Building on HF

Sergio Paniego PRO

sergiopaniego

https://sergiopaniego.github.io/

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updated a dataset about 1 hour ago

agents-course/final-certificates

updated a dataset about 1 hour ago

agents-course/course-certificates-of-excellence

posted an update about 11 hours ago

TRL is officially an adult 🥳 excited to announce TRL v1.0❗️ head to the blog to see how we got here and what’s next for this post-training library, designed to keep pace with the field https://huggingface.co/blog/trl-v1

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posted an update about 11 hours ago

Post

TRL is officially an adult 🥳

excited to announce TRL v1.0❗️

head to the blog to see how we got here and what’s next for this post-training library, designed to keep pace with the field

https://huggingface.co/blog/trl-v1

posted an update 18 days ago

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672

ICYMI, great blog by @kashif and @stas on Ulysses Sequence Parallelism: train with million-token contexts

on 4×H100s: 12x longer sequences, 3.7x throughput

learn how to integrate it with Accelerate, Transformers, and TRL ⤵️
https://huggingface.co/blog/ulysses-sp

posted an update 20 days ago

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We just released a big blog surveying 16 OSS frameworks for async RL training of LLMs!

We're building a new async GRPO trainer for TRL and as first step, we needed to understand how the ecosystem solves this problem today.

The problem: in synchronous RL training, generation dominates wall-clock time. 32K-token rollouts on a 32B model take hours while training GPUs sit completely idle. With reasoning models and agentic RL making rollouts longer and more variable, this only gets worse.

The ecosystem converged on the same fix: separate inference + training onto different GPU pools, rollout buffer, and async weight sync.

We compared 16 frameworks across 7 axes: orchestration, buffer design, weight sync, staleness management, partial rollouts, LoRA, and MoE support.

This survey is step one. The async GRPO trainer for TRL is next!

https://huggingface.co/blog/async-rl-training-landscape

posted an update 20 days ago

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Nemotron 3 Super by @nvidia is here! NVIDIA's hybrid Mamba2/Transformer models are now natively supported in transformers (no trust_remote_code needed)

Fine-tune them with TRL in just a few lines of code. Notebook + script included to get started right away. goooo!

- Notebook: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_nemotron_3.ipynb
- Script: https://github.com/huggingface/trl/blob/main/examples/scripts/sft_nemotron_3.py
- Collection with all the models: https://huggingface.co/collections/nvidia/nvidia-nemotron-v3

posted an update 28 days ago

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did you know you can train agentic models with RL deploying the environments on HF Spaces? 🤗

with TRL + OpenEnv, your training script connects to remote environments hosted as Spaces

want to train faster? → just add more Spaces (TRL handles the parallelization natively)

we used this to train a model to solve the trolley problem in CARLA. 2 HF Spaces running a full driving simulator, each on a T4 GPU

full write-up with code and results → https://huggingface.co/blog/sergiopaniego/bringing-carla-to-openenv-trl

posted an update 30 days ago

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Qwen3.5 dense (smol 🤏) models just dropped

- natively multimodal
- 0.8B · 2B · 4B · 9B (+ base variants)
- 262K context extensible to 1M
- built-in thinking

fine-tune them with TRL out of the box → SFT, GRPO, DPO and more!

examples: https://huggingface.co/docs/trl/example_overview
collection: https://huggingface.co/collections/Qwen/qwen35

posted an update about 1 month ago

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What happens when you make an LLM drive a car where physics are real and actions can't be undone?

I ported CARLA, the autonomous driving simulator, to OpenEnv and added training support via TRL + Hugging Face Spaces.

The model interacts with the simulator through tool calls (observe, brake, change lane) and learns from a reward signal.

In 50 training steps, Qwen 0.6B learns to swerve and brake to avoid pedestrians in emergency situations.

The project supports text and vision (VLMs can see through a camera sensor), open-world driving with traffic, and multiple driving scenarios.

This builds on the carla-env project by sinatras, which originally placed LLMs inside CARLA for evaluation. We extended it with vision, new scenarios, rubric-based rewards, and made it trainable end-to-end.

Blog: https://huggingface.co/blog/sergiopaniego/bringing-carla-to-openenv-trl/
CARLA env in OpenEnv: https://github.com/meta-pytorch/OpenEnv/tree/main/envs/carla_env
Training script: https://github.com/huggingface/trl/blob/main/examples/scripts/openenv/carla.py

posted an update about 1 month ago

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Tiny Aya 🌿 just dropped from @CohereLabs , a really powerful multilingual small model!

To celebrate, we cooked up fresh resources to train it for tool calling 🔧

> Free Google Colab guide: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_tool_calling.ipynb
> Standalone training script: https://github.com/huggingface/trl/blob/main/examples/scripts/sft_tiny_aya_tool_calling.py

posted an update about 2 months ago

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595

The latest piece by @MiniMax-AI is a must-read.

