to surpass O1 in deep learning models
The article introduces a new approach to language modeling called test-time scaling, which enhances performance by utilizing additional compute resources during testing. The authors present a method involving a curated dataset and a technique called budget forcing to control compute usage, allowing models to double-check answers and improve reasoning. The approach is demonstrated with the Qwen2.5-32B-Instruct language model, showing significant improvements on competition math questions.
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.
DeepSeek-R1 is a groundbreaking AI model that uses reinforcement learning to teach large language models to reason, outperforming models like GPT4-o1 at a fraction of the computational cost.
