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Computation and Language - GLM-4.5 Agentic, Reasoning, and Coding (ARC) Foundation Models
Alright learning crew, welcome back to PaperLedge! Ernis here, ready to dive into some seriously cool AI research that I think you're gonna love. Today, we're cracking open a paper about a new large language model called GLM-4.5. Now, I know "large language model" sounds intimidating, but trust me, the core idea is pretty straightforward.
Think of it like this: imagine you're trying to learn a new language. You could try to memorize every single word and grammar rule, right? That's kind of like how older AI models worked. But what if you could learn by seeing how people actually use the language, by reading tons of books, articles, and conversations? That’s the approach of large language models. They learn by absorbing massive amounts of text data. GLM-4.5 took this to the next level!
This particular model is a Mixture-of-Experts (MoE). That's a fancy term, but it basically means GLM-4.5 has a bunch of specialized "mini-brains" inside of it. It’s like having a team of experts on hand for different tasks. One might be great at coding, another at logical reasoning, and another at creative writing. When you ask GLM-4.5 a question, it figures out which "expert" is best suited to answer it. This version boasts 355 billion total parameters (think of parameters as connections in the brain), but only 32 billion are activated at any given time, which is pretty efficient.
The developers trained GLM-4.5 on a staggering 23 trillion tokens. Imagine reading every book, news article, and website you could get your hands on – that's the scale we're talking about! This massive training dataset, combined with clever techniques like expert model iteration and reinforcement learning, allows GLM-4.5 to perform exceptionally well in areas like:
And the results are impressive! It scored 70.1% on TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified. These are benchmarks that test its abilities in those three areas. In fact, GLM-4.5 ranks 3rd overall among all evaluated models and 2nd on agentic benchmarks, while using fewer parameters than many of its competitors. That means it's not just smart, it's also relatively efficient!
"GLM-4.5 achieves strong performance across agentic, reasoning, and coding (ARC) tasks... with much fewer parameters than several competitors."
Here's why this research matters, and why you should care:
They even released a smaller, more compact version called GLM-4.5-Air (106B parameters), making it even easier to experiment with. This is a big deal!
So, as we wrap up this introduction, here are a couple of things I'm pondering:
Food for thought, right? That's all for this episode of PaperLedge. I hope you found this breakdown of GLM-4.5 informative and engaging. Until next time, keep learning!
Credit to Paper authors: GLM-4. 5 Team, :, Aohan Zeng, Xin Lv, Qinkai Zheng, Zhenyu Hou, Bin Chen, Chengxing Xie, Cunxiang Wang, Da Yin, Hao Zeng, Jiajie Zhang, Kedong Wang, Lucen Zhong, Mingdao Liu, Rui Lu, Shulin Cao, Xiaohan Zhang, Xuancheng Huang, Yao Wei, Yean Cheng, Yifan An, Yilin Niu, Yuanhao Wen, Yushi Bai, Zhengxiao Du, Zihan Wang, Zilin Zhu, Bohan Zhang, Bosi Wen, Bowen Wu, Bowen Xu, Can Huang, Casey Zhao, Changpeng Cai, Chao Yu, Chen Li, Chendi Ge, Chenghua Huang, Chenhui Zhang, Chenxi Xu, Chenzheng Zhu, Chuang Li, Congfeng Yin, Daoyan Lin, Dayong Yang, Dazhi Jiang, Ding Ai, Erle Zhu, Fei Wang, Gengzheng Pan, Guo Wang, Hailong Sun, Haitao Li, Haiyang Li, Haiyi Hu, Hanyu Zhang, Hao Peng, Hao Tai, Haoke Zhang, Haoran Wang, Haoyu Yang, He Liu, He