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@thom_wolf

  • LLMs as Dynamic System Orchestrators Beyond Fixed Agent Harnesses
    LLMs as Dynamic System Orchestrators Beyond Fixed Agent Harnesses

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    @thom_wolf

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    the LLM is the computer Ronak Malde (@rronak_) I have long felt that agent harnesses – even claude code – are too restrictive, because they are still designed by humans. New paper for Tinsghua and Shenzhen says, what if AI itself runs the harness, rather than defining it in code? Given a natural language SOP of how an agent should orchestrate subagents, memory, compaction, etc., we can just have an LLM execute that logic! (And AI could design that SOP dynamically and depending on the task too) It's a bit mind-warping to think about, but genius once it clicks. Makes you wonder how else we should be designing AI systems as we can start consuming more and more tokens — https://nitter.net/rronak_/status/2038401494177694074#m

    → View original post on X — @thom_wolf, 2026-03-30 07:44 UTC

  • Best Algorithm and Data Combination of the Year Competition

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    @thom_wolf

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    Who would win when combining best algo(model+optimization)/data of the year? h/t @lvwerra

    → View original post on X — @thom_wolf, 2026-03-27 10:32 UTC

  • Unitree open-sources UnifoLM-WBT humanoid robot teleoperation dataset

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    @thom_wolf

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    🚀 Unitree open-sources UnifoLM-WBT-Dataset — a high-quality real-world humanoid robot whole-body teleoperation (WBT) dataset for open environments. 🥳Publicly available since March 5, 2026, the dataset will continue to receive high-frequency rolling updates. It aims to establish the most comprehensive real-world humanoid robot dataset in terms of scenario coverage, task complexity, and manipulation diversity. 👉 Explore the dataset here: huggingface.co/collections/u…

    → View original post on X — @thom_wolf, 2026-03-27 08:04 UTC

  • Chroma Context-1: Open-source 20B Search Agent Released

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    @thom_wolf

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    Introducing Chroma Context-1, a 20B parameter search agent. > pushes the pareto frontier of agentic search > order of magnitude faster > order of magnitude cheaper > Apache 2.0, open-source

    → View original post on X — @thom_wolf, 2026-03-26 19:01 UTC

  • Beyond Random Search: Guiding Autoresearch Agents Toward Meaningful Exploration

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    @thom_wolf

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    What are the best current techniques to have autoresearch behave better than (slightly improved) random search? By which I mean (in Sijun below example), having the agent understand that (given some constraints) exploring int5 quantization is more exciting and have more downstream fruits than playing with the random seed? I’m talking about the beginning of having an agent pushed a real research program. The ones where you know the current technique will not give crazy results out of the box but it still push it because it believe and can demonstrate that the general direction has potential. Like neural networks used to be a worse way to do AI performance-wise. But we still pushed them… Sijun Tan (@sijun_tan) We took @karpathy's autoresearch agent, scaled it into a collaborative swarm, and topped @OpenAI's Parameter Golf Challenge—twice. Here’s how we did it: — https://nitter.net/sijun_tan/status/2036584756729749802#m

    → View original post on X — @thom_wolf, 2026-03-25 12:29 UTC

  • Auto-Research for Data: The Underrated ML Game Changer

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    @thom_wolf

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    Auto-research for ML training models is all the rage now, but underrated is: auto-research for data! Sure, you can squeeze out a bit of model performance by optimizing hyperparameters, but code agents can do data work that has been very labour intensive and required a lot of attention to a lot details effortlessly: > download data from many different data sources > bring all the data sources into uniform format > do detailed EDA: find patterns and outliers > look at 100s of samples and take detailed notes > make beautiful infographics rather than mpl plots > iterate on data filtering by looking at more samples > make a simple pipelines robust and scalable It's now possible to write data pipelines for dozens of data sources in hours that would have taken weeks of reading many docs, debugging APIs and data formats, wrangling outliers and missing data. A few weeks ago we gave Claude access to the CPU partition of our cluster and it iteratively refined filters to retrieve a domain subset of FineWeb. This would have taken me 2-3 days to work through while it took Claude just a few hours with almost no babysitting and with a nice logbook. Thus the long tail of small, niche data sources becomes more accessible and can be aggregated to even larger high quality datasets for cool applications. Data has been fuelling LLM progress more than model architecture innovations, so I am very excited about this!

    → View original post on X — @thom_wolf, 2026-03-24 17:04 UTC

  • hf-mount: Mount Hugging Face datasets and models as local filesystems
    hf-mount: Mount Hugging Face datasets and models as local filesystems

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    @thom_wolf

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    Now available on Hugging Face: hf-mount 🧑‍🚀 The team really cooked, still wrapping my head everything possible but you can do things like: – mount a 5TB dataset as a local folder and query only the parts you need with DuckDB (✅ works) – browse any model repo with ls/cat like it's a USB drive – use a shared read-write bucket as a team drive for ML artifacts – drop the init container that downloads models in your k8s pods – point llama.cpp at a mounted GGUF and run inference (infinite storage??)

    → View original post on X — @thom_wolf, 2026-03-24 16:13 UTC

  • hf-mount: Mount Hugging Face Storage as Local Filesystem
    hf-mount: Mount Hugging Face Storage as Local Filesystem

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    @thom_wolf

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    hf-mount Attach any Storage Bucket, model or dataset from @huggingface as a local filesystem This is a game changer, as it allows you to attach remote storage that is 100x bigger than your local machine's disk. This is also perfect for Agentic storage!! Read-write for Storage Buckets, read-only for models and datasets. Here's an example with FineWeb-edu (a 5TB slice of the Web): 1️⃣> hf-mount start repo datasets/HuggingFaceFW/fineweb-edu /tmp/fineweb It takes a few seconds to mount, and then: 2️⃣> du -h -d1 /tmp/fineweb 4.1T ./data 1.2T ./sample 5.3T . 🤯😮 Two backends are available: NFS (recommended) and FUSE Let's f**ing go 💪

    → View original post on X — @thom_wolf, 2026-03-24 13:34 UTC

  • LiteLLM Compromise: Malicious Update Alert Version 1.82.8

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    @thom_wolf

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    LiteLLM HAS BEEN COMPROMISED, DO NOT UPDATE. We just discovered that LiteLLM pypi release 1.82.8 has been compromised. It contains litellm_init.pth with base64 encoded instructions to send all the credentials it can find to a remote server and self-replicate. Link below [Translated from EN to English]

    → View original post on X — @thom_wolf, 2026-03-24 12:06 UTC

  • Pretraining LLMs entirely on Hugging Face Hub infrastructure

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    @thom_wolf

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    You can now pretrain LLMs entirely on the HF Hub 💥 Last week, @OpenAI launched a competition to see who can pretrain the best LLM in under 10 minutes. So over the weekend, I made a little demo to automate this end-to-end using the Hub as the infra layer: – Jobs to scale compute – Buckets to store all experiments – Trackio to log all the metrics The cool thing here is that everything is launched locally: no ssh shenanigans into a cluster or fighting with colleagues over storage and GPUs ⚔️ All that's left is coming up with new ideas, but luckily Codex can automate that part too 😁 Can I have a job now please @reach_vb 🙏?

    → View original post on X — @thom_wolf, 2026-03-23 16:29 UTC