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#metatextgrad 搜尋結果

Kevin_GuoweiXu's profile picture. Undergraduate student at Yao Class (Tsinghua University), interested in Language Models and Reinforcement Learning
Guowei Xu @ NeurIPS 2025
@Kevin_GuoweiXu
年11月25日

Introducing #metaTextGrad🌟: a meta-optimization framework built on #TextGrad , designed to improve existing LLM optimizers by aligning them more closely with specific tasks. 📰 NeurIPS 2025 paper: openreview.net/pdf?id=10s01Yr… 🧑‍💻Code: github.com/zou-group/meta… 📚 Slides:…

Kevin_GuoweiXu's tweet image. Introducing #metaTextGrad🌟: a meta-optimization framework built on #TextGrad , designed to improve existing LLM optimizers by aligning them more closely with specific tasks. 📰 NeurIPS 2025 paper: openreview.net/pdf?id=10s01Yr… 🧑‍💻Code: github.com/zou-group/meta… 📚 Slides:…
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james_y_zou's profile picture. @Stanford professor. Chan-Zuckerberg investigator. Sloan Fellow. Overton Prize. @togethercompute. AI for science + health.
James Zou
 @james_y_zou
年11月25日

2/ Existing LM optimizers are broad and generic. #metaTextGrad automatically adapts them to specific tasks, greatly improving performance and efficiency. 📰 #NeurIPS2025 paper: openreview.net/pdf?id=10s01Yr… 🧑‍💻 Code: github.com/zou-group/meta… 📖 Slides: neurips.cc/media/neurips-…

james_y_zou's tweet image. 2/ Existing LM optimizers are broad and generic. #metaTextGrad automatically adapts them to specific tasks, greatly improving performance and efficiency. 📰 #NeurIPS2025 paper: openreview.net/pdf?id=10s01Yr… 🧑‍💻 Code: github.com/zou-group/meta… 📖 Slides: neurips.cc/media/neurips-…
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Kevin_GuoweiXu's profile picture. Undergraduate student at Yao Class (Tsinghua University), interested in Language Models and Reinforcement Learning
Guowei Xu @ NeurIPS 2025
@Kevin_GuoweiXu
年11月25日

(6/8) Across various reasoning datasets, #metaTextGrad shows a marked improvement in performance over baselines.

Kevin_GuoweiXu's tweet image. (6/8) Across various reasoning datasets, #metaTextGrad shows a marked improvement in performance over baselines.
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Kevin_GuoweiXu's profile picture. Undergraduate student at Yao Class (Tsinghua University), interested in Language Models and Reinforcement Learning
Guowei Xu @ NeurIPS 2025
@Kevin_GuoweiXu
年11月25日

(3/8) The optimization in #metaTextGrad is divided into an inner loop and an outer loop. In the inner loop, an LLM optimizer optimizes programs, and its optimization results indicate the quality of the optimizer and how well it aligns with the task.

Kevin_GuoweiXu's tweet image. (3/8) The optimization in #metaTextGrad is divided into an inner loop and an outer loop. In the inner loop, an LLM optimizer optimizes programs, and its optimization results indicate the quality of the optimizer and how well it aligns with the task.
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james_y_zou's profile picture. @Stanford professor. Chan-Zuckerberg investigator. Sloan Fellow. Overton Prize. @togethercompute. AI for science + health.
James Zou
 @james_y_zou
年11月25日

2/ Existing LM optimizers are broad and generic. #metaTextGrad automatically adapts them to specific tasks, greatly improving performance and efficiency. 📰 #NeurIPS2025 paper: openreview.net/pdf?id=10s01Yr… 🧑‍💻 Code: github.com/zou-group/meta… 📖 Slides: neurips.cc/media/neurips-…

james_y_zou's tweet image. 2/ Existing LM optimizers are broad and generic. #metaTextGrad automatically adapts them to specific tasks, greatly improving performance and efficiency. 📰 #NeurIPS2025 paper: openreview.net/pdf?id=10s01Yr… 🧑‍💻 Code: github.com/zou-group/meta… 📖 Slides: neurips.cc/media/neurips-…
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Kevin_GuoweiXu's profile picture. Undergraduate student at Yao Class (Tsinghua University), interested in Language Models and Reinforcement Learning
Guowei Xu @ NeurIPS 2025
@Kevin_GuoweiXu
年11月25日

(6/8) Across various reasoning datasets, #metaTextGrad shows a marked improvement in performance over baselines.