Zhao, Hongwei Liu, Hongxi Yan, Huan Liu, Huilong Chen, Ji Li, Jiajing Zhao, Jiamin Ren, Jian Jiao, Jiani Zhao, Jianyang Yan, Jiaqi Wang, Jiayi Gui, Jiayue Zhao, Jie Liu, Jijie Li, Jing Li, Jing Lu, Jingsen Wang, Jingwei Yuan, Jingxuan Li, Jingzhao Du, Jinhua Du, Jinxin Liu, Junkai Zhi, Junli Gao, Ke Wang, Lekang Yang, Liang Xu, Lin Fan, Lindong Wu, Lintao Ding, Lu Wang, Man Zhang, Minghao Li, Minghuan Xu, Mingming Zhao, Mingshu Zhai, Pengfan Du, Qian Dong, Shangde Lei, Shangqing Tu, Shangtong Yang, Shaoyou Lu, Shijie Li, Shuang Li, Shuang-Li, Shuxun Yang, Sibo Yi, Tianshu Yu, Wei Tian, Weihan Wang, Wenbo Yu, Weng Lam Tam, Wenjie Liang, Wentao Liu, Xiao Wang, Xiaohan Jia, Xiaotao Gu, Xiaoying Ling, Xin Wang, Xing Fan, Xingru Pan, Xinyuan Zhang, Xinze Zhang, Xiuqing Fu, Xunkai Zhang, Yabo Xu, Yandong Wu, Yida Lu, Yidong Wang, Yilin Zhou, Yiming Pan, Ying Zhang, Yingli Wang, Yingru Li, Yinpei Su, Yipeng Geng, Yitong Zhu, Yongkun Yang, Yuhang Li, Yuhao Wu, Yujiang Li, Yunan Liu, Yunqing Wang, Yuntao Li, Yuxuan Zhang, Zezhen Liu, Zhen Yang, Zhengda Zhou, Zhongpei Qiao, Zhuoer Feng, Zhuorui Liu, Zichen Zhang, Zihan Wang, Zijun Yao, Zikang Wang, Ziqiang Liu, Ziwei Chai, Zixuan Li, Zuodong Zhao, Wenguang Chen, Jidong Zhai, Bin Xu, Minlie Huang, Hongning Wang, Juanzi Li, Yuxiao Dong, Jie Tang
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By ernestasposkusAlright learning crew, welcome back to PaperLedge! Ernis here, ready to dive into some seriously cool AI research that I think you're gonna love. Today, we're cracking open a paper about a new large language model called GLM-4.5. Now, I know "large language model" sounds intimidating, but trust me, the core idea is pretty straightforward.
Think of it like this: imagine you're trying to learn a new language. You could try to memorize every single word and grammar rule, right? That's kind of like how older AI models worked. But what if you could learn by seeing how people actually use the language, by reading tons of books, articles, and conversations? That’s the approach of large language models. They learn by absorbing massive amounts of text data. GLM-4.5 took this to the next level!
This particular model is a Mixture-of-Experts (MoE). That's a fancy term, but it basically means GLM-4.5 has a bunch of specialized "mini-brains" inside of it. It’s like having a team of experts on hand for different tasks. One might be great at coding, another at logical reasoning, and another at creative writing. When you ask GLM-4.5 a question, it figures out which "expert" is best suited to answer it. This version boasts 355 billion total parameters (think of parameters as connections in the brain), but only 32 billion are activated at any given time, which is pretty efficient.
The developers trained GLM-4.5 on a staggering 23 trillion tokens. Imagine reading every book, news article, and website you could get your hands on – that's the scale we're talking about! This massive training dataset, combined with clever techniques like expert model iteration and reinforcement learning, allows GLM-4.5 to perform exceptionally well in areas like:
And the results are impressive! It scored 70.1% on TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified. These are benchmarks that test its abilities in those three areas. In fact, GLM-4.5 ranks 3rd overall among all evaluated models and 2nd on agentic benchmarks, while using fewer parameters than many of its competitors. That means it's not just smart, it's also relatively efficient!
"GLM-4.5 achieves strong performance across agentic, reasoning, and coding (ARC) tasks... with much fewer parameters than several competitors."
