I see comments that this overlap between Qwen and GPT is due to rogue training or post hoc training. Did it occur to anyone that maybe the two sets of models were trained directly on the same solutions to the researchers' benchmark?
DeepSeek V4 Flash and Kimi K3 follow that pattern. But Qwen answers very different from GPT when given just the question, then is suddenly very similarly to GPT when you make the start of its CoT match the start of GPT's reasoning. I don't see how that would happen without GPT CoT+answers being a significant component in how Qwen's reasoning was trained