Discovery of the reward function for embodied reinforcement learning agents - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9WWlNBeGY4V0haaENNSElrQm5kbTJpNnJwdU9VaXZlTGZ2MFFOTm1KbGpZckVQTU9HQnBoRFFfLUV0U2dvS1QzTkFFX0g5U1dfZkxEa1FHX1FvdHluYVVB?oc=5" target="_blank">Discovery of the reward function for embodied reinforcement learning agents</a> <font color="#6f6f6f">Nature</font>
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Predicting When RL Training Breaks Chain-of-Thought Monitorability
Crossposted from the DeepMind Safety Research Medium Blog . Read our full paper about this topic by Max Kaufmann, David Lindner, Roland S. Zimmermann, and Rohin Shah. Overseeing AI agents by reading their intermediate reasoning “scratchpad” is a promising tool for AI safety. This approach, known as Chain-of-Thought (CoT) monitoring, allows us to check what a model is thinking before it acts, often helping us catch concerning behaviors like reward hacking and scheming . However, CoT monitoring can fail if a model’s chain-of-thought is not a good representation of the reasoning process we want to monitor. For example, training LLMs with reinforcement learning (RL) to avoid outputting problematic reasoning can result in a model learning to hide such reasoning without actually removing problem
OpenClaw Nodes: Connecting Your AI Agent to Physical Devices
<p>Your AI agent lives on a gateway. The gateway talks to Slack, Discord, or Telegram. But what if you want the agent to see through a camera, grab your phone's location, snap a screenshot, or run a shell command on a remote server? That's what <strong>nodes</strong> are for.</p> <p>A node is a companion device — iOS, Android, macOS, or any headless Linux machine — that connects to the OpenClaw Gateway over WebSocket and exposes a command surface. Once paired, your agent can invoke those commands as naturally as any other tool call. No polling loops, no bespoke APIs. Just pairing and using.</p> <h2> What Is a Node? </h2> <p>In OpenClaw's architecture, the <strong>gateway</strong> is the always-on brain — it receives messages, runs the model, routes tool calls. A <strong>node</strong> is a
Predicting When RL Training Breaks Chain-of-Thought Monitorability
Crossposted from the DeepMind Safety Research Medium Blog . Read our full paper about this topic by Max Kaufmann, David Lindner, Roland S. Zimmermann, and Rohin Shah. Overseeing AI agents by reading their intermediate reasoning “scratchpad” is a promising tool for AI safety. This approach, known as Chain-of-Thought (CoT) monitoring, allows us to check what a model is thinking before it acts, often helping us catch concerning behaviors like reward hacking and scheming . However, CoT monitoring can fail if a model’s chain-of-thought is not a good representation of the reasoning process we want to monitor. For example, training LLMs with reinforcement learning (RL) to avoid outputting problematic reasoning can result in a model learning to hide such reasoning without actually removing problem
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