Viewing enriched Twitter post
๐๐ถ๐ฎ๐ข๐ฏ๐ด ๐ต๐ฉ๐ช๐ฏ๐ฌ ๐ง๐ญ๐ถ๐ช๐ฅ๐ญ๐บโ๐ฏ๐ข๐ท๐ช๐จ๐ข๐ต๐ช๐ฏ๐จ ๐ข๐ฃ๐ด๐ต๐ณ๐ข๐ค๐ต ๐ค๐ฐ๐ฏ๐ค๐ฆ๐ฑ๐ต๐ด ๐ฆ๐ง๐ง๐ฐ๐ณ๐ต๐ญ๐ฆ๐ด๐ด๐ญ๐บ, ๐ง๐ณ๐ฆ๐ฆ ๐ง๐ณ๐ฐ๐ฎ ๐ณ๐ช๐จ๐ช๐ฅ ๐ญ๐ช๐ฏ๐จ๐ถ๐ช๐ด๐ต๐ช๐ค ๐ฃ๐ฐ๐ถ๐ฏ๐ฅ๐ข๐ณ๐ช๐ฆ๐ด. But current reasoning models remain constrained by discrete tokens, limiting their fullโฆ https://t.co/yt1KfjrNEO
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Improved accuracy on math & code benchmarks by up to 2.48% (pass@1).\nโ
Reduced token usage by up to 22.4%, making reasoning models both smarter and more efficient.",
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"text": "Good question! \n\nCOCONUT utilizes the last hidden state of the LLM as a continuous representation. It must train LLM to do that because of the misalignment of the hidden state space and embedding space. \n\nSoft Thinking uses soft concept tokens that combine embeddings in LLM's native embedding space. Therefore, it is completely training-free and can be plugged into all the SOTA reasoning models. \n\nTechnically, they are not comparable, as one is a training method, and the other is a training-free method. As shown in Table 3 of our paper, the training-free version of COCONUT would not work.",
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"full_text": "@reasoningTokens @XuehaiH @zhenzhangzz @_akhaliq @arankomatsuzaki ๐Also, see more discussions in the Related Work and Experiments section. \n\nhttps://t.co/cs86cSW1Iz",
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