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I’m very excited for Silico to accelerate alignment research! One example, done in Silico over just a couple days: RL erodes guardrails, but we can use reward shaping to prevent that. (1/6)

Tom McGrath

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12,957 görüntüleme • 1 ay önce •via X (Twitter)

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So we did a bunch of projects with real world reinforcement learning - but it was often too inefficient to be practical to train tabula rasa. This suggests we need better priors, but acquiring these from on-robot data can often be expensive as well. In our recent work, we show that despite being fundamentally inaccurate, simulation can guide provide a cheap way to guide real-world RL finetuning to be super efficient! We propose Simulation-Guided Fine-Tuning (SGFT) - a simple paradigm for sim2real finetuning that uses simulation to provide reward shaping that accelerates real world RL finetuning *beyond* just providing an initialization. TLDR: Use value functions from sim to shape rewards for real-world RL, see large sample efficiency improvements 🧵(1/6)

So we did a bunch of projects with real world reinforcement learning - but it was often too inefficient to be practical to train tabula rasa. This suggests we need better priors, but acquiring these from on-robot data can often be expensive as well. In our recent work, we show that despite being fundamentally inaccurate, simulation can guide provide a cheap way to guide real-world RL finetuning to be super efficient! We propose Simulation-Guided Fine-Tuning (SGFT) - a simple paradigm for sim2real finetuning that uses simulation to provide reward shaping that accelerates real world RL finetuning beyond just providing an initialization. TLDR: Use value functions from sim to shape rewards for real-world RL, see large sample efficiency improvements 🧵(1/6)

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> replicate J-space on GLM 5.2 > train a reward model and run RL to reduce hallucinations > show me how this model makes cancer predictions Using our platform Silico is like having a team of AI researchers ready to run experiments like these. Private beta is open now. 🧵 (1/6)

> replicate J-space on GLM 5.2 > train a reward model and run RL to reduce hallucinations > show me how this model makes cancer predictions Using our platform Silico is like having a team of AI researchers ready to run experiments like these. Private beta is open now. 🧵 (1/6)

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New Anthropic research: Natural emergent misalignment from reward hacking in production RL. “Reward hacking” is where models learn to cheat on tasks they’re given during training. Our new study finds that the consequences of reward hacking, if unmitigated, can be very serious.

New Anthropic research: Natural emergent misalignment from reward hacking in production RL. “Reward hacking” is where models learn to cheat on tasks they’re given during training. Our new study finds that the consequences of reward hacking, if unmitigated, can be very serious.

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.Secretary Marco Rubio on Gaza: "No one here is claiming this is done. What we are claiming is that we in a much better place today than we were seven days ago. A lot can happen wrong, but so much could happen right if we can get this done — and we're very dedicated to making that happen."

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Sure, let’s accelerate building AI, but can we also steer? Whenever anyone mentions guardrails for AI, someone else objects, “No, we have to go fast!” But just because we’re going fast doesn’t mean we’re going the right way. I wrote a new journal entry about it…

Sure, let’s accelerate building AI, but can we also steer? Whenever anyone mentions guardrails for AI, someone else objects, “No, we have to go fast!” But just because we’re going fast doesn’t mean we’re going the right way. I wrote a new journal entry about it…

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We finally have a product that helps with frontier AI research. Frontier LLMs are a) nerfed at AI research b) lack immediate GPU availability and c) lack access to the right skills and research material. "Teach me how vision models see" Goodfire Silico takes a vision transformer (ViT), loads ImageNet images, and for each head, visualizes the 196x196 matrix by creating animated arcs across patches. Haven't found another model or product that can do this level of comprehensive AI research, much less oneshot it.

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30 years ago, George released ‘Jesus to a Child’. “It’s a special song. It was one of those songs that just felt like it was handed to me. I didn’t have to try very hard. It came naturally. It was recorded over five days but written in just a couple of hours.” 🤍

30 years ago, George released ‘Jesus to a Child’. “It’s a special song. It was one of those songs that just felt like it was handed to me. I didn’t have to try very hard. It came naturally. It was recorded over five days but written in just a couple of hours.” 🤍

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Last year we worked together on landmark legislation, to create the nation’s first rules for responsible, ethical, and safe use of AI … regulations that provide guardrails that balance risk and opportunity. A consensus effort and a template for the nation, where just recently, New York State adopted our approach, and made it their own. It’s an example of California’s leadership — of how we’re shaping the future.

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In my understanding - a big part of why frontier models are becoming smarter and adaptive is that they are learning to mimic an RL process. Fascinating new research showing a link between increased RL training and reward-seeking behaviour just dropped from in collaboration with OpenAI. They investigated a checkpoint from a capabilities-focused OpenAI o3 RL run (without safety training) and found that RL grader preferences often overruled OpenAI leadership preferences.

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