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So what's changed to make these models so much more capable? Quite a bit, actually. The past year has seen a flurry of advancements not only in model training, but also in the frameworks necessary to harness them. You may recall the market tumbling excitement around DeepSeek R1, which was among the first open-weights frontier models to employ reinforcement learning (RL) to replicate GPT-o1's chain-of-thought reasoning to trade time for higher quality outputs. This approach, now referred to as test-time scaling, has helped smaller models make up for their lower parameter counts by "thinking" fo