Kevin_GuoweiXu's tweet image. (6/8) Across various reasoning datasets, #metaTextGrad shows a marked improvement in performance over baselines.
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Kevin_GuoweiXu's profile picture. Undergraduate student at Yao Class (Tsinghua University), interested in Language Models and Reinforcement Learning
Guowei Xu @ NeurIPS 2025
@Kevin_GuoweiXu
年11月25日

(3/8) The optimization in #metaTextGrad is divided into an inner loop and an outer loop. In the inner loop, an LLM optimizer optimizes programs, and its optimization results indicate the quality of the optimizer and how well it aligns with the task.

Kevin_GuoweiXu's tweet image. (3/8) The optimization in #metaTextGrad is divided into an inner loop and an outer loop. In the inner loop, an LLM optimizer optimizes programs, and its optimization results indicate the quality of the optimizer and how well it aligns with the task.
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Kevin_GuoweiXu's profile picture. Undergraduate student at Yao Class (Tsinghua University), interested in Language Models and Reinforcement Learning
Guowei Xu @ NeurIPS 2025
@Kevin_GuoweiXu
年11月25日

Introducing #metaTextGrad🌟: a meta-optimization framework built on #TextGrad , designed to improve existing LLM optimizers by aligning them more closely with specific tasks. 📰 NeurIPS 2025 paper: openreview.net/pdf?id=10s01Yr… 🧑‍💻Code: github.com/zou-group/meta… 📚 Slides:…

Kevin_GuoweiXu's tweet image. Introducing #metaTextGrad🌟: a meta-optimization framework built on #TextGrad , designed to improve existing LLM optimizers by aligning them more closely with specific tasks. 📰 NeurIPS 2025 paper: openreview.net/pdf?id=10s01Yr… 🧑‍💻Code: github.com/zou-group/meta… 📚 Slides:…
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未找到 "#metatextgrad" 的結果
Kevin_GuoweiXu's profile picture. Undergraduate student at Yao Class (Tsinghua University), interested in Language Models and Reinforcement Learning
Guowei Xu @ NeurIPS 2025
@Kevin_GuoweiXu
年11月25日

Introducing #metaTextGrad🌟: a meta-optimization framework built on #TextGrad , designed to improve existing LLM optimizers by aligning them more closely with specific tasks. 📰 NeurIPS 2025 paper: openreview.net/pdf?id=10s01Yr… 🧑‍💻Code: github.com/zou-group/meta… 📚 Slides:…

Kevin_GuoweiXu's tweet image. Introducing #metaTextGrad🌟: a meta-optimization framework built on #TextGrad , designed to improve existing LLM optimizers by aligning them more closely with specific tasks. 📰 NeurIPS 2025 paper: openreview.net/pdf?id=10s01Yr… 🧑‍💻Code: github.com/zou-group/meta… 📚 Slides:…
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Kevin_GuoweiXu's profile picture. Undergraduate student at Yao Class (Tsinghua University), interested in Language Models and Reinforcement Learning
Guowei Xu @ NeurIPS 2025
@Kevin_GuoweiXu
年11月25日

(6/8) Across various reasoning datasets, #metaTextGrad shows a marked improvement in performance over baselines.

Kevin_GuoweiXu's tweet image. (6/8) Across various reasoning datasets, #metaTextGrad shows a marked improvement in performance over baselines.
1
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james_y_zou's profile picture. @Stanford professor. Chan-Zuckerberg investigator. Sloan Fellow. Overton Prize. @togethercompute. AI for science + health.
James Zou
 @james_y_zou
年11月25日

2/ Existing LM optimizers are broad and generic. #metaTextGrad automatically adapts them to specific tasks, greatly improving performance and efficiency. 📰 #NeurIPS2025 paper: openreview.net/pdf?id=10s01Yr… 🧑‍💻 Code: github.com/zou-group/meta… 📖 Slides: neurips.cc/media/neurips-…

james_y_zou's tweet image. 2/ Existing LM optimizers are broad and generic. #metaTextGrad automatically adapts them to specific tasks, greatly improving performance and efficiency. 📰 #NeurIPS2025 paper: openreview.net/pdf?id=10s01Yr… 🧑‍💻 Code: github.com/zou-group/meta… 📖 Slides: neurips.cc/media/neurips-…
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Kevin_GuoweiXu's profile picture. Undergraduate student at Yao Class (Tsinghua University), interested in Language Models and Reinforcement Learning
Guowei Xu @ NeurIPS 2025
@Kevin_GuoweiXu
年11月25日

(3/8) The optimization in #metaTextGrad is divided into an inner loop and an outer loop. In the inner loop, an LLM optimizer optimizes programs, and its optimization results indicate the quality of the optimizer and how well it aligns with the task.

Kevin_GuoweiXu's tweet image. (3/8) The optimization in #metaTextGrad is divided into an inner loop and an outer loop. In the inner loop, an LLM optimizer optimizes programs, and its optimization results indicate the quality of the optimizer and how well it aligns with the task.
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