Part 8/11:
Deep Seek's emergence sets a precedent, suggesting that innovation may proliferate rapidly, incorporating insights from its preceding models. With the potential for widespread accessibility, it threatens to redefine benchmarks and performance standards across the AI realm, prompting a critical exploration of safety measures in AI model deployment.
Connecting the Dots: Reinforcement Learning and Reasoning Flaws
Deep Seek R1's training methodology utilizes reinforcement learning, focusing on rewarding correct outcomes rather than each individual reasoning step. This presents a stark contrast with traditional models that emphasize verifying every stage. Observations reveal that Deep Seek has enabled models to self-correct and evolve without hardcoding specific methodologies.