u/lewtun

Hi folks, it's Lewis here from the post-training team at Hugging Face. We've been exploring how to train open models in different coding harnesses and wrote up a looong guide on how we solved this using open source libraries like TRL and the Harbor framework for RL environments. We hope you find this interesting, especially since everyone nowadays has their own custom harness (e.g. Pi + extensions) and now there's a recipe on how to squeeze the best performance on them with whatever open model you use as your daily driver. Happy to hear any comments or feedback!

Link to the guide: https://huggingface.co/spaces/FineEnvs/multi-harness-rl

Hi everyone, I'm happy to share ml-intern, which is a harness for agents to have tighter integration with Hugging Face's open-source libraries (transformers, datasets, trl, etc) and Hub infrastructure:

https://github.com/huggingface/ml-intern

The harness is quite simple (basically tools + system prompt) and we built it initially for Claude Opus. However, now that open models are getting really good at agentic workflows, I just added support for running ml-intern with local models via llama.cpp or ollama. As you can see in the video, Qwen3.6-35B-A3B is able to SFT a model end-to-end by orchestrating CPU/GPU sandboxes and jobs on the Hub. I find this pretty neat because we can now have an AI researcher running 24/7 on a laptop, without maxing out token limits :)

Anyway, I hope this is useful to the community and please let me know if there are any features that you'd like us to include.

Hi everyone, at Hugging Face we've been developing agentic harnesses for various domains and today we're releasing physics-intern to tackle research-level problems in theoretical physics.

It's a multi-agent framework which we designed to mimic the research process and decomposes the work into several focused tasks that are dispatched to dedicated subagents (computing, reviewing claims, challenging the research strategy...)

Using the physics-intern, we were able to double the performance of Gemini models on the CritPt benchmark and set a new SOTA compared to models like GPT-5.5 Pro, while being significantly cheaper :)

We wrote up how our framework was built in a blog post and hope it's useful for the community to build on: https://huggingface.co/spaces/huggingface/physics-intern

Hi it's Lewis from the Hugging Face post-training team! We spent the past month building RL environments in every major framework (verifiers, OpenEnv, Nemo-Gym, OpenRewards etc) and training models to better understand how they differ and scale across different axes.

We're very excited to share another looong blog post on what we found, which frameworks work best under which conditions and how to scale RL envs reliably:

https://huggingface.co/spaces/AdithyaSK/rl-environments-guide

Hope yall will enjoy it, don't hesitate to make feedback on the community tab :)