Here's why this research matters, and why you should care:
They even released a smaller, more compact version called GLM-4.5-Air (106B parameters), making it even easier to experiment with. This is a big deal!
So, as we wrap up this introduction, here are a couple of things I'm pondering:
Food for thought, right? That's all for this episode of PaperLedge. I hope you found this breakdown of GLM-4.5 informative and engaging. Until next time, keep learning!
Credit to Paper authors: GLM-4. 5 Team, :, Aohan Zeng, Xin Lv, Qinkai Zheng, Zhenyu Hou, Bin Chen, Chengxing Xie, Cunxiang Wang, Da Yin, Hao Zeng, Jiajie Zhang, Kedong Wang, Lucen Zhong, Mingdao Liu, Rui Lu, Shulin Cao, Xiaohan Zhang, Xuancheng Huang, Yao Wei, Yean Cheng, Yifan An, Yilin Niu, Yuanhao Wen, Yushi Bai, Zhengxiao Du, Zihan Wang, Zilin Zhu, Bohan Zhang, Bosi Wen, Bowen Wu, Bowen Xu, Can Huang, Casey Zhao, Changpeng Cai, Chao Yu, Chen Li, Chendi Ge, Chenghua Huang, Chenhui Zhang, Chenxi Xu, Chenzheng Zhu, Chuang Li, Congfeng Yin, Daoyan Lin, Dayong Yang, Dazhi Jiang, Ding Ai, Erle Zhu, Fei Wang, Gengzheng Pan, Guo Wang, Hailong Sun, Haitao Li, Haiyang Li, Haiyi Hu, Hanyu Zhang, Hao Peng, Hao Tai, Haoke Zhang, Haoran Wang, Haoyu Yang, He Liu, He Zhao, Hongwei Liu, Hongxi Yan, Huan Liu, Huilong Chen, Ji Li, Jiajing Zhao, Jiamin Ren, Jian Jiao, Jiani Zhao, Jianyang Yan, Jiaqi Wang, Jiayi Gui, Jiayue Zhao, Jie Liu, Jijie Li, Jing Li, Jing Lu, Jingsen Wang, Jingwei Yuan, Jingxuan Li, Jingzhao Du, Jinhua Du, Jinxin Liu, Junkai Zhi, Junli Gao, Ke Wang, Lekang Yang, Liang Xu, Lin Fan, Lindong Wu, Lintao Ding, Lu Wang, Man Zhang, Minghao Li, Minghuan Xu, Mingming Zhao, Mingshu Zhai, Pengfan Du, Qian Dong, Shangde Lei, Shangqing Tu, Shangtong Yang, Shaoyou Lu, Shijie Li, Shuang Li, Shuang-Li, Shuxun Yang, Sibo Yi, Tianshu Yu, Wei Tian, Weihan Wang, Wenbo Yu, Weng Lam Tam, Wenjie Liang, Wentao Liu, Xiao Wang, Xiaohan Jia, Xiaotao Gu, Xiaoying Ling, Xin Wang, Xing Fan, Xingru Pan, Xinyuan Zhang, Xinze Zhang, Xiuqing Fu, Xunkai Zhang, Yabo Xu, Yandong Wu, Yida Lu, Yidong Wang, Yilin Zhou, Yiming Pan, Ying Zhang, Yingli Wang, Yingru Li, Yinpei Su, Yipeng Geng, Yitong Zhu, Yongkun Yang, Yuhang Li, Yuhao Wu, Yujiang Li, Yunan Liu, Yunqing Wang, Yuntao Li, Yuxuan Zhang, Zezhen Liu, Zhen Yang, Zhengda Zhou, Zhongpei Qiao, Zhuoer Feng, Zhuorui Liu, Zichen Zhang, Zihan Wang, Zijun Yao, Zikang Wang, Ziqiang Liu, Ziwei Chai, Zixuan Li, Zuodong Zhao, Wenguang Chen, Jidong Zhai, Bin Xu, Minlie Huang, Hongning Wang, Juanzi Li, Yuxiao Dong, Jie Tang