Exploration of three key reinforcement learning algorithms: Dynamic Programming (DP) for optimal policies in MDPs, Monte Carlo methods for learning from complete episodes without a model, and Temporal Difference (TD) learning for efficient updates from incomplete episodes using bootstrapping. Each method has unique characteristics and trade-offs essential for understanding advanced concepts in reinforcement learning.
Since OpenAI released its "o1-series" model, several teams have developed their own approaches to "deep thinking" models. DeepSeek introduced their o1-like model, DeepSeek-R1-Lite, while Qwen released QwQ-32B-Preview, and Intern launched Intern Thinker.
While this isn't the first blog about DSPy, I've noticed recent updates to the DSPy documentation and GitHub repository, including a new optimization method called BootstrapFinetune.
This isn't my first blog post on DSPy—I've written several before. However, I've noticed some recent updates to DSPy, and I'd rather not consult the documentation every time I want to build programs. So, I plan to jot down some basic DSPy concepts in this post. Additionally, I intend to use this document as external knowledge for GPT or Claude.
The blog introduces a novel method for evaluating LLM performance by having them play the Snake game, assessing their decision-making, planning, and strategy skills. The experiment tested several models, revealing that o1-mini performed best with a score of 11, while Claude models outperformed GPT models. The findings suggest that reinforcement learning significantly enhances LLMs' capabilities in dynamic decision-making tasks. Although preliminary, this approach highlights the potential of game-based assessments for deeper insights into LLM competencies, with recommendations for further testing across more models and scenarios.
