The Paradigm
9 days ago
- #AI
- #Machine Learning
- #Reinforcement Learning
- AI breakthroughs like AlphaGo, AlphaStar, and ChatGPT combine large-scale data gathering (self-supervised or imitation learning) with reinforcement learning (RL) for performance refinement.
- Recent trends show a shift from narrow RL optimization (e.g., mastering a single game) to general RL optimization (e.g., solving math problems, writing code, playing multiple games).
- General RL models outperform self-supervised learning (SSL) models in benchmarks, particularly in reasoning and error correction.
- Policy learning in RL involves teaching models to generate useful trajectories (sequences of actions and observations) to achieve goals, akin to human subroutines.
- Error correction is a key strength of RL models, as they learn to review and correct mistakes, unlike SSL models which struggle with unexpected failures.
- Intentionality and refinement in RL involve distilling complex cycles of observation, planning, and action into simpler, more efficient processes.
- Reasoning models use long token sequences and knowledge retrieval to improve answers, with general RL optimization enhancing performance across diverse tasks.
- The future of AI hinges on enabling models to interact with the world effectively and measuring task completion robustly, though these challenges remain difficult.