louis_lifu
/
AReaL
mirror of https://github.com/inclusionAI/AReaL
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

[fix] nitpick fixes for readme (#204)

This commit is contained in:
Richard.Bian 2025-08-01 11:00:42 +08:00 committed by GitHub
parent a0f9f404d6
commit a5e55acb26
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 5 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
README.md
View File

@ -23,11 +23,11 @@ like how you enjoy real-world milk tea (cheers).
- ⚡ <span style="color: red; font-weight: bold;">**\[NEW\] AReaLite:**</span> Our new
release AReaLite is a **light-weight** and **AI-centric** codebase that prioritizes
better development experiences for AI researchers. As a result, AReaLite delivers most
AReaL functionalities and maintains its high performance with much fewer lines of
code, supporting users to build their own **agentic** and **RLVR** training workflows
AReaL functionalities while maintains its high performance with much fewer lines of
code. This allows users to build their own **agentic** and **RLVR** training workflows
with minimal effort.
- 🔥 **Asynchronous RL**: With algorithm-system co-design, AReaL supports fully
asynchronous RL for **the fastest training**! Experimental support for multi-turn
asynchronous RL for **the fastest training speed**! Experimental support for multi-turn
agentic RL is also provided.
- 🛠️ **Open & Reproducible**: We continuously release _all code, datasets, and training
recipes_ for RL training of LLMs.
@ -69,7 +69,7 @@ New highlights in AReaLite:
- Instead of the *system-centric* architecture in old AReaL, AReaLite follows an
**AI-centric** API design that aims to provide the following key features:
- **Light-weight** & **focused** algorithm and training workflow customization.
- **Light-weight** & **easy to write** algorithm and training workflow customization.
- **Easy to scale up** without knowing system and infrastructure details.
- **Adaptable and plugable:** Smooth to integrate with other modern AI applications.
@ -106,7 +106,7 @@ Our training scripts will automatically download the dataset (openai/gsm8k) and
python3 -m arealite.launcher.local examples/arealite/gsm8k_grpo.py --config examples/arealite/configs/gsm8k_grpo.yaml
```
On a ray cluster with 2 nodes & 8 GPUs each node, runs (Remember to change paths in YAML
On a Ray cluster with 2 nodes & 8 GPUs each node, runs (Remember to change paths in the YAML
file to your own shared storage):
```