Post
269
Your Language Model needs better (open) environments to learn ๐
๐ https://huggingface.co/blog/anakin87/environments-hub
RL environments help LLMs practice, reason, and improve.
I explored the Environments Hub and wrote a walkthrough showing how to train and evaluate models using these open environments.
1๏ธโฃ ๐ช๐ต๐ ๐ฅ๐ ๐บ๐ฎ๐๐๐ฒ๐ฟ๐ ๐ณ๐ผ๐ฟ ๐๐๐ ๐
DeepSeek-R1 made clear that Reinforcement Learning can be used to incentivize reasoning in LLMs.
In GRPO, the model generates multiple answers and learns to prefer the better ones from rewards.
2๏ธโฃ ๐ช๐ต๐ฎ๐ ๐ฒ๐ป๐๐ถ๐ฟ๐ผ๐ป๐บ๐ฒ๐ป๐๐ ๐ฎ๐ฟ๐ฒ
In classic RL, the environment is the world where the Agent lives, interacts, and get rewards to learn.
We can also think of them as software packages, containing data, harness and scoring rules - for the model
to learn and be evaluated.
Nowadays, the Agent is not just the LLM. It can use tools, from a weather API to a terminal.
This makes environments for training and evaluation more complex and critical.
3๏ธโฃ ๐๐ก๐ ๐จ๐ฉ๐๐ง ๐๐ก๐๐ฅ๐ฅ๐๐ง๐ ๐
Big labs are advancing, but open models and the community still face a fragmented ecosystem.
We risk becoming users of systems built with tools we can't access or fully understand.
4๏ธโฃ ๐๐ง๐ฏ๐ข๐ซ๐จ๐ง๐ฆ๐๐ง๐ญ๐ฌ ๐๐ฎ๐
That's why, I was excited when Prime Intellect released the Environments Hub.
It's a place where people share RL environments: tasks you can use to train LLMs with RL (GRPO-style) or evaluate Agents.
Plus, the Verifiers library ( @willcb ) standardizes the creation of RL environments and evaluations.
They can help to keep science and experimentation open. ๐ฌ
I explored the Hub and wrote a hands-on walkthrough ๐
- RL + LLMs basics
- Environments Hub navigation
- Evaluating models/Agents
- GRPO Training a tiny model on an alphabetical sort task
Take a look!
๐ https://huggingface.co/blog/anakin87/environments-hub
๐ https://huggingface.co/blog/anakin87/environments-hub
RL environments help LLMs practice, reason, and improve.
I explored the Environments Hub and wrote a walkthrough showing how to train and evaluate models using these open environments.
1๏ธโฃ ๐ช๐ต๐ ๐ฅ๐ ๐บ๐ฎ๐๐๐ฒ๐ฟ๐ ๐ณ๐ผ๐ฟ ๐๐๐ ๐
DeepSeek-R1 made clear that Reinforcement Learning can be used to incentivize reasoning in LLMs.
In GRPO, the model generates multiple answers and learns to prefer the better ones from rewards.
2๏ธโฃ ๐ช๐ต๐ฎ๐ ๐ฒ๐ป๐๐ถ๐ฟ๐ผ๐ป๐บ๐ฒ๐ป๐๐ ๐ฎ๐ฟ๐ฒ
In classic RL, the environment is the world where the Agent lives, interacts, and get rewards to learn.
We can also think of them as software packages, containing data, harness and scoring rules - for the model
to learn and be evaluated.
Nowadays, the Agent is not just the LLM. It can use tools, from a weather API to a terminal.
This makes environments for training and evaluation more complex and critical.
3๏ธโฃ ๐๐ก๐ ๐จ๐ฉ๐๐ง ๐๐ก๐๐ฅ๐ฅ๐๐ง๐ ๐
Big labs are advancing, but open models and the community still face a fragmented ecosystem.
We risk becoming users of systems built with tools we can't access or fully understand.
4๏ธโฃ ๐๐ง๐ฏ๐ข๐ซ๐จ๐ง๐ฆ๐๐ง๐ญ๐ฌ ๐๐ฎ๐
That's why, I was excited when Prime Intellect released the Environments Hub.
It's a place where people share RL environments: tasks you can use to train LLMs with RL (GRPO-style) or evaluate Agents.
Plus, the Verifiers library ( @willcb ) standardizes the creation of RL environments and evaluations.
They can help to keep science and experimentation open. ๐ฌ
I explored the Hub and wrote a hands-on walkthrough ๐
- RL + LLMs basics
- Environments Hub navigation
- Evaluating models/Agents
- GRPO Training a tiny model on an alphabetical sort task
Take a look!
๐ https://huggingface.co/blog/anakin87/environments-hub
