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每日公开源信号
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2026-07-21
日期、来源、摘要、可信度和合规说明均来自通过完整审核门禁与哈希校验的每日公开快照；周报发布后由同期完整发布包接管。
Exa MCP search metadata: Scaling Agentic RL: High-Throughput Agentic Training with Tunix. # Scaling Agentic RL: High-Throughput Agentic Training with Tunix - Google Developers Blog Published: 2026-07-21T15:42:56+00:00 Scaling Agentic RL: High-Throughput Agentic Training with Tunix - Google Developers Blog # Scaling Agentic RL: High-Throughput Agentic Training with Tunix JULY 21, 2026 Tunix—Google’s post-training library—natively solves this bottleneck in its latest release, introducing an efficient, composable framework for training LLM agents at scale. Tunix keeps accelerators fully utilized on two fronts: specialized observability. While profilers like deep operator-level traces, their hi... 来源：Exa MCP。Exa MCP public search metadata via the Agent Reach documented route; no cookies, login state, or full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
打开公开来源RSS 公开元数据：Grabette: an open system to record robot-manipulation data。Update on GitHub Record your own manipulation tasks in minutes with a handheld gripper, turn them into robot-ready datasets automatically, and help grow an open, collaborative dataset for robot learning. The bottleneck isn’t the model. It’s the data. Robot learning has a supply problem. We have capable policy architectures (transformer-based VLAs, diffusion and flow-matching policies, and even world models) and the GPUs to train them. What we lack is large, diverse, real-world manipulation data. Teleoperating a robot to collect it can be expensive and demanding: first of all, it requires a robot. And depending on the teleo... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
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