基于大语言模型的多目标强化学习实现Pareto最优RTL代码生成
arXiv:2610.26904v1 发布类型: new 摘要: 近期研究提出了带可验证奖励的强化学习(RLVR)方法,使LLM能从自然语言规范生成高质量RTL电路。现有方法优化电路PPA…
AI 摘要：要点
- arXiv:2610.26904v1 发布类型: new 摘要: 近期研究提出了带可验证奖励的强化学习(RLVR)方法,使LLM能从自然语言规范生成高质量RTL电路。现有方法优化电路PPA…
基于大语言模型的多目标强化学习实现Pareto最优RTL代码生成
相关文章
ENThe Problem with Jev: Can You Trust AI Decisions?
Will it rain tomorrow? It is decidedly so. Oh, really? How can you be so sure? Reply hazy. Try again. Okay, I'm trying again. How do you know it's going to rain? You seem pretty confident about that. Um. Better not tell you now. Okay. That's not helping. This is clearly not…
ENWhat Is Loop Engineering? Building Better AI Agents
Loop engineering is a term that's related to working with AI agents, and it's kind of like the the newer, cooler version of prompt engineering. But with prompt engineering, that's all about writing the one, perfect, ultimate prompt and then sending that prompt to a large…
谷歌刚刚推翻了 OpenAI 和 Anthropic 吗？
上周，天然气永远改变了世界。特斯拉最终两次展示了 Roadster 及其通过放冷气体飞行的能力。但是，当埃隆忙着表演他最伟大的魔术时，谷歌正在研制自己的惰性气体，Gemini 4 Argon，这是最……
