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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🌟
AReaL is a system for agent orchestration, distributed model training, and parameter-efficient tuning. It provides a framework for developing multi-turn reasoning agents and training large models using reinforcement learning from human feedback. The project implements a toolkit for improving the visual reasoning and geometry problem solving capabilities of vision-language models. It utilizes a memory-efficient tuning system to optimize mathematical and reasoning models across different inference backends. The infrastructure supports large-scale training through tensor, pipeline, and expert p
Reproduce R1 Zero on Logic Puzzle
The main features of unakar/logic-rl are: Reasoning Environments, Reasoning Models, Reinforcement Learning Frameworks.
Projects with overlapping indexed features include: jiayi-pan/tinyzero — TinyZero is a reinforcement learning framework and implementation designed to train language models to develop… hiyouga/easyr1 — EasyR1 is a distributed model training system and reinforcement learning framework for large language and… deep-agent/r1-v. inclusionai/areal — AReaL is a system for agent orchestration, distributed model training, and parameter-efficient tuning. It provides a… agentica-project/rllm — 🚀 Reinforcement Learning for Language Agents🌟. modalminds/mm-eureka — MM-EUREKA: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning.