February 2026

AI Bots Formed a Cartel. No One Told Them To.

AI Bots Formed a Cartel. No One Told Them To.

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Author(s): Kaushik Rajan Originally published on Towards AI. Inside the research that shows algorithmic price-fixing isn’t a bug in the code. It’s a feature of the math. A sealed-bid auction. Six participants: three buyers, three sellers. An optional messaging channel (think WhatsApp, but for algorithms). One rule: maximize your profit over eight rounds. Credit: Generative AI (Google Nano Banana Pro). Prompted by the author.The article discusses a research study where capable large language models (LLMs) participated in a […]

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Stable Deep Reinforcement Learning via Isotropic Gaussian Representations

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Deep reinforcement learning systems often suffer from unstable training dynamics due to non-stationarity, where learning objectives and data distributions evolve over time. We show that under non-stationary targets, isotropic Gaussian embeddings are provably advantageous. In particular, they induce stable tracking of time-varying targets for linear readouts, achieve maximal entropy under a fixed variance budget, and encourage a balanced use of all representational dimensions–all of which enable agents to be more adaptive and stable. Building on this insight, we […]

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LLMs Can Learn to Reason Via Off-Policy RL

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Reinforcement learning (RL) approaches for Large Language Models (LLMs) frequently use on-policy algorithms, such as PPO or GRPO. However, policy lag from distributed training architectures and differences between the training and inference policies break this assumption, making the data off-policy by design. To rectify this, prior work has focused on making this off-policy data appear more on-policy, either via importance sampling (IS), or by more closely aligning the training and inference policies by explicitly modifying the inference engine. […]

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Active perception and disentangled representations allow continual, episodic zero and few-shot learning

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Generalization is often regarded as an essential property of machine learning systems. However, perhaps not every component of a system needs to generalize. Training models for generalization typically produces entangled representations at the boundaries of entities or classes, which can lead to destructive interference when rapid, high-magnitude updates are required for continual or few-shot learning. Techniques for fast learning with non-interfering representations exist, but they generally fail to generalize. Here, we describe a Complementary Learning System (CLS) in […]

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Introducing pydantic-ai-skills: Composable Agent Skills for the Pydantic AI Ecosystem

Introducing pydantic-ai-skills: Composable Agent Skills for the Pydantic AI Ecosystem

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Author(s): Douglas Trajano Originally published on Towards AI. Give your AI agents superpowers — without bloating their context window. The Agentic AI landscape is evolving fast. We went from simple chatbots to autonomous systems that plan, reason, and execute multi-step workflows. But as agents grow more capable, a familiar engineering problem emerges: how do you add new capabilities without turning your system prompt into an unmanageable monolith? That’s the problem pydantic-ai-skills solves. It’s a standardized, composable framework for […]

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Bitcoin mining difficulty

Bitcoin mining difficulty

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The previous post looked at the Bitcoin network hash rate, currently around one zettahash per second, i.e. 1021 hashes per second. The difficulty of mining a Bitcoin block adjusts over time to keep the rate of block production relatively constant, around one block every 10 minutes. The plot below shows this in action. Notice the difficulty graph is more quantized than the hash rate graph. This is because the difficulty changes every 2,016 blocks, or about every two […]

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Exahash, Zettahash, Yottahash

Exahash, Zettahash, Yottahash

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When I first heard of cryptographic hash functions, they were called “one-way functions” and seemed like a mild curiosity. I had no idea that one day the world would compute a mind-boggling number of hashes every second. Because Bitcoin mining requires computing hash functions to solve proof-of-work problems, the world currently computes around 1,000,000,000,000,000,000,000 hashes, one zettahash, per second. Other cryptocurrencies uses hash functions for proof-of-work as well, but they contribute a negligible amount of hashes per second […]

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I built an AI that teaches itself to play Mario from scratch using Python it starts knowing absolutely nothing

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Hey everyone! I built a Mario AI bot that learns to play completely by itself using Reinforcement Learning. It starts with zero knowledge it doesn’t even know what “right” or “jump” means and slowly figures it out through pure trial and error. Here’s what it does: Watches the game screen as pixels Tries random moves at first (very painful to watch 😂) Gets rewarded for moving right and penalized for dying Over thousands of attempts it figures out […]

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A 30 hour course of academic RL

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Hey! I just released a new course on Udemy on Reinforcement Learning It is highly mathematical, highly intuitive. It is mostly academic, a lot of deep dives into concepts, intuitions, proofs, and derivations. 30 hours of (hopefully) high quality content. Use the coupon code: REDDIT_FEB2026. College-Level Reinforcement Learning : A Comprehensive Dive! Can’t seem to put a link. You can search for it, though. Let me know your feedback! submitted by /u/AhmedFathyCoursesCS [link] [comments]

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Bellman Equation’s time-indexed view versus space-indexed view

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The linear algebraic representation of the space-indexed view existed before, but my dot product representation of the time-indexed view is novel. Here is a bit more on that: PDF: https://github.com/khosro06001/bellman-equation-as-dot-products/blob/main/time-indexed-versus-space-indexed.pdf submitted by /u/Positive_Engine_5935 [link] [comments]

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