DeepSeek-R1 has demonstrated emergent reasoning capabilities—a phenomenon researchers call the "Aha Moment." Unlike traditional fine-tuning, this behavior wasn’t explicitly trained—the model figured it out on its own using Reinforcement Fine-Tuning (RFT).
This is the first time we’ve seen reinforcement learning unlock novel reasoning strategies in an LLM without human-labeled examples, making it a huge step forward for Chain-of-Thought fine-tuning.
Read our full breakdown on why Reinforcement Learning beats Supervised Fine-Tuning (SFT) for reasoning tasks:
👉 https://pbase.ai/RFT-vs-SFT
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