volcengine/verl

Features

• Distributed Training - Offers a distributed framework for scaling large language model training across multiple GPUs using expert parallelism.
• Large-Scale Model Training - Enables training of models with hundreds of billions of parameters using expert parallelism and distributed backends.
• Expert Parallelism Configurations - Provides an expert-parallel distributed backend to scale training for massive parameter counts across multiple GPU nodes.
• Reinforcement Learning Alignment - Implements reinforcement learning algorithms like PPO and GRPO to align large language models using reward signals.
• Memory Resharding - Optimizes communication overhead by dynamically reconfiguring model partitions when switching between training and generation phases.
• Sharded Device Mapping - Distributes model layers and tensors across multiple GPUs to optimize hardware utilization and bypass memory limits.
• Model Training and Inference Engines - Ships a hybrid engine that optimizes memory and communication when switching between model generation and gradient updates.
• Memory Resharding Optimizations - Optimizes memory and communication during transitions between generation and gradient updates using a hybrid engine.
• Post-Training Configuration Recipes - Executes post-training workflows including supervised fine-tuning and reinforcement learning to refine model behavior.
• Reinforcement Learning Algorithms - Implements core reinforcement learning algorithms including PPO and GRPO for reward-based model alignment.
• Reinforcement Learning Training Pipelines - Provides an orchestration pipeline for executing supervised fine-tuning and RLHF to improve model safety and accuracy.
• Supervised Fine-Tuning - Provides supervised fine-tuning capabilities as a prerequisite or complement to reinforcement learning.
• Model Alignment and Feedback - Facilitates large-scale model alignment using reward signals and reinforcement learning while minimizing communication costs.
• Agentic Interaction Training - Enables training conversation models to handle multiple turns and tool calls using reinforcement learning for complex agentic behaviors.
• RL Dataflow Construction - Implements decoupled computation dataflows to construct complex post-training reinforcement learning workflows.
• Large Language Model Optimization - Optimizes the speed and efficiency of large language models through reinforcement learning post-training processes.
• Model Integration Interfaces - Provides modular interfaces to integrate with various training and inference engines for scalable model execution.
• Multi-Modal Reinforcement Learning - Supports reinforcement learning for models that process both image and text data.
• Multi-Modal Training - Applies reinforcement learning to models processing images and text to improve performance across diverse data types.
• Low-Rank Adaptation - Integrates low-rank adaptation to reduce memory footprints by updating only a small subset of model weights.
• Modular Provider Interfaces - Provides standardized wrappers to connect diverse training and inference engines while maintaining backend independence.
• Logic And Infrastructure Decoupling - Implements architectural separation between reinforcement learning algorithm logic and the underlying hardware execution and data movement.
• Agentic Reinforcement Learning - Reinforcement learning framework for large models.
• Fine-Tuning Frameworks - Reinforcement learning framework for large models.
• Large Language Models - Reinforcement learning framework for LLMs.
• Model Training - Scalable reinforcement learning framework for training reward models.
• Model Training Frameworks - Flexible and efficient reinforcement learning framework for LLMs.
• Reinforcement Learning - Industrial-level RLHF training framework for LLMs.
• Reinforcement Learning Frameworks - Flexible and efficient framework for reinforcement learning from human feedback.

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