It tries to break the impossible triangle of agent RL: throughput × stability × flexibility.

A lot to learn here, go read it 🫵
https://huggingface.co/blog/MiniMax-AI/forge-scalable-agent-rl-framework-and-algorithm

posted an update about 2 months ago

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if you're looking for a good first issue to get your open-source journey started, you could contribute to this TRL issue by documenting one impactful paper in the docs

we have a broad list to cover!! 🧐

https://github.com/huggingface/trl/issues/4407

posted an update 2 months ago

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Meet the Post-Training Toolkit (PTT), which easily integrates with TRL via a single callback, by Aditya Challapally (@microsoft ):

🔍 Detects training issues early
🛠 Lets you intervene safely
📊 Keeps long training runs stable, auditable & efficient

Microsoft blog: https://devblogs.microsoft.com/engineering-at-microsoft/diagnosing-instability-in-production-scale-agent-rl/

Integration guide: https://huggingface.co/docs/trl/main/en/ptt_integration

Code: https://github.com/microsoft/post-training-toolkit

posted an update 2 months ago

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New TRL + OpenEnv example! 💥

Fine tune an LLM for playing Sudoku using an RL env via OpenEnv

Includes a script that runs on 1 or multiple GPUs with vLLM, plus a Colab-ready notebook.

Enjoy!

Notebook: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/openenv_sudoku_grpo.ipynb

Script: https://github.com/huggingface/trl/blob/main/examples/scripts/openenv/sudoku.py

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posted an update 2 months ago

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Date idea: read the entire Transformers v5.0.0 release notes

Officially stable now: https://github.com/huggingface/transformers/releases/tag/v5.0.0

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posted an update 2 months ago

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FunctionGemma Tuning Lab is a new no-code tool by @google that lets you fine-tune a model directly from the browser, with no coding knowledge required, using TRL behind the scenes.

blog: https://developers.googleblog.com/a-guide-to-fine-tuning-functiongemma/

try it out: google/functiongemma-tuning-lab

This example builds on a more advanced one for learning fine-tuning with SFT using TRL: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma

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posted an update 2 months ago

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TRL v0.27.0 is out!! 🥳

It includes GDPO, the latest variant of GRPO for multi-reward RL ✨
GDPO decouples reward normalization to avoid reward collapse and improve per-reward convergence — developed by
@sliuau @SimonX et al.

Explore the paper: GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization (2601.05242)

Explore the full set of changes here:
https://github.com/huggingface/trl/releases/tag/v0.27.0

posted an update 3 months ago

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New REPL environment in OpenEnv available! ✨
Used in the Recursive Language Models (RLM) paper by Alex Zhang.

Ready for inference & post-training using trajectories. Handles long contexts:

> Run Python code in a sandbox
> Make recursive calls to LMs
> Explore data programmatically
> Return final result

Docs: https://meta-pytorch.org/OpenEnv/environments/repl/
Inference script: https://github.com/meta-pytorch/OpenEnv/blob/main/examples/repl_oolong_simple.py

posted an update 3 months ago

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Recursive Language Models (RLM) is a new interface for LLMs with cool ideas by Alex Zhang!

⚠️ LLMs struggle with long prompts → attention overload & lost info
🔄 RLMs inspect, split & call themselves on chunks, then aggregate results
✅ Handles millions of tokens, reduces noise, improves reasoning
💡 System prompt guides recursion
🎯 RLM trajectories can be used for RL training or distillation (OpenEnv+TRL!!)

We're adding it to OpenEnv (with Kashif Rasul): https://github.com/meta-pytorch/OpenEnv/pull/282

More resources:

> Paper: Recursive Language Models (2512.24601)
> Paper blog: https://alexzhang13.github.io/blog/2025/rlm/
> RLM repo: https://github.com/alexzhang13/rlm

2 replies

posted an update 3 months ago

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New GRPO + TRL free Colab notebook out! 🔥

Fine-tune 7B+ models on T4 GPUs thanks to a ton of memory optimizations for GRPO

7B model uses only 9.2 GB VRAM (~7× reduction) 🤯

Try the notebook here 👉 https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/grpo_trl_lora_qlora.ipynb

posted an update 3 months ago

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The list of hands-on notebooks (some beginner-friendly!) to get started with fine-tuning using TRL keeps growing!!

• SFT
• GRPO
• Tool calling & agents
• RL environments with OpenEnv
• LLMs and VLMs
✨ Many run on FREE Colab, making it super easy to get started fast!

https://github.com/huggingface/trl/tree/main/examples/notebooks

posted an update 3 months ago

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As the year comes to an end, it’s a good moment to catch up on some of the best long-form pieces published by the @huggingface team.

I’ve gathered them all here if you want to read or save them for later:
https://huggingface.co/collections/sergiopaniego/research-and-long-form-blog-posts

Sergio Paniego PRO

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