Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently
NeutralArtificial Intelligence
- Recent research has demonstrated that transformers can effectively learn sparse Boolean functions through two distinct approaches: Reinforcement Learning (RL) and Supervised Fine-Tuning (SFT). The study specifically analyzes the learning dynamics of a one-layer transformer when fine-tuned with Chain-of-Thought (CoT) capabilities, confirming the learnability of functions like k-PARITY, k-AND, and k-OR under both methods.
- This development is significant as it clarifies the theoretical underpinnings of how transformers can be trained to solve complex reasoning tasks, which is crucial for advancing artificial intelligence applications in various fields, including natural language processing and decision-making systems.
- The exploration of different training methodologies highlights ongoing debates in the AI community regarding the efficiency and effectiveness of RL versus SFT. Additionally, the findings contribute to a broader understanding of transformer architectures, which are increasingly being integrated into diverse applications, from vision tasks to particle physics, showcasing their versatility and potential for innovation.
— via World Pulse Now AI Editorial System
