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I’ve been playing with deep reinforcement learning for a while. I originally started with a simple DQN, added all improvements from the Rainbow paper, and finally changed C51 for a quantile regression (and plan to swap it for an Implicit Quantile Network). After implementing C51 (which was my first time with distributional RL) I started playing with policies that take advantage of the learned distributions : By independently taking N samples from each action-value distribution, scoring actions by […]

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Part 2 of my “tiny models, big surprises” series. This time I stopped just running them and started shaping them. A few weeks ago I wrote about running TinyLlama on my MacBook Air and being quietly stunned that a 637MB model could write working code. A lot of you wrote back asking the same question: “Cool, but can you actually use these for real work?” That question lived in my head for two weeks. So I went looking for an answer. […]

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Build AI features that hold up in production. These days, adding an AI feature to your application is easier than ever — a few API calls, a model endpoint, some glue code, and you have semantic search, summarization, or a chatbot working. The hard part is not building it but understanding what is actually happening underneath — because that is what determines whether your feature holds up when real users arrive. Here are five foundational concepts that show up in nearly every AI […]

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I’m working on this vscode extension to hopefully reduce the learning curve for Isaac Lab! It’s browser style, with modular tabs for editing scripts, running training sessions (both local and remote/ssh machines), and even a training monitor that plots rewards over time! It is very much a work in progress but let me know what yall think, though bugs at this stage are probably super easy to find: IsaacLab-Tools submitted by /u/the_hammershot_148 [link] [comments]

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Read Online | Sign Up | Advertise Good morning, {{ first_name | AI enthusiasts }}. Google DeepMind just took AI’s coding strategy and applied it to math: don’t ask a model for the answer, give a team of agents the workspace. The company’s AI co-mathematician just scored a new high on a benchmark built to stump AI for decades, with one professor even cracking an unsolved problem using a strategy buried inside a proof the system’s own reviewers […]

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Author(s): DrSwarnenduAI Originally published on Towards AI. For a decade, we asked if RNNs can represent what Transformers represent. We proved they can. We forgot to ask how expensively. That omission just cost us ten years. “Can our architecture represent everything a Transformer can?” The benchmarks run. The perplexity scores appear. The answer, roughly, is yes. A paper at ICLR 2026, titled “Transformers are Inherently Succinct,” was awarded Outstanding Paper.The article discusses the limitations of recurrent neural networks […]

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