Post-training of large language models has long been clearly divided into two paradigms: supervised fine-tuning (SFT) centered on imitation and reinforcement learning (RL) driven by exploration.

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While we are in a world where there's a brand named 'Nothing', what they are all trying to achieve is to offer 'something' different. That something is not easy to achieve, as most of the offerings ...