Nate Mauer

Also live today: ARC Prize 2026 - 3 tracks, $2,000,000 in prizes available! Get involved: • Play a Game: arcprize.org/tasks/ls20 • Build Agents: docs.arcprize.org • Win Prizes: arcprize.org/competitions/2…

@RayDalio @RichardSSutton Whoa! This is macroeconomics, fiscal theory, political economy that can be modeled with reinforcement learning and game theory. It’s a Pareto frontier shift due to an efficiency collapse in a multi agent system with adversarial fiscal policies impacting international economics.

History and logic have made clear that sanctions reduce the demand for fiat currencies and debts denominated in them and support gold. Throughout history, before and during shooting wars, there have been financial and economic wars that we now call sanctions (which means cutting opponents off from money and needed goods). When there is a debtor-creditor relationship between opponents, the debtor choosing to not pay the debt service owed to the opponent creditor country has the beneficial effects of hurting the opponent financially and reducing its own debt service burdens. But it also has the detrimental effects of weakening the sanctioning/debtor country's currency and the value of its debt. When this occurs with the world's leading power and its reserve currency, the global monetary order is inevitably weakened. As a result, the holding and price of gold rise, as it is a non-fiat currency that remains securely held and universally accepted. #principles #raydalio

@dwarkesh_sp Interesting exploration on RL value and policy methods. Check out Charels Anderson’s dissertation from 1986, which extends the actor-critic architecture and explores the backprop neural network concept. cs.colostate.edu/~anderson/wp/p…

How does backprop work with RL? The virtue of backprop is that it updates EACH individual parameter in proportion to how much wiggling it affects the loss. This is only possible if you know how changing each parameter affects the loss function. But of course with RL this is not the case: the environment (and the reward it produces) is a whole separate system. You don’t have some continuous differentiable function which tells you how much wiggling each parameter affects the probability of falling off a cliff. The solutions are quite clever! Here are some ways to come up with a differentiable proxy for reward: Policy gradient methods: You can’t differentiate the reward with respect to the network. But you can differentiate the probabilities of different actions/tokens suggested by the network. So just make the loss = the (sum of negative log) probabilities WEIGHTED by the reward. Loss is higher when reward is lower, so the model learns to output tokens which lead to higher reward at higher probability. Q-learning: Again, reward is not differentiable with respect to the network. But you know what is? The network’s prediction of the reward. And you can update it based on how wrong that prediction was. Now that you can predict what actions will lead to what reward, your policy can simply just be to take the highest expected reward actions.

Today is the start of a new era of natively multimodal AI innovation. Today, we’re introducing the first Llama 4 models: Llama 4 Scout and Llama 4 Maverick — our most advanced models yet and the best in their class for multimodality. Llama 4 Scout • 17B-active-parameter model with 16 experts. • Industry-leading context window of 10M tokens. • Outperforms Gemma 3, Gemini 2.0 Flash-Lite and Mistral 3.1 across a broad range of widely accepted benchmarks. Llama 4 Maverick • 17B-active-parameter model with 128 experts. • Best-in-class image grounding with the ability to align user prompts with relevant visual concepts and anchor model responses to regions in the image. • Outperforms GPT-4o and Gemini 2.0 Flash across a broad range of widely accepted benchmarks. • Achieves comparable results to DeepSeek v3 on reasoning and coding — at half the active parameters. • Unparalleled performance-to-cost ratio with a chat version scoring ELO of 1417 on LMArena. These models are our best yet thanks to distillation from Llama 4 Behemoth, our most powerful model yet. Llama 4 Behemoth is still in training and is currently seeing results that outperform GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM-focused benchmarks. We’re excited to share more details about it even while it’s still in flight. Read more about the first Llama 4 models, including training and benchmarks ➡️ go.fb.me/gmjohs Download Llama 4 ➡️ go.fb.me/bwwhe9

