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sergiopaniego
posted an update 8 days ago
Post
1699
OpenEnv is growing fast in tutorials. If you're looking to get started with RL environments, check them out
> evaluate your agents using OpenEnv
> learn how rewards work via rubrics
> connect agents via MCP
> many moreeeee!
anything you think it's missing?
https://meta-pytorch.org/OpenEnv/tutorials/index.html
> evaluate your agents using OpenEnv
> learn how rewards work via rubrics
> connect agents via MCP
> many moreeeee!
anything you think it's missing?
https://meta-pytorch.org/OpenEnv/tutorials/index.html
sergiopaniego
posted an update 9 days ago
Post
773
OpenEnv already ships 🚢 with a ready-to-deploy RLM environment on free HF Spaces
Drop "Attention Is All You Need", write code that spawns parallel LLM calls → ✅ correct answer, reward 1.0, in 4.2s
Run GRPO (TRL) → model learns to write that search strategy itself
test it yourself → sergiopaniego/repl-env
check out OpenEnv → https://github.com/meta-pytorch/OpenEnv
Drop "Attention Is All You Need", write code that spawns parallel LLM calls → ✅ correct answer, reward 1.0, in 4.2s
Run GRPO (TRL) → model learns to write that search strategy itself
test it yourself → sergiopaniego/repl-env
check out OpenEnv → https://github.com/meta-pytorch/OpenEnv
hamza-hcompany
updated a
collection 18 days ago
hamza-hcompany
published a
model 18 days ago
hamza-hcompany
updated a
model 18 days ago
h-aurelien-lac
updated a
model 18 days ago
sergiopaniego
posted an update about 1 month ago
Post
1363
Earlier this month, Apple introduced Simple Self-Distillation: a fine-tuning method that improves models on coding tasks just by sampling from the model and training on its own outputs with plain cross-entropy
And… it's already supported in TRL, built by Kashif Rasul. you can really feel the pace of development in the team 🐎
Paper by Ruixiang ZHANG, He Bai, Huangjie Zheng, Navdeep Jaitly, Ronan Collobert, Yizhe Zhang at Apple 🍎
How it works: the model generates completions at a training-time temperature (T_train) with top_k/top_p truncation, then fine-tunes on them with plain cross-entropy. no labels or verifier needed
You can try it right away with this ready-to-run example (Qwen3-4B on rStar-Coder):
https://github.com/huggingface/trl/blob/main/trl/experimental/ssd/ssd.py
or benchmark a checkpoint with the eval script:
https://github.com/huggingface/trl/blob/main/trl/experimental/ssd/ssd_eval.py
One neat insight from the paper: T_train and T_eval compose into an effective T_eff = T_train × T_eval, so a broad band of configs works well. even very noisy samples still help
Want to dig deeper?
Paper: Embarrassingly Simple Self-Distillation Improves Code Generation (2604.01193)
Trainer docs: https://huggingface.co/docs/trl/main/en/ssd_trainer
And… it's already supported in TRL, built by Kashif Rasul. you can really feel the pace of development in the team 🐎
Paper by Ruixiang ZHANG, He Bai, Huangjie Zheng, Navdeep Jaitly, Ronan Collobert, Yizhe Zhang at Apple 🍎
How it works: the model generates completions at a training-time temperature (T_train) with top_k/top_p truncation, then fine-tunes on them with plain cross-entropy. no labels or verifier needed
You can try it right away with this ready-to-run example (Qwen3-4B on rStar-Coder):
https://github.com/huggingface/trl/blob/main/trl/experimental/ssd/ssd.py
or benchmark a checkpoint with the eval script:
https://github.com/huggingface/trl/blob/main/trl/experimental/ssd/ssd_eval.py
One neat insight from the paper: T_train and T_eval compose into an effective T_eff = T_train × T_eval, so a broad band of configs works well. even very noisy samples still help
Want to dig deeper?
Paper: Embarrassingly Simple Self-Distillation Improves Code Generation (2604.01193)
Trainer docs: https://huggingface.co/docs/trl/main/en/ssd_trainer
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Hcompany
• • 24 sergiopaniego
posted an update about 1 month ago
Post
458
Great experience yesterday at PyTorch Conf Europe in Paris 🇫🇷
We (w/ @kashif ) talked about training LLMs through interaction, using trajectories across games, browsers, or simulators
Room was packed, a clear sign of interest in where RL post-training is heading.
sharing the slides! 🤓
https://drive.google.com/file/d/16k7YRnf5EJEo0XjXGlRJ_hVeLoFWKyNP/view?usp=sharing
We (w/ @kashif ) talked about training LLMs through interaction, using trajectories across games, browsers, or simulators
Room was packed, a clear sign of interest in where RL post-training is heading.
sharing the slides! 🤓
https://drive.google.com/file/d/16k7YRnf5EJEo0XjXGlRJ_hVeLoFWKyNP/view?usp=sharing
Rename README.md to README.mds
#3 opened about 1 month ago
by 
faizikhan1
sergiopaniego
posted an update about 1 month ago
Post
2863
Gemma 4 💎 is here and it’s strong!
to celebrate, we’re rolling out in TRL:
> support for multimodal tool responses for environments (OpenEnv)
> an example to train it in CARLA for autonomous driving with image-based tool calls
go check it out 🏎️🏎️
blog: https://huggingface.co/blog/gemma4
script: https://github.com/huggingface/trl/blob/main/examples/scripts/openenv/carla_vlm_gemma.py
to celebrate, we’re rolling out in TRL:
> support for multimodal tool responses for environments (OpenEnv)
> an example to train it in CARLA for autonomous driving with image-based tool calls
go check it out 🏎️🏎️
blog: https://huggingface.co/blog/gemma4
script: https://github.com/huggingface/trl/blob/main/examples/scripts/openenv/carla_vlm_gemma.py
Are there any plans regarding Holo3-9B?
4
#1 opened about 2 months ago
by 
Pevernow
Quickstart guide link on model card leads to 404
1
#2 opened about 1 month ago
by 
TomSchelsen
Are there any plans regarding Holo3-9B?
4
#1 opened about 2 months ago
by Pevernow
Quickstart guide link on model card leads to 404
1
#2 opened about 1 month ago
by TomSchelsen
plcedoz38
updated a
model about 1 month ago
ramzidecoster
published an article about 1 month ago
Article
Holo3: Breaking the Computer Use Frontier
Hcompany
• • 46
emricksini-h
updated a
model about 1 month ago
sergiopaniego
posted an update about 2 months ago
Post
2081
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
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
- 2 replies