An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
EasyR1 is a distributed model training system and reinforcement learning framework for large language and vision-language models. It functions as a multimodal trainer and an implementation of a Proximal Policy Optimization pipeline designed to refine the reasoning and perception capabilities of models that process both text and images. The system specializes in distributing reinforcement learning workloads across multiple compute nodes to manage high memory requirements. It optimizes hardware utilization through padding-free training and fine-tuning to fit large models onto available graphics
🚀 Reinforcement Learning for Language Agents🌟
Die Hauptfunktionen von agentica-project/rllm sind: Reasoning Datasets, Reasoning Models, Reinforcement Learning Frameworks.
Open-Source-Alternativen zu agentica-project/rllm sind unter anderem: open-reasoner-zero/open-reasoner-zero — An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model. inclusionai/areal — AReaL is a system for agent orchestration, distributed model training, and parameter-efficient tuning. It provides a… deep-agent/r1-v. gair-nlp/limo — 📄 Paper | 🌐 Dataset (v2) | 📘 Model (v2). hiyouga/easyr1 — EasyR1 is a distributed model training system and reinforcement learning framework for large language and… huggingface/open-r1 — Open-r1 is a framework designed for the large-scale training, distillation, and optimization of language models…
