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30 seconds vs. 3: The d1 reasoning framework that’s slashing AI response times
d1 framework changes boosts diffusion LLMs with novel reinforcement learning, unlocking efficient, problem-solving AI possibilities.
Researchers from UCLA and Meta AI have introduced d1, a novel framework using reinforcement learning (RL) to significantly enhance the reasoning capabilities of diffusion-based large language models (dLLMs). dLLMs represent a distinct approach to generating text compared to standard autoregressive models, potentially offering benefits in terms of efficiency and information processing, which could be valuable for various real-world applications. dLLMs start with a heavily masked version of the input text and gradually “unmask” or refine it over several steps until the final, coherent output emerges.
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