Once a year, @gradypb and I sit down with our trusty AI collaborators 👾 and zoom out to the big picture on what’s happening in Generative AI. Here’s our 3rd annual take… 1: The foundation model layer of Generative AI (large, pre-trained language models) is stabilizing around key players like @OpenAI, @AnthropicAI, @Meta, and @GoogleDeepMind. What felt like a dynamic and volatile market a year ago is now stabilizing. 2: The next frontier is the development of the reasoning layer. o1 🍓 represents a significant advancement in general reasoning capabilities achieved through inference-time compute. This was Generative AI’s AlphaGo moment, achieved by deep RL for the first time in a general setting. 3: There’s a new scaling law in town: the more inference-time compute given to a model, the better it reasons. With whispers of diminishing marginal returns in the pre-training world, it’s deeply exciting to be staring down the starting line of a promising new scaling law. 4: State of the art models, and the AI applications built on top, will shift from "thinking fast" (rapid responses from pre-training) to "thinking slow" (reasoning at inference time). 5: Better reasoning is finally unlocking the promise of agents. A new cohort of agentic applications is emerging across various sectors, expanding markets by reducing the marginal cost of delivering services. 6: These AI-native agent companies look different than their SaaS counterparts. Increasingly, AI companies are selling work outcomes rather than software licenses, targeting the multi-trillion dollar services market. Services-as-a-Software is the new SaaS (h/t @bhalligan). 7: Two years ago, application level AI companies were derided as just a thin skin on top of a model. Now, it’s becoming clear that there is a ton for application builders to get right to bring value to end users, including engineering cognitive architectures (h/t @hwchase17), systems design, and novel UX paradigms. 8: As investors, we are increasingly shifting our attention towards the application layer. Many exciting @sequoia investments in agentic applications from law (@harvey__ai) to customer support (@SierraPlatform) to coding (@FactoryAI) to security (@Xbow) to general knowledge work (@glean). What did we miss? What did we get right and wrong? What’s next that we should be keeping our eyes out for? DMs open! Full essay linked below. And thanks o1 for the assist 😏

Due to a security incident, we strongly suggest you rotate any tokens or keys you use in secrets for HF Spaces: #managing-secrets" target="_blank" rel="nofollow noopener">huggingface.co/docs/hub/en/sp…. We have already proactively revoked a number of HF tokens and are working with cybersecurity forensic specialists to investigate the issue as well as review our security policies and procedures. You can find more initial information at huggingface.co/blog/space-sec….

More #LLM Agents will be #Python and not #JSON-based - due to 20% higher success rates and 30% fewer actions to solve tasks! huggingface.co/papers/2402.01… via @huggingface

Cincinnati millionaire Hugh Hoffman left historic $58M gift to ALS Association cincinnati.com/story/news/202…

NEW REPORT—#ArtificialIntelligence is rapidly changing the world and could improve government operations. But until there is government-wide guidance on acquisition and use of #AI, federal agencies can’t effectively address AI risks and benefits: gao.gov/products/gao-2…

Breaking news: E.U. officials reached a landmark deal on the world’s most sweeping bill to regulate artificial intelligence, cementing its role as the de facto global tech regulator as governments scramble to address the risks created by rapid AI advances. wapo.st/3tjA9L0

Join this session to discover how to prepare data from a Q&A chatbot in Snowflake, fine-tune the Llama 2 #LLM (specifically Code Llama), compare model candidates, manage the end-to-end model lifecycle leveraging the @wandb platform. okt.to/TMdG5Y

@ATLUTD @EASPORTSFC 🎮

Day 5: Another day, another gift 🙌 Like, RT and drop a 🎮 for your chance to win an @EASPORTSFC 24 PS5 copy!

Thank you @BarackObama for being an inspiration to so many, including me. It was an honor to receive your congrats call tonight. In Congress, I hope to continue your public service legacy & the work of countless Obama alums to bend the moral arc of the universe towards justice.

Thank you, Rhode Island. It’s time for us to come together and ensure that we keep this seat in Democratic hands in November. #TeamGabe

Meet Rhode Island’s next Congressman, Gabe Amo: the son of African immigrants and a child of Pawtucket. So proud.

Guns are the #1 killer of kids in America. More than car accidents and more than cancer. We can't let that become just another statistic. Let's ban AR-15-style firearms and other assault weapons.

All gas. No brakes. ⛽️ #WeAreTheA

Here we go, 17s! Our Starting XI for tonight's match vs. @SJEarthquakes 📋

Honored to serve and humbled to share a bit about the work!