SFT vs RL ; Generalization Power in Foundation Models 🚀🤖
Supervised Fine-Tuning (SFT) vs. Reinforcement Learning (RL): Which drives better generalization in foundation models?
The research from Google / Google DeepMind dives deep into the impact of SFT and RL on models’ ability to handle unseen data across textual and visual reasoning tasks. Here’s what they discovered:
🚀 RL = Superior Generalization: RL significantly boosts performance on novel tasks, going beyond mere memorization.
🧩 SFT = Stability Anchor: While SFT focuses on learning from training data, it’s crucial for stabilizing outputs, creating the foundation for RL’s success.
👀 Enhanced Visual Recognition: RL sharpens multimodal models’ visual reasoning capabilities.
âš¡ More Inference-Time Computation = Better Generalization: Increasing computational effort during inference leads to noticeable performance gains.
This dynamic duo of SFT + RL shows that while RL pushes models to generalize, SFT keeps them grounded and reliable. The future of foundation models lies in balancing both approaches!
#AI #FoundationModels #ReinforcementLearning #SupervisedLearning #Generalization #MachineLearning #MultimodalAI #ResearchInsights #Google #AGI #GenAI
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