Post
441
Your Language Model needs better (open) environments to learn 🌀
📝 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.
1️⃣ 𝗪𝗵𝘆 𝗥𝗟 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀
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.
2️⃣ 𝗪𝗵𝗮𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 𝗮𝗿𝗲
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.
3️⃣ 𝐓𝐡𝐞 𝐨𝐩𝐞𝐧 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞
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.
4️⃣ 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 𝐇𝐮𝐛
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. 🔬
I explored the Hub and wrote a hands-on walkthrough 📝
- RL + LLMs basics
- Environments Hub navigation
- Evaluating models/Agents
- GRPO Training a tiny model on an alphabetical sort task
Take a look!
📝 https://huggingface.co/blog/anakin87/environments-hub
📝 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.
1️⃣ 𝗪𝗵𝘆 𝗥𝗟 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀
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.
2️⃣ 𝗪𝗵𝗮𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 𝗮𝗿𝗲
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.
3️⃣ 𝐓𝐡𝐞 𝐨𝐩𝐞𝐧 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞
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.
4️⃣ 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 𝐇𝐮𝐛
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. 🔬
I explored the Hub and wrote a hands-on walkthrough 📝
- RL + LLMs basics
- Environments Hub navigation
- Evaluating models/Agents
- GRPO Training a tiny model on an alphabetical sort task
Take a look!
📝 https://huggingface.co/blog/anakin87/environments-hub