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Top Comments (4)
GLM was heavily distilled from Claude, basically GLM has learned from Claude's mistakes, that is why the Min task% for GLM is higher 😄
I would add a fifth social-type dimension, to adapt the research output to the purpose of the human interlocutors
The only way for AI to self-improve before 2028 is if inferences in computer coding and mathematics lead to discoveries that improve the architecture of AI models. By implementing continuous learning and drawing on the various structures of the human psyche, AI will be caught in a self-sustaining improvement loop. (Deepmind, Yann LeCun...)
Looking at the paper, the minimum/maximum tasks measures don't appear to be the min and max range (variation) for a given task, but the best and worst performance across all 100 tasks which mixes or conflates capability and variation. ("Minimum task score and Maximum task score are the lowest and highest task-level scores for the model. ") However, variance (e.g. as a histogram) per model-task would be a great to know!
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Top Comments (4)
GLM was heavily distilled from Claude, basically GLM has learned from Claude's mistakes, that is why the Min task% for GLM is higher 😄
I would add a fifth social-type dimension, to adapt the research output to the purpose of the human interlocutors
The only way for AI to self-improve before 2028 is if inferences in computer coding and mathematics lead to discoveries that improve the architecture of AI models. By implementing continuous learning and drawing on the various structures of the human psyche, AI will be caught in a self-sustaining improvement loop. (Deepmind, Yann LeCun...)
Looking at the paper, the minimum/maximum tasks measures don't appear to be the min and max range (variation) for a given task, but the best and worst performance across all 100 tasks which mixes or conflates capability and variation. ("Minimum task score and Maximum task score are the lowest and highest task-level scores for the model. ") However, variance (e.g. as a histogram) per model-task would be a great to know!