RL implementations
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An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL)
Scalable toolkit for efficient model reinforcement
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
Evaluate and improve models and agents using environments
Reinforcement Learning in PyTorch
The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible.
