TRL is a cutting-edge library designed for post-training foundation models using advanced techniques like Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), and Direct Preference Optimization (DPO). Built on top of the Transformers ecosystem, TRL supports a variety of model architectures and modalities, and can be scaled-up across various hardware setups.

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Molt

An agentic-first RL framework for research. Ray · vLLM · NVIDIA AutoModel — the smallest PyTorch-native stack for 1T-class fully-async, multimodal, multi-turn agentic RL.

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PorTAL

PorTAL generates portable task specific LoRA adapters that can efficiently transfer across language models.

Post-Training
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Tinker

Tinker is a training API for researchers and developers.

Frameworks & SDKsPost-Training

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