Trinity-RFT and LightRFT
These are competitors in the RFT space, both targeting LLM fine-tuning through reinforcement learning but with different design philosophies—Trinity-RFT emphasizing general-purpose flexibility and scalability while LightRFT prioritizes lightweight efficiency and multimodal reward modeling.
About Trinity-RFT
agentscope-ai/Trinity-RFT
Trinity-RFT is a general-purpose, flexible and scalable framework designed for reinforcement fine-tuning (RFT) of large language models (LLM).
This helps developers fine-tune large language models (LLMs) using reinforcement learning. You provide an existing LLM and define an environment for it to interact with, and this framework helps train the model to perform better at specific tasks. It is for AI developers, machine learning engineers, and researchers who want to improve the performance of their LLMs.
About LightRFT
opendilab/LightRFT
LightRFT: Light, Efficient, Omni-modal & Reward-model Driven Reinforcement Fine-Tuning Framework
This framework helps AI practitioners improve the performance and behavior of Large Language Models (LLMs) and Vision-Language Models (VLMs). You feed in a pre-trained language or vision-language model along with human feedback or a reward model, and it outputs a fine-tuned model that better aligns with desired outcomes, like generating more accurate text or understanding multimodal data. It's designed for machine learning engineers and researchers working with advanced AI models.
Scores updated daily from GitHub, PyPI, and npm data. How scores work